論文 - 大槻　知明

Mobile Edge Computing Based Intelligent Charging Strategy for Electric Vehicles in Cyber Physical Energy System

Pan G., Guan X., Wang N., Liu Y., Wu H., Chen H., Ohtsuki T., Han Z.

IEEE Transactions on Vehicular Technology 75 （ 2 ） 3205 - 3221 2025年

責任著者,  ISSN  00189545

　概要を見る

The development of intelligent transportation systems has promoted the application of wireless technology in vehicular communication networks and mobile services. At the same time, electric vehicles are popular due to their environmental-friendliness and cost-saving characteristics. However, charging of electric vehicles without proper and real-time rules may cause serious traffic congestion and high expense, which results in unnecessary charging stations and wasted power generation with high carbon emission. Therefore, it is crucial to design an efficient charging strategy for electric vehicles without causing road congestion, together with an economic dispatch strategy to offer proper power generation and decarbonization. In this paper, we introduce mobile edge computing architecture to enhance real-time response capabilities through edge device positioning data. This approach allows charging strategies to swiftly adapt to varying road conditions and user demands, effectively mitigating traffic congestion. Regarding the forecasting of load demand for charging stations, this paper employs a deep learning model based on informer and deploys it on edge servers, quickly providing accurate demand predictions for economic dispatching and enhancing the precision of scheduling strategies. With the application of deep reinforcement learning models, the system can formulate efficient charging plans based on real-time user data, improving user satisfaction and quality of services. Numerical results show the efficiency of the obtained strategies for electric vehicles charging and power economic dispatching, and the two-stage modeling approach significantly improves the convergence of the model and the quality of the solution.

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A Rapid SAR Image Simulation Method for Ship Wakes Coupled with Sea Waves Using Fluid Velocity Potential

Zhao C., Li K., Wang L., Ohtsuki T., Adachi F.

IEEE Signal Processing Letters 32   271 - 275 2025年

責任著者,  ISSN  10709908

　概要を見る

In simulating synthetic aperture radar (SAR) ship wakes, dynamic wake modeling often uses the linear superposition of sea waves and Kelvin wakes. This method, however, overlooks the alterations in sea surface roughness caused by the nonlinear interaction between waves and wakes, thus failing to accurately capture real sea surface variations. In this letter, we introduce a rapid SAR image simulation technique for ship wakes that incorporates sea waves using fluid velocity potential. Firstly, the computational domain and ship grid are constructed, with the grid scale tailored to the ship's surface structure to satisfy boundary conditions for efficient fluid velocity potential calculations. Next, to enhance boundary calculation accuracy, we employ the Taylor expansion boundary element method to swiftly resolve both steady and unsteady velocity potential components. Additionally, our approach not only depicts the interaction between sea waves and ship wakes but also facilitates the simulation analysis of various sea condition parameters. By treating the ship wake as noise and comparing images containing only background sea waves with the simulation images, the results show that the accuracy of the proposed approach is 0.2 SSIM higher than that of the linear superposition method, and the speed is 3 hours faster than that of CFD method.

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Deep Learning-Based Channel Estimation to Mitigate Channel Aging in Massive MIMO With Pilot Contamination

Hirose H., Yang S., Ohtsuki T., Bouazizi M.

IEEE Access 13   1834 - 1845 2025年

研究論文（学術雑誌）, 責任著者

　概要を見る

In time division duplex (TDD)-based massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) between the base station (BS) and user terminal (UT) is crucial for efficient signal processing, including received signal separation and transmission precoding. However, due to the time-varying nature of wireless channels and the limited coherence time, the pilot signals must be short, and the number of orthogonal pilot sequences is finite. Consequently, pilot signal reuse across neighboring cells leads to pilot contamination, significantly degrading channel estimation accuracy. Traditional methods like minimum mean square error (MMSE) estimation require prior knowledge of the channel covariance matrix, which is often unavailable. This paper proposes a novel deep learning-based channel estimation approach that effectively mitigates the dual challenges of channel aging and pilot contamination. The method leverages two distinct convolutional neural networks (CNNs): one for processing pilot signals to suppress inter-cell interference and another for data signals to address both inter-cell and intra-cell interference. To further enhance estimation accuracy, a smoothing filter is employed to minimize local distortions caused by incorrect symbol detection in the time domain. Simulation results demonstrate the effectiveness of the proposed method, particularly at normalized Doppler frequencies above 0.01, where it significantly outperforms conventional techniques and interpolation-based approaches. The results highlight the proposed method's robustness in maintaining high channel estimation accuracy in dynamic and interference-prone environments.

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Indoor Human Activity Recognition Using Multiple Dynamic Nonlinear Mapping Applied to 3-D LiDAR-Collected Data

Meng X., Bouazizi M., Li Z., Ohtsuki T.

IEEE Internet of Things Journal 12 （ 11 ） 16797 - 16812 2025年

研究論文（学術雑誌）, 最終著者

　概要を見る

Activity recognition is essential in computer vision applications, such as smart homes and healthcare services. While RGB images have been widely used in this area, they pose challenges related to privacy invasion and environmental constraints. To address these issues, some research has explored using 3-D light detection and ranging (3-D LiDAR) to collect 3-D point cloud data for activity recognition. However, the high-computational cost and large model parameters required for processing 3-D point clouds remain major limitations. To overcome these challenges, we propose a novel multiclass activity recognition system based on skeleton extraction from depth images collected by 3-D LiDAR. First, we use 3-D LiDAR to collect depth images of ten distinct activities, such as walking, falling, and squatting. Next, we process these depth images using our proposed multiple dynamic nonlinear mapping (MDNLM) method. The MDNLM method enhances the clarity of human body details by adjusting the color distribution of depth values based on the human position, ensuring that more colors are allocated to specific regions of the human body. This enhancement allows a fine-tuned algorithm to extract skeleton joints accurately from the mapped images. Finally, the extracted skeletons are fed into a convolutional neural network combined with a long short-term memory network (CNN+LSTM) for multiclass activity recognition. Our proposed method achieved 100.0% accuracy for a 2-class classification task (fall detection), 99.0% accuracy for a 7-class classification task, and 94.7% accuracy for a 10-class classification task.

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Avoiding Shortcuts: Enhancing Channel-Robust Specific Emitter Identification via Single-Source Domain Generalization

Wang Y., Ohtsuki T., Sun Z., Niyato D., Wang X., Gui G.

IEEE Transactions on Wireless Communications 24 （ 4 ） 3163 - 3176 2025年

研究論文（学術雑誌）, 責任著者,  ISSN  15361276

　概要を見る

By extracting radio frequency (RF) fingerprints from received signals, specific emitter identification (SEI) becomes a promising technique for physical layer identification of wireless devices. Recently, channel-robust SEI has attracted increasing attention due to the weak robustness exhibited by deep learning (DL)-based SEI methods in cross-channel conditions. To address these limitations, we propose a novel channel-robust SEI framework based on single-source domain generalization (SDG). Initially, we analyze the weak robustness of existing SEI methods from the perspective of the "shortcut learning"phenomenon in DL. Shortcut learning may lead traditional SEI methods to prioritize easily-mined, yet transient, channel characteristics in signal samples, rather than focusing on the more stable RF fingerprints derived from hardware differences. Next, from the perspective of SDG, we outline the optimization goal to rectify the shortcut learning in SEI. Inspired by this optimization goal, we then propose a channel-robust SEI method. This method consists of feature embedding through a multi-scale convolutional attention network (MSCAN), domain expansion using random overlay augmentation (ROA) to generate multiple virtual domains, and dual alignment strategy based on contrastive learning. Specifically, supervised contrastive learning is implemented for category-wise alignment, while supervised contrastive adversarial learning is utilized for domain-wise alignment. This dual alignment strategy can optimize the MSCAN to learn discriminative and domain-invariant feature representations, thereby enhancing the robustness of SEI. Simulation experiments on the ORACLE dataset and the WiSig dataset have demonstrated the superiority of our method compared to state-of-the-art techniques.

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Towards Collaborative and Cross-Environment UAV Classification: Federated Semantic Regularization

X. Fu, Y. Wang, Y. Lin, T. Ohtsuki, B. Adebisi, G. Gui, and H. Sari

IEEE Transactions on Information Forensics And Security 2025年

研究論文（学術雑誌）, 責任著者

A Non-Invasive Approach for Facial Action Unit Extraction and Its Application in Pain Detection

Bouazizi M., Feghoul K., Wang S., Yin Y., Ohtsuki T.

Bioengineering 20 （ 2 ）  2025年

研究論文（学術雑誌）, 最終著者

　概要を見る

A significant challenge that hinders advancements in medical research is the sensitive and confidential nature of patient data in available datasets. In particular, sharing patients’ facial images poses considerable privacy risks, especially with the rise of generative artificial intelligence (AI), which could misuse such data if accessed by unauthorized parties. However, facial expressions are a valuable source of information for doctors and researchers, which creates a need for methods to derive them without compromising patient privacy or safety by exposing identifiable facial images. To address this, we present a quick, computationally efficient method for detecting action units (AUs) and their intensities—key indicators of health and emotion—using only 3D facial landmarks. Our proposed framework extracts 3D face landmarks from video recordings and employs a lightweight neural network (NN) to identify AUs and estimate AU intensities based on these landmarks. Our proposed method reaches a 79.25% F1-score in AU detection for the main AUs, and 0.66 in AU intensity estimation Root Mean Square Error (RMSE). This performance shows that it is possible for researchers to share 3D landmarks, which are far less intrusive, instead of facial images while maintaining high accuracy in AU detection. Moreover, to showcase the usefulness of our AU detection model, using the detected AUs and estimated intensities, we trained state-of-the-art Deep Learning (DL) models to detect pain. Our method reaches 91.16% accuracy in pain detection, which is not far behind the 93.14% accuracy obtained when employing a convolutional neural network (CNN) with residual blocks trained on actual images and the 92.11% accuracy obtained when employing all the ground-truth AUs.

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Behavioral Modeling of Power Amplifiers Leveraging Multi-Channel Convolutional Long Short-Term Deep Neural Network

Li R., Yao Z., Wang Y., Lin Y., Ohtsuki T., Gui G., Sari H.

IEEE Transactions on Vehicular Technology 20 （ 7 ） 11456 - 11460 2025年

責任著者,  ISSN  00189545

　概要を見る

To improve the performance metrics of power amplifier (PA) nonlinear modeling while reducing the complexity of neural networks (NN), this paper proposes a multi-channel convolutional long short-term deep neural network (MCLDNN) approach. This method is designed to efficiently extract information from both temporal and spatial dimensions. The input to the model is a two-dimensional (2D) array, which includes envelope-dependent terms and the in-phase and quadrature (I/Q) components of both current and past signals. Additionally, this model splits the 2D array into multiple one-dimensional (1D) array. We utilize 1D and 2D convolution processes on these arrays to capture local and global patterns. Long short-term memory (LSTM) is then applied to process sequential data, enabling accurate and effective extraction of temporal correlations. The experimental evaluation of PA nonlinear behavioral modeling demonstrates that the proposed method outperforms other comparative methods in normalized mean square error (NMSE) and adjacent channel error power ratio (ACEPR).

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Toward Collaborative and Cross-Environment UAV Classification: Federated Semantic Regularization

Fu X., Wang Y., Lin Y., Ohtsuki T., Adebisi B., Gui G., Sari H.

IEEE Transactions on Information Forensics and Security 20   1624 - 1635 2025年

研究論文（学術雑誌）, 責任著者,  ISSN  15566013

　概要を見る

The rapid and widespread adoption of unmanned aerial vehicles (UAVs) poses significant threats to public safety and security in sensitive areas and subsequently underscores the urgent need for effective UAV surveillance solutions, where UAV classification emerges as a vital technology. Deep learning (DL) methods can autonomously extract implicit features from UAV signals and subsequently infer their types, provided that sufficient signal samples are available. Due to the high mobility of UAVs, it is challenging to ensure continuous monitoring between UAVs and the surveillance system to obtain sufficient samples. Moreover, DL models developed from sufficient but environment-specific datasets tend to be less generalized. This paper proposes a novel federated semantic regularization for learning an UAV classification model and further classifying UAVs across diverse environmental conditions. The approach enhances model generalization by regularizing semantic features during the local model training process on each participant. Subsequently, these local models are aggregated into a robust global model. Extensive testing across multiple environments demonstrates the superior classification performance of our approach compared to existing non-federated and federated approaches. The average classification accuracy of the proposed method in the three environments is 95.68%, which is improved by 13.39% compared to the non-federated methods and by 2.75% compared to the federated methods.

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Loss-Adapter: Addressing Network Packet Loss in Distributed Inference for Lossy IoT Environments

Hou Z., Ohtsuki T.

IEEE Internet of Things Journal 12 （ 12 ） 22048 - 22057 2025年

研究論文（学術雑誌）, 最終著者

　概要を見る

The deployment of deep neural networks (DNNs) in the Internet of Things (IoT) is essential for various AI applications. This trend highlights the growing need for distributed inference (DI) capabilities to process data efficiently and effectively in IoT environments. In lossy networks, data loss can significantly affect inference accuracy. Prioritizing algorithmic optimizations over hardware modifications proves to be a more effective approach for enhancing DNN performance in the IoT. We propose a plug-and-play module called Loss-Adapter, which aims to improve the accuracy of DI on lossy networks. To simulate network packet loss, we design a Gaussian distribution sampling dropout. This method is utilized during the training of the Loss-Adapter to acquire fault-tolerant inference capabilities for operation over unreliable communication links. Moreover, we employ the tree-structured Parzen estimator algorithm to optimize the parameters of the Gaussian dropout layer. We design two IoT packet loss simulation systems to conduct experiments, exploring the impact of the DNN split position and packet size on inference accuracy. Our experiments demonstrate that applying the Loss-Adapter significantly enhances the inference accuracy of DNN models operating on lossy IoT networks.

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Robust Cross-Scenario WiFi Wireless Sensing Using Incremental Learning and Elastic Weight Consolidation Loss

He Z., Bouazizi M., Yin Y., Gui G., Ohtsuki T.

IEEE Internet of Things Journal 12 （ 13 ） 24288 - 24299 2025年

研究論文（学術雑誌）, 最終著者

　概要を見る

WiFi-based wireless sensing has emerged as a promising passive sensing technology that is precious for human activity recognition (HAR) across diverse applications. However, achieving robustness across varying scenarios presents a significant challenge, limiting its broader adoption. To address this issue, we propose a robust cross-scenario incremental learning (IL) method for WiFi-based wireless sensing that leverages WiFi channel state information (CSI) and elastic weight consolidation (EWC) loss. Our approach integrates a convolutional neural network and attention-based long short-term memory (CNN-ABLSTM) framework, which effectively captures the spatial and temporal features of CSI data. The IL strategy enhances model adaptability across dynamic environments, while EWC minimizes catastrophic forgetting by preserving critical weights from prior tasks. The method’s integration of a memory set and EWCLoss enables it to balance the retention of learned features with adaptation to new scenarios, effectively mitigating performance degradation across tasks. Experimental results on the MM-Fi dataset demonstrate robust cross-scenario performance: starting with initial training on scene E01, the model achieves incremental recognition in new scenes E02, E03, and E04 with cross-scenario accuracies of 88.01%, 80.16%, and 70.93%, respectively. The proposed approach substantially improves cross-scenario adaptability and test accuracy compared to traditional and cross-domain methods such as transfer learning. This work marks a significant advancement toward robust and scalable WiFi-based wireless sensing for diverse real-world applications.

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A Cross-Subject Transfer Learning Method for CSI-Based Wireless Sensing

He Z., Bouazizi M., Gui G., Ohtsuki T.

IEEE Internet of Things Journal 12 （ 13 ） 23946 - 23960 2025年

研究論文（学術雑誌）, 最終著者

　概要を見る

WiFi-based passive noncontact sensing is widely regarded as a leading technology in wireless sensing, owing to its extensive application scope and favorable growth outlook. Nevertheless, although current WiFi-based sensing techniques attain remarkable accuracy in identifying activities within particular scenarios, they need stronger generalization capabilities across different targets and environments, hindering further commercial development. To address this issue, this article uses convolutional neural network (CNN), BLSTM, and attention layers to propose a cross-subject transfer learning method based on the CNN-ABLSTM algorithm model. This method combines widely used transfer learning methods with deep neural network algorithms in cross-domain sensing. Specifically, this method leverages the performance advantages of the CNN-ABLSTM algorithm model in processing time-series data like channel state information (CSI) and utilizes transfer learning to fine-tune the pretrained model from the source domain for application in the target domain with different subjects. This enables faster and more accurate achievement of cross-subject tasks. The simulated results show that the proposed new approach achieves higher recognition accuracy and shorter training times than traditional transfer learning methods for cross-subject tasks. In testing with the dataset used, it achieves up to around 85% performance of activity recognition accuracy in cross-subject tasks.

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Joint Angle-Based User Selection and Multiagent Reinforcement Learning for Dynamic Beamforming in HAPS-Assisted IoT Vehicular Networks

Yang S., Ohtsuki T., Jiang X.

IEEE Internet of Things Journal 12 （ 14 ） 27075 - 27083 2025年

研究論文（学術雑誌）, 責任著者

　概要を見る

High Altitude Platform Stations (HAPS) have emerged as a compelling solution to extend wireless coverage for next-generation vehicular networks, thanks to their ability to hover at altitudes of around 20 km and provide robust Line-Of-Sight (LoS) links. However, the expansive coverage area of a single HAPS introduces intense co-channel interference among vehicles, particularly when traffic is dense or nonuniformly distributed. To address these challenges, this article proposes a joint angle-based user selection (AUS) strategy and an attention-based mean-field actor-critic (MF-A2C) framework for fully digital beamforming. First, AUS partitions angle-near vehicles into different groups to reduce within-group correlation. Then, each group is served by a high-dimensional beamforming vector, where an attention-based mean-field mechanism captures intergroup interference without incurring exponential complexity. Extensive simulations under realistic highway and urban mobility models demonstrate that the proposed scheme not only accelerates the convergence of the reinforcement learning process but also improves system throughput.

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Generative Augmented Cascade Broad Learning for Lightweight Multi-Band Spectrum Prediction

Ji N., Liu T., Zhang Y., Wang Q., Ohtsuki T., Gui G., Yuen C., Adachi F.

IEEE Transactions on Cognitive Communications and Networking 12   382 - 394 2025年

研究論文（学術雑誌）, 責任著者

　概要を見る

The rapid development of wireless communication and Internet of Things (IoT) devices has exacerbated the challenge of spectrum scarcity. Spectrum prediction plays a critical role in enhancing resource utilization and facilitating dynamic spectrum access (DSA). However, existing methods often face difficulties with missing data arising from sensor failures or environmental changes, while prioritizing prediction accuracy over computational efficiency, hindering deployment. To overcome these challenges, this paper introduces the generative augmented cascade broad learning network (GA-CBLN) for spectrum prediction. This approach combines a deep learning (DL)-based pre-trained model for data imputation with an online broad learning (BL) process to ensure efficient prediction. Initially, a DL-based regressor imputes missing values by learning spatiotemporal patterns, generating a complete dataset. Subsequently, a generative adversarial network (GAN) produces synthetic samples to augment the dataset, enhancing the model’s adaptability and generalization. Finally, the cascade broad learning network (CBLN) conducts feature extraction and prediction using both original and augmented data, exploiting the efficient learning capabilities of BL. Simulations on real spectrum datasets from four widely used frequency bands show that GA-CBLN surpasses traditional methods in computational efficiency, maintaining robust performance even under high rates of missing data.

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SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network

Jiahui J.E., Wang L., Zhao C., Mathiopoulos P.T., Ohtsuki T., Adachi F.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 18   12133 - 12149 2025年

研究論文（学術雑誌）, 責任著者,  ISSN  19391404

　概要を見る

As synthetic aperture radar (SAR) image change detection can continuously acquire target information under all weather conditions, it has been used in the past for various applications. However, it is very challenging to use SAR images for small area change detection due to the presence of noise and the imbalance between classes. To deal with these problems this article proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network. First, employing Multiscale Superpixel Reconstructed Difference Image effectively enhances the edge information of the images. Second, a two-stage Weighted Center Constrained Fuzzy C-Means clustering algorithm is proposed. To constrain incorrect migration in the subsequent clustering phase, the algorithm uses a two-stage clustering algorithm. During the first stage weights in the clustering process to improve the accuracy of the resulting clustering centers are introduced while during the second stage, these refined cluster centers can be constrained. Third, a Convolutional Squeeze-and-Excitation Attention Mechanism Wavelet Neural Network (CSWNN) is proposed which more accurately distinguishes uncertain pixels and thus reduces redundant features. The final Denoising Diffusion Probabilistic Model is introduced into the CSWNN to expand pseudolabeled samples. Comparing this algorithm with seven sets of contrast experiments on six real SAR datasets shows that the accuracy of detecting changes in small regions is 2% –9% better than the classical algorithm.

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A Clustering-Aided Optimization Algorithm for Antenna Beamforming in Multicell HAPS Systems

Dai W., Yang S., Yin Y., Bouazizi M., Ohtsuki T.

IEEE Internet of Things Journal 12 （ 15 ） 30168 - 30184 2025年

研究論文（学術雑誌）, 最終著者

　概要を見る

High altitude platform station (HAPS) systems have emerged as a key solution to address the increasing networking demands of the Internet of Things (IoT), providing wide-area coverage, low latency, enhanced network resilience, and cost-effective service delivery, particularly in remote regions. Given that the continuous movement of HAPS and the inherent mobility of user equipments (UEs) often lead to low and unevenly distributed UE throughput, it is crucial for HAPS systems to dynamically control the antenna using beamforming techniques. However, the current reactive approaches to dynamic control fail to effectively minimize the number of low-throughput UEs and achieve low time complexity. To overcome these challenges, we propose a clustering-aided particle swarm optimization (PSO) algorithm to determine the antenna parameters, enabling HAPS to configure multiple cells and dynamically control beams based on UE distribution. This algorithm leverages UE clustering information to redefine the search space, reducing the complexity while enhancing the ability to find the global optimum. Specifically, we propose a novel regulated K-means algorithm that groups UEs into appropriately balanced clusters, precisely reducing the search space for global optimization. Simulations using real-world UE distributions demonstrate that our proposed method outperforms conventional approaches in reducing low-throughput UEs and providing balanced throughput distribution, while maintaining low computational complexity.

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Self-Supervised Learning Enhanced Channel Estimation in Massive MIMO System with Low-Resolution ADCs

Liu G., Cao Y., Ohtsuki T., Quek T.Q.S.

IEEE Wireless Communications Letters 14 （ 7 ） 2209 - 2213 2025年

研究論文（学術雑誌）, 責任著者,  ISSN  21622337

　概要を見る

Traditional deep learning-based end-to-end (E2E) channel estimation approaches typically require extensive paired data for offline training, which is even more demanding when the signal is quantized by low-resolution analog-digital converters (ADCs), making E2E channel estimation impractical. Motivated by the above, we propose a model-driven self-supervised framework aimed at estimating channel information in massive multiple-input multiple-output systems. By applying the additive quantization noise model (AQNM) and the Bussgang decomposition theorem, we reformulate the nonlinear quantization process of low-resolution ADCs into a representation that resembles additive noise. Afterward, we integrate the self-supervised framework into the minimum mean squared error (MMSE) and Bussgang linear MMSE (BLMMSE) methods to refine channel estimation in low-resolution ADC scenarios. Notably, our approach has the advantage of not requiring true data labels, making it more adaptable to new scenarios compared to traditional E2E methods. Simulation results reveal that our approach using limited data outperforms existing supervised-learning approaches and exhibits an inherent robustness and generalization capabilities.

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Face Mesh for Dementia Detection: Evaluating Data Augmentation in Deep Learning and Traditional Machine Learning

Zheng C., Bouazizi M., Okunishi T., Ohtsuki T., Kitazawa M., Horigome T., Kishimoto T.

IEEE Access 13   104501 - 104520 2025年

研究論文（学術雑誌）, 責任著者

　概要を見る

Alzheimer’s disease (AD) stands as a prevalent form of dementia, primarily impacting the elderly, and presently lacking any known cure. Early screening and intervention have demonstrated efficacy for both current and prospective treatments. With the global population aging, early detection of AD has become imperative. Language-based approaches for dementia detection have received considerable attention in research due to dementia’s nature of hurting cognitive and language functions. Facial features, on the other hand, have been comparatively neglected despite their potential in identifying AD. The face mesh, comprising 478 landmarks, serves as a rich and informative facial representation. This study concentrates on leveraging face mesh data extracted from the PROMPT dataset for AD detection. The dataset comprises 445 voice and video recordings from 117 dementia patients and healthy individuals collected during professionally conducted interview sessions. We explored data augmentation techniques to address the challenges of over-fitting, particularly impactful in training neural networks on a small dataset. Deep learning methods, including transformer, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were employed to classify dementia patients and healthy subjects. By augmenting the dataset, we improved the performance of the deep learning methods. The combined use of 1D CNN networks and data augmentation method reported the highest accuracy of 76%, which is the state-of-the-art accuracy in dementia detection using face mesh. These results confirmed the effectiveness of our data augmentation methods. They also underscore the potential of face-related features in enhancing early detection approaches for AD.

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Malware Traffic Classification via Expandable Class Incremental Learning with Architecture Search, IEEE Transactions on Information Forensics and Security

X. Zhang, Y. Wang, T. Ohtsuki, G. Gui, C. Yuen, M. D. Renzo, and H. Sari

IEEE Transactions on Information Forensics and Security 2025年

研究論文（学術雑誌）, 責任著者

mmPoint-Attention: A Unified Attention Framework for Human Pose and Activity Recognition from mmWave Radar Point Clouds," IEEE Sensors Journal

X. Shi, M. Bouazizi, T. Ohtsuki

IEEE Sensors Journal 2025年

研究論文（学術雑誌）, 最終著者

An Ensemble Learning-Based Fault Detection Method for Vehicular Ad Hoc Networks in Intelligent Transportation Systems

Liu J., Zhao H., Han P., Gui G., Ohtsuki T., Sari H., Adachi F.

IEEE Transactions on Vehicular Technology 74 （ 3 ） 5114 - 5124 2025年03月

研究論文（学術雑誌）,  ISSN  00189545

　概要を見る

Vehicular ad hoc networks (VANETs) are integral to Intelligent Transportation Systems (ITS), serving as a crucial platform for deploying applications. The overall performance of VANETs is highly dependent on the vehicles, emphasizing the importance of effective fault (vehicle crash or exit) detection methods. Traditional fault detection faces challenges due to the high mobility of vehicles and prevalent software, hardware, and communication link faults, necessitating a robust solution for timely and accurate fault detection. This paper introduces ET-FD, a novel fault detection method based on ensemble learning designed to address the unique challenges in VANETs. ET-FD satisfies stringent accuracy requirements and aims to reduce system overhead significantly. We present experimental results demonstrating the superior performance of ET-FD in terms of fault detection accuracy and false positive rates, alongside comparisons with other classification methods that highlight its reduced training time. This confirms the potential of ET-FD as an efficient and reliable solution for fault detection in VANET environments.

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Wi-Fi Sensing Techniques for Human Activity Recognition: Brief Survey, Potential Challenges, and Research Directions

Miao F., Huang Y., Lu Z., Ohtsuki T., Gui G., Sari H.

ACM Computing Surveys 57 （ 107 ） 1 - 30 2025年01月

研究論文（学術雑誌）,  ISSN  03600300

　概要を見る

Recent advancements in wireless communication technologies have made Wi-Fi signals indispensable in both personal and professional settings. The utilization of these signals for Human Activity Recognition (HAR) has emerged as a cutting-edge technology. By leveraging the fluctuations in Wi-Fi signals for HAR, this approach offers enhanced privacy compared to traditional visual surveillance methods. The essence of this technique lies in detecting subtle changes when Wi-Fi signals interact with the human body, which are then captured and interpreted by advanced algorithms. This article initially provides an overview of the key methodologies in HAR and the evolution of non-contact sensing, introducing sensor-based recognition, computer vision, and Wi-Fi signal based approaches, respectively. It then explores tools for Wi-Fi-based HAR signal collection and lists several high-quality datasets. Subsequently, the article reviews various sensing tasks enabled by Wi-Fi signal recognition, highlighting the application of deep learning networks in Wi-Fi signal detection. Experimental results are then presented that assess the capabilities of different networks. The findings indicate significant variability in the generalization capacities of neural networks and notable differences in test accuracy for various motion analyses.

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A Comparison Study of Person Identification Using IR Array Sensors and LiDAR

Liu K., Bouazizi M., Xing Z., Ohtsuki T.

Sensors 25 （ 1 ） 271 2025年01月

研究論文（学術雑誌）, 最終著者

　概要を見る

Person identification is a critical task in applications such as security and surveillance, requiring reliable systems that perform robustly under diverse conditions. This study evaluates the Vision Transformer (ViT) and ResNet34 models across three modalities—RGB, thermal, and depth—using datasets collected with infrared array sensors and LiDAR sensors in controlled scenarios and varying resolutions (16 × 12 to 640 × 480) to explore their effectiveness in person identification. Preprocessing techniques, including YOLO-based cropping, were employed to improve subject isolation. Results show a similar identification performance between the three modalities, in particular in high resolution (i.e., 640 × 480), with RGB image classification reaching 100.0%, depth images reaching 99.54% and thermal images reaching 97.93%. However, upon deeper investigation, thermal images show more robustness and generalizability by maintaining focus on subject-specific features even at low resolutions. In contrast, RGB data performs well at high resolutions but exhibits reliance on background features as resolution decreases. Depth data shows significant degradation at lower resolutions, suffering from scattered attention and artifacts. These findings highlight the importance of modality selection, with thermal imaging emerging as the most reliable. Future work will explore multi-modal integration, advanced preprocessing, and hybrid architectures to enhance model adaptability and address current limitations. This study highlights the potential of thermal imaging and the need for modality-specific strategies in designing robust person identification systems.

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Dementia and MCI Detection Based on Comprehensive Facial Expression Analysis From Videos During Conversation

Okunishi T., Zheng C., Bouazizi M., Ohtsuki T., Kitazawa M., Horigome T., Kishimoto T.

IEEE Journal of Biomedical and Health Informatics 29 （ 5 ） 3537 - 3548 2025年01月

研究論文（学術雑誌）, 責任著者,  ISSN  21682194

　概要を見る

The development of a cost-effective digital biomarker for detecting dementia is highly needed. While numerous studies have explored dementia detection through speech and natural language analysis, only a few studies have focused on dementia detection using face video recordings, and more in-depth research is needed. In this paper, we propose a method for detecting dementia and mild cognitive impairment (MCI), a pre-dementia stage, by utilizing four types of facial expression features extracted from recorded videos of participants. These features include Action Units, emotion categories, Valence-Arousal, and face embeddings. From the above features obtained from each video frame, various statistical information was extracted and used as features, and predictions were performed using a decision tree-based model. Our method was evaluated using face video recordings during conversations. The method achieved an area under the receiver operating characteristic curve (AUC) of 0.933 for dementia detection and 0.889 for MCI detection. Statistical analysis of facial expression features revealed that participants with dementia had fewer positive emotions, more negative emotions, and lower valence and arousal than healthy participants. These results indicate that the proposed method could serve as an explainable screening tool for the early detection of dementia and MCI.

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Accurate Cardiac Duration Detection for Remote Blood Pressure Estimation Using mm-Wave Doppler Radar

S. Wang, M. Bouazizi, Siyuan Yang, and T. Ohtsuki

Sensors 25 （ 3 ） 619 2025年01月

研究論文（学術雑誌）, 最終著者

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

Pan G., Guan X., Jiang H., Liu Y., Wu H., Chen H., Ohtsuki T., Han Z.

Computer Networks 255 2024年10月

研究論文（学術雑誌）, 責任著者,  ISSN  13891286

　概要を見る

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user's charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G's high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user's range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

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Deep Reinforcement Learning Based Data-Driven Mapping Mechanism of Digital Twin for Internet of Energy

Xu S., Guan X., Peng Y., Liu Y., Cui C., Chen H., Ohtsuki T., Han Z.

IEEE Transactions on Network Science and Engineering 11 （ 4 ） 3876 - 3890 2024年07月

研究論文（学術雑誌）, 責任著者

　概要を見る

Digital twin technology can be used in the internet of energy (IoE) systems to perform physical objects optimization and decision-making by updating relevant models in real-time. Model update is performed using real-time data generated by multiple devices contained in an IoE system. Since volume of the real-time data generated by IoE system is big, it is difficult for digital twin models to map the real-time data into virtual space. The difficulty of data mapping is because of high data volume and high resource consumption. In this paper, an adaptive data mapping mechanism using deep reinforcement learning (DRL) technique is devised. The proposed mechanism can dynamically adjust mapping data selection according to the environmental state and user requirements. By dynamically adjusting mapping data selection, the data and resource consumption used for mapping can be effectively reduced. The experimental results validate that the proposed mechanism can perform dynamic mapping data selection with reduced mapping data and low resource consumption.

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SAR Image Wake Detection Based on Pseudo-Siamese Structure and Multidomain Feature Fusion

Zhao C., Liu H., Wang L., Ohtsuki T., Adachi F.

IEEE Geoscience and Remote Sensing Letters 21 2024年

研究論文（学術雑誌）, 責任著者,  ISSN  1545598X

　概要を見る

The wake target has garnered increasing attention due to its length, which can be up to ten times that of the ship, and its inclusion of critical navigation information such as heading and speed. However, deep learning methods used in synthetic aperture radar (SAR) image wake detection tasks are limited to analyzing the features of the image itself, overlooking the characteristics of ship wakes in the frequency domain. This letter proposes a network called pseudo-siamese and multidomain feature fusion network (PSMDNet) that is composed of two parallel feature extraction branches. The feature extraction in the frequency domain uses the frequency channel attention network (FcaNet) as the backbone, incorporating an adjacent scale space attention module (ASSAM) to fuse high-level features into low-level features. The time domain uses the residual network (ResNet) as the backbone, incorporating a bidirectional feature channel module (BFCM) to enhance the representation of low-level spatial information. These two parallel branches extract the time- and frequency-domain features from the image to better capture the wake feature information. The proposed ASSAM module calculates weighted coding with context information, thereby selectively aggregating the unique linear spatial features of the wake into the low-level feature map. Verification experiments were conducted on the SAR-WAKE dataset, and the results demonstrate that the proposed method excels in detection accuracy compared with other algorithms, achieving excellent results of 92.71%. Particularly noteworthy is that the positioning and visualization of wake vertex and Kelvin arms are realized by the loss function designed for the wake.

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Advancing Malware Detection in Network Traffic with Self-Paced Class Incremental Learning

Xu X., Zhang X., Zhang Q., Wang Y., Adebisi B., Ohtsuki T., Sari H., Gui G.

IEEE Internet of Things Journal 11 （ 12 ） 21816 - 21826 2024年

研究論文（学術雑誌）, 責任著者

　概要を見る

Ensuring network security, effective malware detection (MD) is of paramount importance. Traditional methods often struggle to accurately learn and process the characteristics of network traffic data, and must balance rapid processing with retaining memory for previously encountered malware categories as new ones emerge. To tackle these challenges, we propose a cutting-edge approach using self-paced class incremental learning (SPCIL). This method harnesses network traffic data for enhanced class incremental learning (CIL). A pivotal technique in deep learning, CIL facilitates the integration of new malware classes while preserving recognition of prior categories. The unique loss function in our SPCIL-driven MD combines sparse pairwise loss with sparse loss, striking an optimal balance between model simplicity and accuracy. Experimental results reveal that SPCIL proficiently identifies both existing and emerging malware classes, adeptly addressing catastrophic forgetting. In comparison to other incremental learning approaches, SPCIL stands out in performance and efficiency. It operates with a minimal model parameter count (8.35 million) and in increments of 2, 4, and 5, achieves impressive accuracy rates of 89.61%, 94.74%, and 97.21% respectively, underscoring its effectiveness and operational efficiency.

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Enhanced Few-Shot Malware Traffic Classification via Integrating Knowledge Transfer With Neural Architecture Search

Zhang X., Wang Q., Qin M., Wang Y., Ohtsuki T., Adebisi B., Sari H., Gui G.

IEEE Transactions on Information Forensics and Security 19   5245 - 5256 2024年

研究論文（学術雑誌）, 責任著者,  ISSN  15566013

　概要を見る

Malware traffic classification (MTC) is one of the important research topics in the field of cyber security. Existing MTC methods based on deep learning have been developed based on the assumption of enough high-quality samples and powerful computing resources. However, both are hard to obtain in real applications especially in availability of IoT. In this paper, we propose a few-shot MTC (FS-MTC) method combining knowledge transfer and neural architecture search (i.e. NAS-based FS-MTC) with limited training samples as well as acceptable computational resources, in order to mitigate the identified challenges. Specifically, our proposed method first converts the raw network traffic into traffic images through data pre-processing to serve as input data for the neural network. Second, we use neural architecture search to adaptively search for the effective feature extraction model on the source domain (including Edge-IIoTset, Bot-IoT, and benign USTC-TFC2016). Third, the searched model is pre-trained on source task to achieve the generic feature representation of malware traffic. Finally, we only use few-shot malware traffic samples to fine-tune the pre-trained model to quickly adapt to new types of MTC tasks in realistic network environments. The experimental results show that the proposed NAS-based FS-MTC method has great scalability and classification performance in different FS-MTC tasks, including 5-way K-shot USTC-TFC2016 dataset and 10-way K-shot CIC-IoT dataset. Compared with state-of-the-art methods in the field of malware classification, the proposed NAS-based FS-MTC has higher classification accuracy. Especially in the 1-shot case of the USTC-TFC2016 dataset, its average accuracy is as high as 86.91%.

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Toward Robust Open-Set Radiofrequency Signal Identification in Internet of Things Using Hypersphere Manifold Embedding

Fu X., Wang Y., Lin Y., Ohtsuki T., Gui G., Sari H.

IEEE Internet of Things Journal 11 （ 24 ） 41235 - 41247 2024年

研究論文（学術雑誌）, 責任著者

　概要を見る

Radiofrequency signal identification (RSI) provides a critical security solution for device authentication in the Internet of Things (IoT), characterized by extensive interconnections and interactions among numerous entities. By analyzing received radiofrequency signals, device-specific features are extracted at the receiver and used for identification. In a dynamic and ever-changing communication environment, where some devices not visible during the training process may appear during testing, a robust RSI method must not only identify devices encountered during training but also reject those that were not. In this article, we propose an open-set RSI method based on hypersphere manifold embedding. This approach leverages hypersphere projection for radiofrequency signal feature extraction on a hypersphere manifold, thereby avoiding the need to optimize intradevice variation in the radial direction. Additionally, we introduce an open-set identification approach based on generalized Pareto distribution, which does not rely on any radiofrequency signals from unknown devices. Extensive experimental results demonstrate that the proposed method achieves state-of-the-art identification performance.

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ime-Efficient Neural-Network-Based Dynamic Area Optimization Algorithm for High-Altitude Platform Station Mobile Communications

W. Takabatake, Y. Shibata, K. Hoshino, and T. Ohtsuki

Future Internet 2024 2024年

研究論文（学術雑誌）, 最終著者

Improved Deep Learning Methods for Beamforming in Secure SWIPT Networks

Ganapathy V., Ramachandran R., Ohtsuki T.

IEEE Transactions on Green Communications and Networking 9 （ 3 ） 962 - 976 2024年

研究論文（学術雑誌）, 最終著者

　概要を見る

Simultaneous Wireless Information and Power Transfer (SWIPT) is a unified approach to transfer power and information by exploiting the properties of radio signals. This paper considers a multi-antenna base station, multiple energy harvesting receivers (EHRs), and legitimate receivers in the presence of multiple multi-antenna eavesdroppers. A resource allocation problem of maximizing the harvested energy available at the EHR side in the presence of secrecy capacity and quality of service constraints is formulated for analysis. The transmit power, secrecy, and total transmit power requirements are used to formulate the constraints. We then propose neural network methods that use the channel state information data and constraint characteristics to obtain novel activation functions that can be used to predict beamforming vectors. Simulation results show that the proposed activation functions predict beamforming vectors to achieve better harvested energy and secrecy capacity.

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Depth-video based Secondary Action Recognition in Vehicles via CNN and Bi-LSTM with Spatial Enhance Attention Mechanism

W. Shao, M. Bouazizi, and T. Ohtsuki

Sensors 2024 2024年

研究論文（学術雑誌）, 最終著者

Heartrate Estimation Considering Reconstructed Signal Features based on VMD Via MIMO FMCW Radar

S. Nakatani, M. Bouazizi, and T. Ohtsuki

Sensors 2024 2024年

研究論文（学術雑誌）, 最終著者

Non-Contact Cross-Person Activity Recognition by Deep Metric Ensemble Learning

Ye C., Xu S., He Z., Yin Y., Ohtsuki T., Gui G.

Bioengineering 11 （ 11 ） 1124 2024年

研究論文（学術雑誌）, 責任著者

　概要を見る

In elderly monitoring or indoor intrusion detection, the recognition of human activity is a key task. Owing to several strengths of Wi-Fi-based devices, including their non-contact and privacy protection, these devices have been widely applied in the area of smart homes. By the deep learning technique, numerous Wi-Fi-based activity recognition methods can realize satisfied recognitions, however, these methods may fail to recognize the activities of an unknown person without the learning process. In this study, using channel state information (CSI) data, a novel cross-person activity recognition (CPAR) method is proposed by a deep learning approach with generalization capability. Combining one of the state-of-the-art deep neural networks (DNNs) used in activity recognition, i.e., attention-based bi-directional long short-term memory (ABLSTM), the snapshot ensemble is the first to be adopted to train several base-classifiers for enhancing the generalization and practicability of recognition. Second, to discriminate the extracted features, metric learning is further introduced by using the center loss, obtaining snapshot ensemble-used ABLSTM with center loss (SE-ABLSTM-C). In the experiments of CPAR, the proposed SE-ABLSTM-C method markedly improved the recognition accuracies to an application level, for seven categories of activities.

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HAPS Cell Design Method for Coverage Extension Considering Coexistence on Terrestrial Mobile Networks

Shibata Y., Takabatake W., Hoshino K., Nagate A., Ohtsuki T.

IEEE Access 12   55506 - 55520 2024年

研究論文（学術雑誌）, 最終著者

　概要を見る

High-altitude platform stations (HAPSs) that provide direct communication services to smartphones on the ground are garnering significant attention as innovative mobile communication platforms suitable for ultrawide coverage areas. The deployment of a multi-cell configuration is essential for enhancing communication capacity over large geographical regions. However, designing and optimizing multi-cell configurations proves challenging due to the numerous antenna parameters involved, such as beamwidth and beam direction. HAPSs also hold promise in serving as reliable networks during disasters, as they can quickly restore coverage when terrestrial base stations (BSs) are in failure, allowing users to maintain connectivity via HAPSs using the same frequency band as terrestrial networks. Despite this advantage, HAPSs can potentially cause interference in terrestrial networks during such scenarios, necessitating careful considerations to avoid disrupting existing terrestrial networks. However, the optimization of cell configurations in the coexistence of HAPSs and terrestrial networks remains largely unexplored. To address this gap, this paper proposes a design scheme that leverages a genetic algorithm (GA) to optimize antenna parameters for each cell, thereby maximizing both user coverage and area coverage while ensuring coexistence with terrestrial networks. We optimize antenna parameters to increase user coverage and area coverage as a multi-objective optimization problem, resulting in a Pareto front that illustrates the trade-off choices. Additionally, the proposed method protects terrestrial networks by setting the initial beam direction of each cell to avoid interference with terrestrial BSs. Consequently, the GA can initiate optimization with candidate combinations that adhere to the interference constraints of terrestrial networks, thus expediting the optimization process and yielding improved coverage. Simulation results demonstrate that the proposed scheme outperforms conventional schemes which solely control beam direction, leading to superior coverage. While the results show trade-off between user coverage and area coverage, it is also clarified that considering both types of coverage show the potential to achieve significant improvements in both aspects simultaneously. Moreover, the results highlight the efficacy of the initial beam direction setting in enhancing coverage rates and significantly reducing the number of combinations required for the GA to converge.

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Regularized Multi-Label Learning Empowered Joint Activity Recognition and Indoor Localization With CSI Fingerprints

Wang Y., Zhao H., Ohtsuki T., Sari H., Gui G.

IEEE Transactions on Wireless Communications 23 （ 11 ） 16865 - 16874 2024年

研究論文（学術雑誌）, 責任著者,  ISSN  15361276

　概要を見る

Contactless Wi-Fi sensing, using channel state information (CSI) fingerprints, plays a pivotal role in communication, smart healthcare, and industrial automation. Deep learning has revolutionized the efficiency of non-contact sensing technology. Owing to its robust feature extraction capabilities and the interconnectedness of diverse sensing tasks, methods that address multiple tasks at once, like joint activity recognition and indoor localization (JARIL), have gained prominence. The primary goal of JARIL is to improve performance while reducing computational demands. Nevertheless, there remains substantial potential for enhancing its effectiveness through additional refinement and optimization measures. To address this, we introduce a regularized multi-label learning (RML) framework specifically designed for JARIL. This framework combines a parameter-efficient backbone network based on multi-scale separable convolution with residual connections, and a regularization training strategy. The latter strategy boosts performance by linearly combining two distinct CSI samples with their labels, creating new training instances in the training process. Simulation results show that the proposed method boasts a recognition accuracy of 91.73% and a localization precision of 99.64%. This marks an improvement of 4.32% and 3.60% respectively, in comparison to the prior ResNet1D+-based JARIL method. The codes can be downloaded from https://github.com/BeechburgPieStar/JARIL.

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People Identification in Private Car Using 3D LiDAR with Generative Image Inpainting and YOLOv5

Shao W., Bouazizi M., Meng X., Ohtsuki T.

IEEE Access 12   38258 - 38274 2024年

研究論文（学術雑誌）, 最終著者

　概要を見る

People Identification is a critical aspect in developing modern vehicles, aimed at enhancing safety and comfort levels. Most traditional methods of people identification in vehicles use RGB images or videos. In this study, we introduce a novel methodology for identifying individuals in private car scenarios, utilizing 3D Light Detection and Ranging (LiDAR) technology, generative image inpainting based on Contextual Attention, and the YOLOv5 model. Initially, we gather data utilizing a 3D-LiDAR instrument and subsequently convert the acquired depth data into depth images. Following this, the depth images are annotated manually to indicate the positions and identifiers of various individuals occupying distinct seats. This annotated data serves as the training material for the YOLOv5 model, facilitating the recognition and categorization of subjects. However, given that individuals seated in the back often have parts of their bodies occluded by the front seats and the passengers in them, we employ generative image inpainting techniques to reveal the occluded portions. This step significantly enhances the precision in detecting and identifying individuals situated in the back seats. We implemented our strategy on a restricted group of four participants, conducting training and testing phases within identical environments. Prior to the inpainting process, the classification's F1 score stood at 66.5%. After inpainting, we observed a notable surge in the F1 score for the rear-seat passengers increased by 17.1%.

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Online Trajectory Optimization for Energy-Efficient Cellular-Connected UAVs With Map Reconstruction

Zhao H., Hao Q., Huang H., Gui G., Ohtsuki T., Sari H., Adachi F.

IEEE Transactions on Vehicular Technology （73, no. 3, pp. 3445-3456）  3 （ 73 ） 3445 - 3456 2024年03月

研究論文（学術雑誌）, 責任著者,  ISSN  00189545

　概要を見る

In this article, we leverage the outage probability knowledge map to characterize the connection between unmanned aerial vehicles (UAVs) and cellular networks. The outage probability knowledge map is a database that simulates the connection between UAV and the cellular network during real hovers, which helps to enhance the UAV’s awareness of the environment and reduce the connection interruption under complex real-time channel state information. We assume that the UAV roughly samples from the actual radio environment of the airspace in advance, and calculates the outage probability of the sampled points. After that, the UAV reconstructs the actual outage knowledge map, and flies in the airspace to learn the optimal UAV trajectory planning policy based on the reconstructed map. The optimization objective is to minimize the flight energy cost of the UAV performing tasks. In this article, we propose a deep image prior based radio map reconstruction (DIPRMR) method to reconstruct the map, and then propose a deep reinforcement learning based trajectory optimization algorithm. The UAV that performs the task adjusts the flight trajectory based on the outage probability knowledge obtained from the reconstructed complete map. Simulation results show that the proposed online trajectory optimization scheme based on outage probability knowledge map can obtain great returns in terms of maintaining connectivity, reducing task completion time and energy consumption.

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Deep Learning Methods for Secure IoT SWIPT Networks

Ganapathy V., Ramachandran R., Ohtsuki T.

IEEE Internet of Things Journal 11 （ 11 ） 19657 - 19677 2024年

研究論文（学術雑誌）, 最終著者

　概要を見る

In this article, a deep-Q network (DQN)-based approach is proposed to improve the harvested energy and the secrecy capacity by optimizing the beamforming vectors in multiple-input-multiple-output (MIMO) networks with simultaneous wireless information and power transmission (SWIPT). Here, in the presence of multiantenna eavesdroppers, a multiantenna hybrid access point (HAP) serves energy harvesting Internet of Things (EH-IoT) nodes and information decoding (ID) receiver. The EH-IoT nodes and the ID receiver are separated from each other and experience different channels from the HAP. Two cases of signal models are studied to maximize the harvested energy. The first case is the consolidated signal case, where the HAP transmits an information-bearing signal to both the EH-IoT nodes and the ID receiver. The second case is the split signal case, where the HAP sends both information-carrying signal and energy signals to the EH-IoT nodes and the ID receiver. For both these cases, the optimization strategies for maximizing the harvested energy under Quality-of-Service (QoS) constraints are formulated with the secrecy capacity and the harvested energy requirements. The harvested energy maximization problems are complex to handle due to the presence of high-dimensional variables. An efficient, robust, and secure DQN-based algorithm is proposed, where Q-learning and deep neural network (DNN) methods are extended to solve these problems. Simulation results show that the proposed DQN approach can achieve a balance between harvested energy and security. By incorporating action and reward spaces that are adaptable to the formulated problem, significant performance gains are achieved.

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Self-Supervised Learning Malware Traffic Classification Based on Masked Autoencoder

Xu K., Zhang X., Wang Y., Ohtsuki T., Adebisi B., Sari H., Gui G.

IEEE Internet of Things Journal 11 （ 10 ） 17330 - 17340 2024年

研究論文（学術雑誌）, 責任著者

　概要を見る

Malware traffic classification (MTC) is one of the important techniques to ensure the security of cyberspace, which aims to detect anomalies and classify different types of network traffic. Recently, MTC methods based on deep learning (DL) have shown their excellent performance. However, these DL-based methods rely on data sets with manually labeled samples for training, which are costly and hard to obtain. To address this problem, this article proposes a novel self-supervised MTC method based on the framework of masked auto-encoder (MAE). Specifically, MAE first constructs a reasonable unsupervised pretext task with a random masking strategy, which reduces the redundant information in samples and speeds up the pretraining process. The transformer-based backbone network then efficiently extracts features from the nonredundant traffic data efficiently. The proposed MTC-MAE method employs self-supervised learning on a large-scale unlabeled data set to acquire unbiased features, and fine-tunes on specific data sets to adapt to diverse traffic classification scenarios. Simulation experiments show that our proposed MTC-MAE method is able to learn universal features with high quality and has excellent classification performance on various downstream data sets. The data sets we used, code implementation, and pretrained models are available on GitHub. Code available at https://github.com/TsuiHark/Self-supervised_MTC.

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Activity Detection in Indoor Environments Using Multiple 2D Lidars

Bouazizi M., Mora A.L., Feghoul K., Ohtsuki T.

Sensors 24 （ 2 ）  2024年

研究論文（学術雑誌）, 最終著者,  ISSN  14248220

　概要を見る

In health monitoring systems for the elderly, a crucial aspect is unobtrusively and continuously monitoring their activities to detect potentially hazardous incidents such as sudden falls as soon as they occur. However, the effectiveness of current non-contact sensor-based activity detection systems is limited by obstacles present in the environment. To overcome this limitation, a straightforward yet highly efficient approach involves utilizing multiple sensors that collaborate seamlessly. This paper proposes a method that leverages 2D Light Detection and Ranging (Lidar) technology for activity detection. Multiple 2D Lidars are positioned in an indoor environment with varying obstacles such as furniture, working cohesively to create a comprehensive representation of ongoing activities. The data from these Lidars is concatenated and transformed into a more interpretable format, resembling images. A convolutional Long Short-Term Memory (LSTM) Neural Network is then used to process these generated images to classify the activities. The proposed approach achieves high accuracy in three tasks: activity detection, fall detection, and unsteady gait detection. Specifically, it attains accuracies of 96.10%, 99.13%, and 93.13% for these tasks, respectively. This demonstrates the efficacy and promise of the method in effectively monitoring and identifying potentially hazardous events for the elderly through 2D Lidars, which are non-intrusive sensing technology.

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Towards Building Trust in Machine Learning Models: Quantifying the Explainability by SHAP and References to Human Strategy

Z. Li, M. Bouazizi, T. Ohtsuki, M. Ishii, and E. Nakaraha

Sensors 2024年

責任著者

Dementia Detection from Speech: What If Language Models Are Not the Answer?

Bouazizi M., Zheng C., Yang S., Ohtsuki T.

Sensors 15 （ 1 ）  2024年

研究論文（学術雑誌）, 最終著者

　概要を見る

A growing focus among scientists has been on researching the techniques of automatic detection of dementia that can be applied to the speech samples of individuals with dementia. Leveraging the rapid advancements in Deep Learning (DL) and Natural Language Processing (NLP), these techniques have shown great potential in dementia detection. In this context, this paper proposes a method for dementia detection from the transcribed speech of subjects. Unlike conventional methods that rely on advanced language models to address the ability of the subject to make coherent and meaningful sentences, our approach relies on the center of focus of the subjects and how it changes over time as the subject describes the content of the cookie theft image, a commonly used image for evaluating one’s cognitive abilities. To do so, we divide the cookie theft image into regions of interest, and identify, in each sentence spoken by the subject, which regions are being talked about. We employed a Long Short-Term Memory (LSTM) neural network to learn different patterns of dementia subjects and control ones and used it to perform a 10-fold cross validation-based classification. Our experimental results on the Pitt corpus from the DementiaBank resulted in a 82.9% accuracy at the subject level and 81.0% at the sample level. By employing data-augmentation techniques, the accuracy at both levels was increased to 83.6% and 82.1%, respectively. The performance of our proposed method outperforms most of the conventional methods, which reach, at best, an accuracy equal to 81.5% at the subject level.

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A Robust Approach Assisted by Signal Quality Assessment for Fetal Heart Rate Estimation from Doppler Ultrasound Signal

Shi X., Niida N., Yamamoto K., Ohtsuki T., Matsui Y., Owada K.

Sensors 23 （ 24 ）  2023年

研究論文（学術雑誌）, 責任著者,  ISSN  14248220

　概要を見る

Fetal heart rate (FHR) monitoring, typically using Doppler ultrasound (DUS) signals, is an important technique for assessing fetal health. In this work, we develop a robust DUS-based FHR estimation approach complemented by DUS signal quality assessment (SQA) based on unsupervised representation learning in response to the drawbacks of previous DUS-based FHR estimation and DUS SQA methods. We improve the existing FHR estimation algorithm based on the autocorrelation function (ACF), which is the most widely used method for estimating FHR from DUS signals. Short-time Fourier transform (STFT) serves as a signal pre-processing technique that allows the extraction of both temporal and spectral information. In addition, we utilize double ACF calculations, employing the first one to determine an appropriate window size and the second one to estimate the FHR within changing windows. This approach enhances the robustness and adaptability of the algorithm. Furthermore, we tackle the challenge of low-quality signals impacting FHR estimation by introducing a DUS SQA method based on unsupervised representation learning. We employ a variational autoencoder (VAE) to train representations of pre-processed fetal DUS data and aggregate them into a signal quality index (SQI) using a self-organizing map (SOM). By incorporating the SQI and Kalman filter (KF), we refine the estimated FHRs, minimizing errors in the estimation process. Experimental results demonstrate that our proposed approach outperforms conventional methods in terms of accuracy and robustness.

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A Novel Approach for Heartbeat Rate Estimation Using 3-D Lidar and Multibeam MIMO Doppler Radar

Bouazizi M., Yamamoto K., Endo K., Ohtsuki T.

IEEE Sensors Journal 23 （ 24 ） 31259 - 31277 2023年

研究論文（学術雑誌）, 最終著者,  ISSN  1530437X

　概要を見る

Noncontact heartbeat detection and heart rate (HR) estimation have been hot topics of research over the last few years. By employing wireless sensors, such as Doppler sensors, the goal of these tasks is to detect subtle movements associated with heart movements. The detection is either used to reconstruct the R-peaks, thus the heartbeat detection, or simply identify the HR. However, due to the nature of the faint movements of the chest caused by the heart beats, unless the person is still, such movements are overwhelmingly obscured by the motion of the person. That being the case, we are interested in this work in identifying such instances where the person is not in motion, in which case, we measure their heartbeats and HR. To do so, we use a combination of a 3-D light detection and ranging (LiDAR) and a multiple-input multiple-output (MIMO) Doppler Radar. The former's objective is to recognize when the person is still, in which case their position and distance from the sensors are reported. The latter's objective is to create a beam directed toward the person's detected chest and measure the reflected signal, for heartbeat detection, HR, or respiration rate (RR) estimation. Our experiments demonstrate that determining the subject's location and distance and identifying their chest position using LiDAR, then collecting the data accordingly using the Doppler radar leads to better HR detection and R-R intervals (RRIs) estimation. For five different scenarios, the RRI estimation error reaches values between 112 and 231 ms.

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Mobility-Aware Routing and Caching in Small Cell Networks Using Federated Learning

Cao Y., Maghsudi S., Ohtsuki T., Quek T.Q.S.

IEEE Transactions on Communications 72 （ 2 ） 815 - 829 2023年

研究論文（学術雑誌）, 最終著者, 責任著者,  ISSN  00906778

　概要を見る

We consider a service cost minimization problem for resource-constrained small-cell networks with caching, where the challenge mainly stems from (i) the insufficient backhaul capacity and limited network bandwidth and (ii) the limited storing capacity of small-cell base stations (SBSs). Besides, the optimization problem is NP-hard since both the users' mobility patterns and content preferences are unknown. In this paper, we develop a novel mobility-Aware joint routing and caching strategy to address the challenges. The designed framework divides the entire geographical area into small sections containing one SBS and several mobile users (MUs). Based on the concept of one-stop-shop (OSS), we propose a federated routing and popularity learning (FRPL) approach in which the SBSs cooperatively learn the routing and preference of their respective MUs and make a caching decision. The FRPL method completes multiple tasks in one shot, thus reducing the average processing time per global aggregation of learning. By exploiting the outcomes of FRPL together with the estimated service edge of SBSs, the proposed cache placement solution greedily approximates the minimizer of the challenging service cost optimization problem. Theoretical and numerical analyses show the effectiveness of our proposed approaches.

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Online Trajectory Optimization for Energy-Efficient Cellular-Connected UAVs with Deep Image Prior Map Reconstruction

H. Zhao, Q. Hao, H. Huang, G. Gui, T. Ohtsuki, H. Sari, and F. Adachi

IEEE Trans. on Vehicular Technology 2023年

研究論文（学術雑誌）, 責任著者

Resource Allocation for Secure MIMO-SWIPT Systems in the Presence of Multi-Antenna Eavesdropper in Vehicular Networks

Ganapathy V., Ramachandran R., Ohtsuki T.

Sensors 23 （ 19 ）  2023年

研究論文（学術雑誌）, 最終著者,  ISSN  14248220

　概要を見る

In this paper, we optimize the secrecy capacity of the legitimate user under resource allocation and security constraints for a multi-antenna environment for the simultaneous transmission of wireless information and power in a dynamic downlink scenario. We study the relationship between secrecy capacity and harvested energy in a power-splitting configuration for a nonlinear energy-harvesting model under co-located conditions. The capacity maximization problem is formulated for the vehicle-to-vehicle communication scenario. The formulated problem is non-convex NP-hard, so we reformulate it into a convex form using a divide-and-conquer approach. We obtain the optimal transmit power matrix and power-splitting ratio values that guarantee positive values of the secrecy capacity. We analyze different vehicle-to-vehicle communication settings to validate the differentiation of the proposed algorithm in maintaining both reliability and security. We also substantiate the effectiveness of the proposed approach by analyzing the trade-offs between secrecy capacity and harvested energy.

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Specific Emitter Identification Using Adaptive Signal Feature Embedded Knowledge Graph

Hua M., Zhang Y., Sun J., Adebisi B., Ohtsuki T., Gui G., Wu H.C., Sari H.

IEEE Internet of Things Journal 11 （ 3 ） 4722 - 4734 2023年

研究論文（学術雑誌）, 責任著者

　概要を見る

Specific emitter identification (SEI) plays an important role in secure Industrial Internet of Things (IIoT). In recent years, many SEI methods based on machine learning (ML) and deep learning (DL) have been proposed due to their great performance. However, DL-based SEI methods are accompanied by huge computation overhead, which is not suitable for IIoT applications. In addition, the existing ML-based SEI methods rely on feature extraction and a heavy and redundant classifier, which do not ensure optimal feature combination and efficient computation. To solve the above problem, we propose an improved DL-based SEI method using a signal feature embedded knowledge graph (KG) composed of universal features. To the best of our knowledge, this is the first attempt to apply KG for SEI technology. Specifically, we explore an adaptive feature combination (AFC) strategy through the attention mechanism to realize an efficient SEI classifier. The simulation results show that the proposed KG-AFC algorithm outperforms existing SEI methods in identification performance and computation overhead. At the same time, under the optimal compression rate, the average accuracy of the proposed SEI algorithm is higher than 99.2% and can effectively reduce complexity. The code and the data set can be downloaded from https://github.com/Lollipophua/KG-AFC.

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Detecting Dementia from Face-Related Features with Automated Computational Methods

Zheng C., Bouazizi M., Ohtsuki T., Kitazawa M., Horigome T., Kishimoto T.

Bioengineering 10 （ 7 ）  2023年

研究論文（学術雑誌）, 責任著者

　概要を見る

Alzheimer’s disease (AD) is a type of dementia that is more likely to occur as people age. It currently has no known cure. As the world’s population is aging quickly, early screening for AD has become increasingly important. Traditional screening methods such as brain scans or psychiatric tests are stressful and costly. The patients are likely to feel reluctant to such screenings and fail to receive timely intervention. While researchers have been exploring the use of language in dementia detection, less attention has been given to face-related features. The paper focuses on investigating how face-related features can aid in detecting dementia by exploring the PROMPT dataset that contains video data collected from patients with dementia during interviews. In this work, we extracted three types of features from the videos, including face mesh, Histogram of Oriented Gradients (HOG) features, and Action Units (AU). We trained traditional machine learning models and deep learning models on the extracted features and investigated their effectiveness in dementia detection. Our experiments show that the use of HOG features achieved the highest accuracy of 79% in dementia detection, followed by AU features with 71% accuracy, and face mesh features with 66% accuracy. Our results show that face-related features have the potential to be a crucial indicator in automated computational dementia detection.

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Intelligent Cellular Offloading with VLC-enabled Unmanned Aerial Vehicles

F. H. Panahi, F. H. Panahi, and T. Ohtsuki

in IEEE Internet of Things Journal 2023年

研究論文（学術雑誌）, 共著, 最終著者

Investigation of Data Leakage in Deep Learning-based Blood Pressure Estimation using Photoplethysmogram/ Electrocardiogram

R. Yoshizawa, K. Yamamoto, and T. Ohtsuki

IEEE Sensors Journal 2023年

研究論文（学術雑誌）, 共著, 最終著者

Deep Learning and Image Super-Resolution-Guided Beam and Power Allocation for mmWave Networks

Cao Y., Ohtsuki T., Maghsudi S., Quek T.Q.S.

IEEE Transactions on Vehicular Technology 72 （ 11 ） 15080 - 15085 2023年

研究論文（学術雑誌）, 共著,  ISSN  00189545

　概要を見る

In this article, we develop a deep learning (DL)-guided hybrid beam and power allocation approach for multiuser millimeter-wave (mmWave) networks, which facilitates swift beamforming at the base station (BS). The following persisting challenges motivated our research: (i) User and vehicular mobility, as well as redundant beam-reselections in mmWave networks, degrade the efficiency; (ii) Due to the large beamforming dimension at the BS, the beamforming weights predicted by the cutting-edge DL-based methods often do not suit the channel distributions; (iii) Co-located user devices may cause a severe beam conflict, thus deteriorating system performance. To address the aforementioned challenges, we exploit the synergy of supervised learning and super-resolution technology to enable low-overhead beam- and power allocation. In the first step, we propose a method for beam-quality prediction. It is based on deep learning and explores the relationship between high- and low-resolution beam images (energy). Afterward, we develop a DL-based allocation approach, which enables high-accuracy beam and power allocation with only a portion of the available time-sequential low-resolution images. Theoretical and numerical results verify the effectiveness of our proposed framework.

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Max-Min Fair 3D Trajectory Design and Transmission Scheduling for Solar-Powered Fixed-Wing UAV-Assisted Data Collection

C. Sun, X. Xiong, Z. Zhai, W. Ni, T. Ohtsuki, and X. Wang

IEEE Trans. on Wireless Communications 2023年

研究論文（学術雑誌）, 共著

A 2D Lidar-equipped unmanned robot-based approach for indoor human activity detection

M. Bouazizi, A.-L. Mora, and T. Ohtsuki

Sensors 2023 2023年

研究論文（学術雑誌）, 共著, 最終著者

Multi-Person Position Estimation Based on Correlation Between Received Signals Using MIMO FMCW Radar

K. Endo, T. Ishikawa, K. Yamamoto, and T. Ohtsuki

in IEEE Access 11   2610 - 2620 2023年

研究論文（学術雑誌）, 共著, 最終著者

A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

K. Endo, K. Yamamoto, and T. Ohtsuki

Sensors 2022 2022年

研究論文（学術雑誌）, 共著, 最終著者

Deep Reinforcement Learning Evolution Algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System

S. Yang, M. Bouazizi, T. Ohtsuki, Y. Shibata, W. Takabatake, K. Hoshino, and A. Nagate

Future Internet 2023 2023年

研究論文（学術雑誌）, 共著

An Intelligent Path Planning Mechanism for Firefighting in Wireless Sensor and Actor Networks

F. H. Panahi, F. H. Panahi, and T. Ohtsuki

in IEEE Internet of Things Journal 2023年

研究論文（学術雑誌）, 共著, 最終著者

Distributed Feature Selection Considering Data Pricing Based on Edge Computing in Electricity Spot Markets

Y. Hu, X. Guan, B. Hu, Y. Liu, H. Chen and T. Ohtsuki

in IEEE Internet of Things Journal 10 （ 3 ） 2231 - 2244 2023年02月

研究論文（学術雑誌）, 共著, 最終著者

SAR Image Change Detection in Spatial-Frequency Domain Based on Attention Mechanism and Gated Linear Unit

C. Zhao, L. Ma, L. Wang, T. Ohtsuki, P.T. Mathiopoulos, and Y. Wang

IEEE Geoscience and Remote Sensing Letters 2023年

研究論文（学術雑誌）, 共著

Graph Learning Empowered Situation Awareness in Internet of Energy with Graph Digital Twin

L. Sui, X. Guan, C. Cui, H. Jiang, H. Pan and T. Ohtsuki

in IEEE Transactions on Industrial Informatics 2022年

研究論文（学術雑誌）, 共著, 最終著者

Make Smart Decisions Faster: Deciding D2D Resource Allocation via Stackelberg Game Guided Multi-Agent Deep Reinforcement Learning

D. Shi, L. Li, T. Ohtsuki, M. Pan, Z. Han and H. V. Poor

in IEEE Transactions on Mobile Computing 21 （ 12 ） 4426 - 4438 2022年12月

研究論文（学術雑誌）, 共著

Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment

X. Shi, K. Yamamoto, T. Ohtsuki, Y. Matsui, and K. Owada

Bioengineering 2023年

研究論文（学術雑誌）, 共著

Fetal Arrhythmia Detection based on Labeling Considering Heartbeat Interval

S. Nakatani, K. Yamamoto, and T. Ohtsuki

Bioengineering 2023年

研究論文（学術雑誌）, 共著, 最終著者

A Reinforcement Learning-based Fire Warning and Suppression System Using Unmanned Aerial Vehicles

F.H. Panahi, F.H. Panahi, and T. Ohtsuki

IEEE Transactions on Instrumentation and Measurement 2022年

研究論文（学術雑誌）, 共著, 最終著者

Heart Action Monitoring From Pulse Signals Using A Growing Hybrid Polynomial Network

L. Wang, C. Zhao, P.T. Mathiopoulos, and T. Ohtsuki

Engineering Applications of Artificial Intelligence 117 2023年01月

研究論文（学術雑誌）, 共著, 最終著者

SAR image change detection based on saliency region guidance and SIFT keypoint extraction

Wang L., Sun B., Zhao C., Mazhar S., Ohtsuki T., Takis Mathiopoulos P., Adachi F.

Pattern Recognition 172 2026年04月

ISSN  00313203

　概要を見る

Synthetic Aperture Radar (SAR) can operate under all-weather, all-day conditions, playing a crucial role in regional change detection (CD). However, due to its unique imaging principles, SAR images contain significant speckle noise and blurred boundary and detail features, which reduces the detection accuracy and leads to missed detection and false detection. To address these issues, this paper proposes a SAR image CD method based on saliency region guidance and Scale-Invariant Feature Transform (SIFT) keypoint extraction to reduce the interference of speckle noise. First, a saliency region guidance method is introduced to analyze the saliency of local features in SAR images, extracting potentially changed regions and reducing the interference of speckle noise. Second, the SIFT is employed to extract keypoints in regions significantly different from the background in the difference map, leveraging its robustness to speckle noise. By extracting keypoints, the approximate location and extent of the changed regions are determined. These are, then, fused with the saliency region information, enhancing the saliency weights of pixels around keypoints for more extraction of change regions. Finally, a Vision Transformer (ViT) detection network is used for SAR image CD, utilizing the combined saliency information from the original saliency map and SIFT keypoints. This approach effectively integrates SIFT's stable description of local features with ViT's modeling capability for global features, improving the model's accuracy and robustness.

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Depthwise-Attentive Hierarchical Cross-Modal Knowledge Distillation Network for Rail Surface Defect Detection

Zhou X., Guan X., Peng Y., Zhang Z., Zhou X., Chen H., Ohtsuki T., Han Z.

IEEE Internet of Things Journal 13 （ 6 ） 10915 - 10928 2026年03月

　概要を見る

Accurate detection of surface defects on railway tracks is critical for safe railway operation. Most existing models rely solely on red-green-blue (RGB) images, limiting their ability to capture structural information. Incorporating depth features provides richer spatial cues, significantly improving detection accuracy. However, current RGB and depth (RGB-D) dual-stream models suffer from high computational complexity and hardware dependencies, making them impractical for real-world deployment. To address these limitations, we propose DAHNet, an asymmetric knowledge distillation model with a teacher-student architecture. DAHNet-T serves as the teacher network, taking RGB-D inputs and integrating a cross-modal attention feature enhancer (CAFE) module to capture contextual information, along with a depthwise feature interaction block (DFIB) for efficient cross-modal fusion. DAHNet-S is the student network, a lightweight single-stream RGB model employing depthwise separable convolutions to reduce computation. We introduce a multilevel distillation strategy with dynamic temperature scaling to balance coarse-grained and fine-grained knowledge transfer, while incorporating contrastive learning and structural loss to improve pixel-level accuracy. Extensive experiments on the NEU RSDDS-AUG dataset demonstrate that our distilled model DAHNet-KD outperforms state-of-the-art methods. Compared to DAHNet-T, the number of parameters is reduced from 87.72 to 13.97 MParams, and the computational cost decreases from 19.79 to 5.41 GFLOPs. The proposed model achieves superior performance across various evaluation metrics and also generalizes well on other public datasets. Therefore, the model provides a lightweight and high-accuracy solution for deployment on mobile devices in real-world industrial scenarios.

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MuECNet: A Lightweight Multiuser Enhanced Convolutional Architecture for Robust CSI-Based Human Activity Recognition in Real-World IoT Environments

Miao F., Takyu O., Shan L., Yin Y., Zhao O., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 13 （ 6 ） 12209 - 12219 2026年03月

　概要を見る

Wi-Fi channel state information (CSI)-based human activity recognition (HAR) leverages rich channel propagation characteristics to enable nonintrusive, privacy-preserving, and device-free sensing. In multiuser wireless environments, however, HAR faces significant challenges, including multipath interference, signal overlap, label ambiguity, cross-domain channel variability, and constraints imposed by real-time deployment on resource-limited edge devices. This article presents multiuser enhanced convolutional network (MuECNet), a lightweight and modular deep learning framework designed to operate under realistic multiuser, multiactivity CSI sensing conditions. MuECNet integrates three key components: 1) an enhanced convolutional encoding module (ECEM) for fine-grained temporal-spectral feature extraction that preserves channel propagation signatures; 2) a branch-wise feature normalization (BFN) module for user-specific channel representation learning; and 3) an adaptive decision module for multilabel activity inference. To improve robustness under diverse and dynamic channel conditions, we introduce MixUp-based data augmentation to emulate activity overlap and reduce label ambiguity. Evaluations on the WiMANS dataset show that MuECNet achieves 64.46% (2.4 GHz) and 64.15% (5 GHz) accuracy with only 1.19-M parameters, 1.74-G FLOPs, and 0.28-s inference latency, outperforming baseline models such as ABLSTM and THAT while reducing model size by up to 75%. Ablation studies confirm the contribution of each module, with accuracy drops of up to 6.41% when removed. These results demonstrate that MuECNet provides a robust and communication-efficient solution for integrating CSI-based sensing into future wireless networks and Internet of Things (IoT) systems.

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Synthetic aperture radar image change detection based on multi-scale deep adaptive convolution and spatial-frequency dual-domain feature extraction

Wang L., Jiahui E., Zhao C., Gao W., Mathiopoulos P., Ohtsuki T.

Expert Systems with Applications 301 2026年03月

ISSN  09574174

　概要を見る

Synthetic Aperture Radar (SAR) change detection is the process of identifying image changes by comparing SAR images of the same area captured at different time intervals. Considering the interference of speckle noise and the lack of spatial adaptability, this paper proposes a spatial-frequency domain feature fusion cooperative detection framework. This method extracts local structural features in the spatial domain while incorporating high-frequency components from the frequency domain to enhance edge feature representation and improve the discrimination of change regions. Additionally, a multi-region dynamic selection mechanism is designed to adaptively capture multi-scale contextual information through cascaded convolutional layers, overcoming the limitations of fixed receptive fields in complex dynamic scenarios. Furthermore, by integrating an attention mechanism with the Swish-Gated Linear Unit (SWiGLU), the network’s feature representation capability is optimized, effectively enhancing the precision of subtle change detection and significantly reducing the impact of speckle noise. This further improves the stability and reliability of SAR change detection. Through performance evaluation on five datasets, we compare the proposed method with state-of-the-art SAR change detection techniques. The results demonstrate that our method exhibits significant advantages in terms of accuracy and kappa coefficient.

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Clock Asynchronous Traffic Signal Timing for Multi-Intersections Based on a Joint Traffic Prediction and Control Method

Wang T., Yang G., Wang L., Mathiopoulos P.T., Ohtsuki T., Ouyang M.

IEEE Transactions on Vehicular Technology 75 （ 1 ） 1111 - 1125 2026年01月

ISSN  00189545

　概要を見る

One major challenge in improving traffic efficiency for multi-intersection signal timing is the synchronous iteration across all intersections. To deal with this problem, in this paper, a novel clock asynchronous traffic signal timing method for multi-intersections based on a joint traffic prediction and control method is proposed and its performance is analyzed. To improve traffic signal timing efficiency, each intersection is viewed as an agent which by following this joint approach designs, in a flexible way, actions, states and rewards. Using traffic prediction and control data, each intersection first computes a value which corresponds to a change in the vehicle position in its lane under control and then adds up this value to other values obtained from all the lanes under control to produce the reward. Traditional methods cannot effectively coordinate multi-intersections with different signal timing time. This has led to the introduction of a novel clock consistency mechanism the operation of which is implemented through a Multi-Agent Coordinated Reinforcement Learning (MACRL) algorithm. For any traffic intersection, the mechanism can estimate, within the duration of the previous action, the impact of adjacent intersections. This is accomplished by considering the previous intersections' reward information and quantify it as its joint reward. The proposed MACRL algorithm optimizes multi-intersections traffic by jointly considering these rewards. Using real traffic data, extensive simulations show that the proposed method outperforms existing schemes under the same conditions in traffic efficiency. The method also delivers more accurate traffic signal timing, reducing congestion and improving overall efficiency.

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A Comprehensive Survey on Self-Supervised Learning for Specific Emitter Identification

Liu C., Gui G., Wang Y., Ohtsuki T., Niyato D., Shen X.

IEEE Communications Surveys and Tutorials 28   1749 - 1775 2026年

　概要を見る

The rapid proliferation of the Internet of Things (IoT) has intensified the need for strong authentication mechanisms to ensure the integrity and reliability of connected devices. Recent advancements in Deep Learning (DL)-based Specific Emitter Identification (SEI) have demonstrated significant potential in leveraging unique Radio Frequency Fingerprints (RFF) for accurate device identification and authentication. However, the efficacy of these DL-based SEI methods is critically dependent on the availability of extensive labeled datasets, which are often scarce and expensive to obtain in practical applications. To address this limitation, Self-Supervised Learning (SSL) becomes a promising solution, capable of harnessing unlabeled data to learn effective representations. Furthermore, current surveys and reviews on SEI are generally summarized from a high-level perspective, lacking a detailed discussion of SEI methods under label-limited scenarios. This article comprehensively surveys SSL-based SEI, including its motivation, definition, paradigms, related work, challenges, and future direction combined with large models. To help readers quickly engage with this field, this paper also undertakes two specific efforts: collecting and organizing currently available open-source datasets with download links and comparing various SSL-based SEI methods with related codes.

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Long-Term Sum-Rate Maximization in Irregular RIS-Aided V2X Communications Using Lyapunov Optimization

Ganeshkumar L., Ramachandran R., Ohtsuki T.

IEEE Open Journal of the Communications Society 7   2446 - 2463 2026年

　概要を見る

In emerging smart cities and intelligent transportation systems (ITS) ecosystems, sixth-generation (6G) vehicular networks demand sustainable wireless environments capable of adapting to high mobility and dynamic propagation conditions. In dynamic vehicular networks, irregular reconfigurable intelligent surface (IRIS) with non-uniformly distributed elements over a larger surface offer improved coverage, reduced interference, and lower power consumption. This article undertakes a rigorous investigation into IRIS-assisted downlink vehicular communication in the context of Doppler shift, with the objective of optimizing the long-term net weighted average sum-rate under both perfect and imperfect channel state information (CSI). This task entails a comprehensive joint optimization of IRIS topology selection, phase-shift matrix, and beamforming vectors, while ensuring queue stability. To address this stochastic network optimization problems involving time-varying system dynamics, a Lyapunov optimization-based framework is proposed. This framework transforms the stochastic problem into a deterministic form, ensuring queue stability without requiring knowledge of future channel conditions. Subsequently, a joint optimization strategy based on the block coordinate descent (BCD) algorithm is applied to make optimal decisions of IRIS topology selection, phase-shift matrix, and beamforming vectors in each time slot. To validate the effectiveness of the proposed framework, extensive simulations were conducted and benchmarked against two established schemes: one employing conventional IRIS configurations in wireless networks and the other utilizing non-orthogonal multiple access (NOMA) techniques. The results consistently demonstrate that the proposed IRIS-assisted Lyapunov optimization framework significantly outperforms both benchmarks in terms of achievable sum-rate and maintains queue stability.

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Lightweight Regularized Network for Multilabel Indoor HAR in Multiuser CSI Environments With Uncertainty Quantification

Miao F., Liu C., Lu Z., Shan L., Takyu O., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 13 （ 4 ） 6475 - 6484 2026年

　概要を見る

Human activity recognition (HAR) with Wi-Fi channel state information (CSI) is attractive for privacy-preserving, device-free sensing; yet, real deployments still struggle with three coupled issues: robustness across rooms and bands, efficiency on edge hardware, and unified support for multiple tasks. We present UN-2DCNN, a lightweight 2-D convolutional neural network (CNN) pipeline tailored to indoor, multiuser CSI sensing. The design reduces temporal redundancy via a simple temporal skipping augmentation, learns a compact 128-D representation with a small CNN + global average pooling (GAP) backbone, and injects reliability feedback through uncertainty-aware feature scaling (UAFS): Stage-1 predictive entropy is mapped to a gating weight that rescales features before a second decision head. A channel-attention MLP further suppresses spurious subcarrier responses. Evaluated on a recent multiuser CSI benchmark across classrooms, meeting rooms, and empty environments at 2.4/5 GHz, UN-2DCNN consistently outperforms competitive recurrent neural network (RNN)/Transformer baselines while using only ~1M parameters and maintaining sub-2-s test-time latency. Beyond higher accuracy, the model exhibits faster, smoother convergence and improved calibration (fewer overconfident errors). Ablations confirm that removing attention, UAFS, or the second-stage head yields consistent drops, and simple temporal skipping on the data side complements model-side selectivity. These results indicate that reliability-aware, lightweight designs can deliver practical accuracy–efficiency tradeoffs for CSI-based perception on edge/IoT platforms.

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Integrated Deployment and Resource Allocation in Multi-Layer UAV-Enabled NOMA Wireless Caching Networks

Yin Y., Bouazizi M., Hu B., Gui G., Ohtsuki T.

IEEE Internet of Things Journal 13 （ 7 ） 14898 - 14911 2026年

　概要を見る

Conventional multi-unmanned aerial vehicle (UAV) assisted non-orthogonal multiple access (NOMA) wireless caching networks (WCNs) usually operate in a distributed and non-collaborative manner, where each UAV serves users independently without coordination or relay support. When UAVs move beyond the communication range of ground base stations (BSs), backhaul disruption occurs, leading to high user latency and limited system scalability. To address these issues, we propose a multi-layer UAV-assisted NOMA WCN architecture, where a primary UAV (PUAV) communicates with the BS and cooperates with multiple secondary UAVs (SUAVs). The PUAV not only acts as a control and coordination hub but also serves as a relay for content transmission to SUAVs when necessary. To minimize user transmission latency, we propose a joint iterative algorithm that integrates user clustering, user pairing, power allocation, and UAV deployment. First, we develop an Advanced Balanced K-Means++ (ABKM) algorithm to ensure that each cluster contains a balanced number of users and to reduce the distance between users and their serving SUAVs. Next, we derive the NOMA power allocation factor that minimizes user transmission latency, ensuring efficient resource distribution among all paired users. Furthermore, we analyze the impact of PUAV and SUAV placement on user latency and propose a two-stage particle swarm optimization (PSO)-based algorithm to iteratively optimize the deployment of all UAVs. Finally, the user pairs and power allocation are jointly optimized based on the updated deployment of all UAVs to further reduce user latency. Simulation results show that, compared with a single-layer UAV architecture and benchmark schemes, the proposed multi-layer design with joint optimization achieves lower user latency. Additionally, comparisons with the optimal power allocation search method confirm the validity of the derived NOMA power allocation factor.

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Handover Optimization for UAV-Assisted LEO Satellite Networks Based on IPPO and Three-Sided Matching Theory

Yang F., Li M., Wang K., Si P., Ohtsuki T., Richard Yu F.

IEEE Internet of Things Journal 13 （ 1 ） 152 - 166 2026年

　概要を見る

Due to the triple mobility of mobile users (MUs), unmanned aerial vehicle (UAV) relays, and low Earth orbit (LEO) satellites, handover becomes a critical and challenging issue for maintaining the continuity and quality of communication services in UAV-assisted LEO satellite networks. This article proposes a distributed handover decision-making process aimed at improving scalability and reducing communication overhead. The handover problem is modeled as a decentralized Markov decision process (DEC-MDP) with the objective of maximizing the total end-to-end (E2E) throughput. We design an independent proximal policy optimization-based distributed intelligent handover (IPPO-DIH) algorithm within a centralized training with decentralized execution framework to solve the DEC-MDP. To analyze the theoretical optimal E2E throughput, we eliminate the correlation between handover decisions at different time steps. A three-sided matching algorithm with theoretical convergence guarantees is designed to obtain a stable matching among MUs, UAV relays, and LEO satellites at each time step. These stable matchings are combined to provide a theoretical performance benchmark for the handover algorithms. Simulation results validate the convergence of the proposed IPPO-DIH and three-sided matching algorithms. Additionally, the total E2E throughput achieved by the IPPO-DIH algorithm approaches the theoretical performance benchmark and outperforms typical handover algorithms.

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Generative Model-Aided Continual Learning for CSI Feedback in FDD mMIMO-OFDM Systems

Liu G., Cao Y., Ohtsuki T., He J., Mumtaz S.

IEEE Signal Processing Letters 33   1013 - 1017 2026年

ISSN  10709908

　概要を見る

Deep autoencoder (DAE) frameworks have demonstrated their effectiveness in reducing channel state information (CSI) feedback overhead in massive multiple-input multiple-output (mMIMO) orthogonal frequency division multiplexing (OFDM) systems. However, existing CSI feedback models struggle to adapt to dynamic environments caused by user mobility, requiring retraining when encountering new CSI distributions. Moreover, returning to previously encountered environments often leads to performance degradation due to catastrophic forgetting. Continual learning involves enabling models to incorporate new information while maintaining performance on previously learned tasks. To address these challenges, we propose a generative adversarial network (GAN)-based learning approach for CSI feedback. By using a GAN generator as a memory unit, our method preserves knowledge from past environments and ensures consistently high performance across diverse scenarios without forgetting. Simulation results show that the proposed approach enhances the generalization capability of the DAE framework while maintaining low memory overhead. Furthermore, it can be seamlessly integrated with other advanced CSI feedback models, highlighting its robustness and adaptability.

• Access to Document (DOI)

Generalized Wideband Power Amplifier Modeling for Vehicular Wireless Transceivers Using Kolmogorov-Arnold Convolutional Liquid Neural Networks

Wu Y., Wang Q., Ohtsuki T., Niyato D., Adachi F., Gui G.

IEEE Transactions on Vehicular Technology 2026年

ISSN  00189545

　概要を見る

Accurate behavioral modeling of power amplifiers (PAs) is crucial for digital pre-distortion and energy-efficient vehicular wireless transceivers, yet remains challenging due to bandwidth-dependent nonlinearities and fast-varying drive conditions in practical RF front-ends. Conventional PA models struggle to capture coupled temporal memory effects and spatial signal dependencies, limiting their effectiveness in broadband vehicular transmitters. To address this issue, we propose a Kolmogorov–Arnold Convolutional Liquid Neural Network (KACLN) that jointly leverages Kolmogorov–Arnold representation learning and liquid neuron adaptive dynamics to model nonlinear distortion with strong temporal generalization. The proposed KACLN integrates one-dimensional and two-dimensional convolutional operator kernels to simultaneously characterize long- and short-term PA memory, frequency-selective distortion patterns, and cross-sample bandwidth interactions. Experiments over diverse vehicular RF operating regimes demonstrate that KACLN improves modeling precision and robustness, achieving 1.789 dB normalized mean square error (NMSE) reduction and 2.639 dB adjacent channel error power ratio (ACEPR) suppression gains compared with the best performed MCLDNN. The proposed KACLN further mitigates efficiency degradation associated with static power back-off, enhancing effective output utilization under fast-varying channel and wideband signal envelopes. These results validate KACLN as a practical, real-time capable nonlinear surrogate suitable for next-generation vehicular PA linearization, DPD-aided waveform shaping, and low-latency wireless transmitter design.

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Efficient Voxel-Based mmWave Radar HAR with Early Temporal Fusion and a Compact 3D–2D Hybrid Network

Zhao R., Miao F., Liu Y., Yin Y., Ohtsuki T., Gui G., Adachi F.

IEEE Internet of Things Journal 2026年

　概要を見る

Millimeter-wave (mmWave) radar has emerged as a powerful sensing modality for human activity recognition (HAR) owing to its capability to capture 3D point cloud sequences without privacy concerns. However, effectively modeling the sparse, irregular, and non-uniform nature of radar data remains a major challenge. Existing approaches often rely on highly complex network architectures to improve accuracy, which leads to excessive computational overhead and poor scalability. To overcome these limitations, this paper proposes a compact hybrid feature extraction network for voxelized radar point cloud classification, which performs early-stage spatio-temporal fusion and is termed STFusionNet (Spatial-Temporal Fusion Network). The STFusionNet comprises (i) a lightweight 3D convolutional front-end, which treats consecutive temporal frames as input channels to encode motion dynamics into a compact volumetric representation and further aggregates features along the depth axis; (ii) a minimalist 2D convolutional backbone after a Depth-to-Channel Folding operation, which captures spatial features with minimal computational cost. Extensive experiments on the public MMActivity and MiliPoint datasets demonstrate that our model achieves competitive accuracy (e.g., 92.20% on MMActivity and 72.86% on MiliPoint) with only about 50K parameters and 46 MMac, outperforming or matching representative spatio-temporal baselines under a much lower computational budget. Extensive ablation experiments verify the contribution of key components, while statistical significance tests confirm the reliability of the performance improvements. These results confirm that STFusionNet offers a robust, efficient, and generalizable solution for mmWave radar-based HAR in Internet of Things (IoT) applications.

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Dual-Stream Multimodal Fusion With Local–Global Attention for Remote-Sensing Object Detection

Huang Y., Wang Z., Tang T., Ohtsuki T., Gui G.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 19   1691 - 1702 2026年

ISSN  19391404

　概要を見る

Object detection in remote-sensing imagery plays a crucial role in providing precise geospatial information for urban planning and environmental monitoring. However, real-world remote-sensing scenarios often involve complex conditions, such as varying illumination, weather interference, and low signal-to-noise ratios, which significantly degrade the performance of traditional single-modal detection methods. To overcome these limitations, multimodal object detection has developed, demonstrating great potential by integrating complementary information from multiple modalities. Nevertheless, existing multimodal frameworks still face challenges, such as insufficient cross-modal interaction, limited learning of complementary features, and high computational costs, due to redundant fusion in complex environments. To overcome these challenges, we propose an enhanced multimodal fusion strategy aimed at maximizing cross-modal feature learning capabilities. Our method employs a dual-backbone architecture to extract mode-specific representations independently, integrating a direction attention module at an early stage of each backbone to enhance discriminative feature extraction. We then introduce a dual-stream feature fusion network to effectively fuse cross-modal features, generating rich representations for the detection head. In addition, we embed a local–global channel attention mechanism in the head stage to strengthen feature learning in the channel dimension before generating the final prediction. Extensive experiments on the widely used vehicle detection in aerial imagery multimodal remote-sensing dataset demonstrate that our method achieves state-of-the-art performance, while evaluations on single-modal datasets confirm its exceptional generalization capability.

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Delay-Constrained Sum-Rate Maximization in STAR-RIS V2X Communications Using Lyapunov Framework

Ganeshkumar L., Ramachandran R., Ohtsuki T.

IEEE Access 14   857 - 874 2026年

　概要を見る

The emerging technology of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) enables 360° full-space signal coverage, making it a promising approach for resilient and sustainable smart cities in next-generation wireless networks. This work investigates long-term downlink communication in STAR-RIS-assisted vehicular networks, aiming to maximize the sum-rate while ensuring queue stability under high vehicle mobility. To model the stochastic variations arising from vehicle mobility and random data arrivals, we employ a Lyapunov optimization framework, which reformulates the long-term stochastic objective into a sequence of per-slot deterministic problems. This method is effective in vehicular networks, as it adapts decisions to rapidly changing channel conditions while guaranteeing queue stability without requiring mobility predictions. A joint optimization strategy is developed, wherein the roadside unit (RSU) beamforming vectors and the STAR-RIS phase-shift coefficients are alternately optimized using zeroforcing beamforming and projected gradient descent (PGD), respectively. Extensive simulations demonstrate that the proposed framework significantly improves the long-term throughput, fairness, and queue stability compared with five benchmark STAR-RIS schemes. The results remain robust across different mobility speeds, transmit powers, and STAR-RIS element configurations, confirming the effectiveness of the proposed approach in dynamic Vehicle-to-Everything (V2X) environments.

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Coverage and Rate Performance Analysis of Multi-RIS-Assisted Dual-Hop mmWave Networks

Cao Y., Wu X., He J., Ohtsuki T., Quek T.Q.S.

IEEE Transactions on Wireless Communications 25   3137 - 3152 2026年

ISSN  15361276

　概要を見る

Millimeter-wave (mmWave) communication, which operates at high frequencies, has gained extensive research interest due to its significantly wide spectrum and short wavelengths. However, mmWave communication suffers from the notable drawbacks as follows: i) The mmWave signals are sensitive to the blockage, which is caused by the weak diffraction ability of mmWave propagation; ii) Even though the introduction of reconfigurable intelligent surfaces (RISs) can overcome the performance degradation caused by serve path loss, the location of users and RISs as well as their densities incur a significant impact on the coverage and rate performance; iii) When the RISs’ density is very high, i.e., the network becomes extremely dense, a user sees several line-of-sight RISs and thus experiences significant interference, which degrades the system performance. Motivated by the challenges above, we first analyze distributed multi-RIS-aided mmWave communication system over Nakagami-m fading from the stochastic geometry perspective. To be specific, we analyze the end-to-end (E2E) signal-to-interference-plus-noise-ratio (SINR) coverage and rate performance of the system. To improve the system performance in terms of the E2E SINR coverage probability and rate, we study the optimization of the phase-shifting control of the distributed RISs and optimize the E2E SINR coverage particularly when deploying a large number of reflecting elements in RISs. To facilitate the study, we optimize the dynamic association criterion between the RIS and destination. Furthermore, we optimize the multi-RIS-user association based on the physical distances between the RISs and destination by exploiting the maximum-ratio transmission. Numerical and simulation results indicate that the deployment of distributed RISs can significantly improve the E2E SINR coverage probability and achievable rate of the system compared to the selected benchmarks.

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Bio-Inspired Dendritic Liquid Neural Network for Robust Near-Field Positioning in B5G/6G

Zhu Q., Wang Y., Ohtsuki T., Mao S., Niyato D., Gui G.

IEEE Wireless Communications Letters 15   350 - 354 2026年

ISSN  21622337

　概要を見る

In B5G and 6G wireless networks, large-scale antenna arrays introduce spherical wavefronts in the near-field region, presenting both opportunities and challenges for communication and positioning. To address this, we propose a novel near-field positioning method using a bio-inspired dendritic liquid neural network (DLNN). The DLNN features a multi-branch dendritic structure, with each neuron processing signals through multiple parallel dendrites and a dynamic gating mechanism to adapt the weighting and fusion of outputs. This enhances its ability to capture complex spatiotemporal features. Additionally, the liquid layer with leaky integration processes time-series data, enabling strong adaptability and robust temporal memory. Simulation results show that DLNN outperforms other deep learning models in localization accuracy and robustness, with RMSE values of 0.1706 meters at 40 dBm and 0.2642 meters at 10 dBm, compared to 0.2836 meters and 0.2986 meters from other models. Its inference latency of 0.0580 seconds demonstrates excellent accuracy, speed, and energy efficiency, making DLNN highly suitable for real-time positioning in resource-constrained environments.

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Beyond Contact: An Open-Set Biometric Identification System Using Radar-Extracted Heart Signals

Xing Z., Bouazizi M., Ohtsuki T.

IEEE Journal of Biomedical and Health Informatics 30 （ 3 ） 2718 - 2731 2026年

ISSN  21682194

　概要を見る

This paper proposes a novel radar-based framework for non-contact biometric identification through heart signal extraction, targeting secure and privacy-conscious identification scenarios. Traditional biometric methods, such as fingerprint and facial recognition, face challenges including privacy concerns, vulnerability to spoofing, and the requirement for close proximity or direct line-of-sight. Our framework addresses these issues by reconstructing electrocardiogram (ECG) signals from radar-extracted cardiac motion data and implementing an open-set person identification system. Specifically, the framework integrates ECGReconNet, a specialized deep learning model for reconstructing ECG signals from human chest wall displacement, the InceptionTime model enhanced with fixed-Class Anchor Clustering (fixed-CAC) loss for robust feature anchoring, and a hypersphere-based delineation method to differentiate known from unknown individuals. Experimental results on a public dataset demonstrate state-of-the-art performance, achieving 99.61% accuracy in closed-set identification (27 subjects) and 93.97% accuracy under challenging open-set conditions (14 known and 13 unknown subjects). However, the proposed approach exhibits limitations, including sensitivity to abrupt body movements and environmental noise, potential performance degradation under severe cardiac irregularities, and reduced efficacy with increased numbers of unknown identities.

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Adaptive Task Offloading for Edge Computing in Internet of Energy via Retrieval-Augmented Generation

Zhou X., Guan X., Wang N., Chen H., Ohtsuki T., Zhang Y.

IEEE Network 2026年

ISSN  08908044

　概要を見る

Traditional task offloading in Internet of Energy (IoE) edge environments struggles to accommodate heterogeneous user tasks and dynamic system conditions. Although Large Language Models (LLMs) offer potential solutions, their deployment is hindered by the dynamic resource constraints and stringent Quality of Service (QoS) requirements of IoE. Retrieval-augmented generation (RAG) enables LLM to overcome these limitations. In this paper, we propose an RAG-empowered adaptive task offloading framework. First, the framework transforms raw IoE-collected data into LLM-comprehensible textual inputs. Second, we design an intelligent preference-matching module that guides RAG to retrieve relevant knowledge and cases using key QoS features. Third, we build a composite knowledge vector database with both positive and negative experiences. We introduce a failure severity value to quantify the importance of counterexamples and enhance decision-making robustness. Finally, we design a hybrid knowledge prompt generation module. It utilizes a hierarchical filtering strategy and optimizes the ranking of retrieved knowledge for comprehensive prompts. Experimental results demonstrate that our proposed framework significantly outperforms baselines, increasing cumulative reward by 48.4% while reducing latency and energy consumption by 44.8% and 50.9%, respectively.

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LRGD: Low-Rank Guided Diffusion for Robust Image Transmission in Semantic Communication

Zhao Z., Wu C., Lin Y., Zhong L., Ji Y., Ohtsuki T., Bennis M.

IEEE Transactions on Cognitive Communications and Networking 12   2439 - 2454 2026年

　概要を見る

Semantic communication has gained significant attention in modern data-intensive applications due to its efficient information transmission capabilities. While recent advancements in large language models and diffusion models have enabled generative image semantic communication in text-based and multimodal settings, existing approaches struggle with visual fidelity due to the inherent limitations of textual descriptions and the lack of dedicated design in multimodal methods. To address these issues, we propose a compressed sensing-driven multimodal semantic communication framework that transmits saliency-adaptive sparse sampling, edge contours, and textual descriptions of images at the transmitter and reconstructs images at the receiver using guided diffusion models. We innovatively design a low-rank guided diffusion (LRGD) model to adapt to this framework, employing a dynamic smoothing low-rank guidance process in the latent space using low-quality reconstructed images. This approach enables highly accurate restoration of visual details under a low sampling ratio without additional training. Compared to state-of-the-art methods, our framework uses model-independent and interpretable universal semantic representations for communication, significantly enhancing cross-model compatibility without model sharing between the transmitter and receiver. Extensive experiments on multiple datasets demonstrate that the proposed method achieves robust and superior image reconstruction performance across various noisy environments while compressing data to 5.7% of the original image size.

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Yours or Mine? Overwriting Attacks Against Neural Audio Watermarking

Yao L., Huang C., Wang S., Xue J., Guo H., Liu J., Lin P., Ohtsuki T., Pan M.

Proceedings of the Aaai Conference on Artificial Intelligence 40 （ 33 ） 27756 - 27763 2026年

ISSN  21595399

　概要を見る

As generative audio models are rapidly evolving, AIgenerated audios increasingly raise concerns about copyright infringement and misinformation spread. Audio watermarking, as a proactive defense, can embed secret messages into audio for copyright protection and source verification. However, current neural audio watermarking methods focus primarily on the imperceptibility and robustness of watermarking, while ignoring its vulnerability to security attacks. In this paper, we develop a simple yet powerful attack: the overwriting attack that overwrites the legitimate audio watermark with a forged one and makes the original legitimate watermark undetectable. Based on the audio watermarking information that the adversary has, we propose three categories of overwriting attacks, i.e., white-box, gray-box, and black-box attacks. We also thoroughly evaluate the proposed attacks on stateof-the-art neural audio watermarking methods. Experimental results demonstrate that the proposed overwriting attacks can effectively compromise existing watermarking schemes across various settings and achieve a nearly 100% attack success rate. The practicality and effectiveness of the proposed overwriting attacks expose security flaws in existing neural audio watermarking systems, underscoring the need to enhance security in future audio watermarking designs.

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VMamba-SLAM: Real-Time Dynamic Scene Adaptation Via Vision Mamba and Selective Feature Preservation for Enhanced Robotic Localization

Sun J., Shen X., Huang H., Li R., Wang Q., Zhao H., Ohtsuki T., Gui G.

IEEE Transactions on Vehicular Technology 2026年

ISSN  00189545

　概要を見る

Dynamic Simultaneous Localization and Mapping (SLAM) remains a challenging problem in robotics, particularly due to the interference of moving objects with feature-based localization and mapping. In this paper, we introduce a novel dynamic object-aware SLAM system built upon ORB-SLAM3, which integrates a computationally efficient Vision Mamba (VMamba) model with an advanced dynamic object filtering strategy. The proposed VMamba model, comprising only 8.8 million parameters, achieves high-precision object detection with minimal computational overhead, thereby enabling real-time operation on embedded platforms. A key contribution of this work is the development of an improved dynamic object filtering strategy that selectively retains static features within dynamic object bounding boxes. By leveraging Harris corners, optical flow, and epipolar geometry, our approach effectively eliminates dynamic outliers while preserving essential static scene information—a significant advancement over conventional methods that indiscriminately remove features. Experimental evaluations on the TUM Dynamic and BONN datasets demonstrate that our method attains superior Absolute Trajectory Error (ATE) performance compared to state-of-the-art dynamic SLAM approaches, including ORB-SLAM3, DynaSLAM, and YOLO-SLAM. Furthermore, real-time deployment on an NVIDIA Jetson Orin NX 16 GB platform incurs only a 6.45% reduction in Frames Per Second (FPS) relative to YOLO-SLAM, while achieving a 28.68% improvement in average ATE. These results underscore the computational efficiency of VMamba and the robust real-time capabilities of our SLAM system. Our study validates that integrating a lightweight, high-precision object detection model with an effective dynamic object filtering strategy can significantly enhance the robustness, accuracy, and real-time performance of dynamic SLAM, even on resource-constrained platforms. Future work will focus on further optimizing VMamba and extending the dynamic filtering techniques to better accommodate increasingly complex dynamic environments, thereby broadening its applicability in real-world robotic systems.

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Secrecy Rate Enhancement for Active RIS-Assisted MISO Wiretap Systems: A QoS-Aware Optimization Approach

Li W., Cao Y., Bouazizi M., Ohtsuki T., Mumtaz S.

IEEE Wireless Communications Letters 15   2398 - 2402 2026年

ISSN  21622337

　概要を見る

Reconfigurable intelligent surface (RIS)-assisted communication has emerged as a critical enabler for sixth-generation (6G) wireless networks. In particular, the active RIS architecture, by virtue of its signal amplification capability, overcomes the double-fading bottleneck inherent to passive RIS and provides a new avenue for secure physical-layer transmission. However, existing studies have not adequately addressed the secrecy performance of RIS in delay-constrained quality-of-service (QoS) scenarios. This letter investigates the problem of maximization of effective secrecy rate (ESR) in an active RIS-assisted multiple-input single-output (MISO) wiretap channel, subject to both transmit power and active RIS power constraints, under delay-limited QoS requirements. We tackle the nonconvex problem using an alternating optimization (AO)-based iterative algorithm. An analysis of the algorithmic complexity is also presented. Experiment results verify the performance gains achieved by the optimized design.

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Interpretability-Oriented UAV Recognition via Frequency-Aware Networks: A Coarse-to-Fine Framework for Enhanced Accuracy and Insight

Lu G., Wang S., Ohtsuki T., Sari H., Gui G.

IEEE Transactions on Information Forensics and Security 21   3312 - 3324 2026年

ISSN  15566013

　概要を見る

With the rapid proliferation of uncrewed aerial vehicles (UAVs) in civilian and industrial applications, the risk of malicious or unauthorized UAV use has become a critical security concern. Existing machine learning (ML)-based UAV recognition methods offer a certain degree of interpretability, but their performance is often limited in complex environments and across diverse UAV types. In contrast, deep learning (DL)-based methods exhibit strong representation capability, yet they generally lack physical interpretability. To address this issue, we propose an interpretable UAV recognition framework, termed frequency-aware network for UAV recognition (FANet-UAV), which performs coarse-to-fine feature learning in the frequency domain. Specifically, a multiplication filter module (MFM) is first designed to capture coarse-grained spectral patterns by exploiting multi-mode and multi-scale frequency characteristics of UAV signals. Based on these coarse representations, a convolutional neural network (CNN) is further employed to extract fine-grained discriminative features for accurate classification. Experimental results on two public UAV datasets demonstrate the effectiveness of the proposed method. In particular, FANet-UAV improves the recognition accuracy from 90.45% to 96.82% on DroneRFa and from 94.15% to 98.83% on DroneRF. Moreover, visualization results and channel-wise SHAP analysis provide both pre-hoc and post-hoc interpretability, revealing that FANet-UAV mainly relies on flight control signal (FCS) features for decision-making, while video transmission signal (VTS) features contribute less to the final recognition results.

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Twin-Timescale Traffic Signal Timing Method with Joint Communication Resource Allocation

Wang T., Yang G., Wang L., Mathiopoulos P.T., Ohtsuki T., Ouyang M.

IEEE Internet of Things Journal 2026年

　概要を見る

In multi-intersection traffic signal timing (TST) the use of a uniform cycle time reduces efficiency at low-volume intersections and signal changes which can alter vehicle flows and degrade communication quality. This paper presents a solution to this problem introducing a Twin-Timescale model whereby a multi-intersection TST method is jointly considered with communication resource allocation (CRA) strategies. For each intersection, a Twin-Timescale Reinforcement Learning (RL) algorithm is proposed, by considering both CRA and TST as the Short- and the Long- Timescale models, respectively, and jointly formulating their rewards. Based on the characteristics of this Twin-Timescale model, the reward function is constructed as a singularly perturbed model (SPM), and a state feedback controller that can ensure asymptotic stability is designed. Furthermore, a novel asynchronous time Multi-Agent Coordinated RL algorithm is proposed, which allows each intersection to generate each own joint parameters for its decision strategy which are based upon information obtained from neighboring intersections at asynchronous time. In addition, the use of Federated Learning (FL) in conjunction with the proposed algorithms is investigated, and its advantages in enhancing the resistance to malicious attacks are discussed. Using real traffic data, various performance evaluation results obtained by means of extensive computer simulation experiments have confirmed that the proposed TST-CRA joint optimization approach significantly improves traffic indicators and maintains the quality of the overall communication performance. Through numerical calculations, the state feedback controller coefficients have been determined.

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Toward Robust Radio Frequency Fingerprint Identification: A Federated Learning Framework With Feature Alignment

Wang Y., Cai Z., Tang T., Ohtsuki T., Gui G.

IEEE Transactions on Information Forensics and Security 21   2641 - 2651 2026年

ISSN  15566013

　概要を見る

With the growing adoption of Internet of Things (IoT) devices, ensuring the security of wireless communications has become increasingly critical. Radio frequency fingerprint identification (RFFI) has shown promise in this regard due to its capability of uniquely identifying devices. Although deep learning (DL) approaches have significantly improved RFFI performance, they typically rely on large-scale centralized data. This poses challenges in terms of privacy preservation and heterogeneous data distributions. To address the performance degradation caused by non-independent and identically distributed (non-IID) data in cross-receiver scenarios, this paper proposes a feature alignment strategy based on federated learning (FL) for RFFI. In such scenarios, due to differences in receiver hardware characteristics, deployment locations, and channel conditions, the signals captured by different receivers often exhibit distribution shifts, resulting in misaligned feature spaces across clients. The proposed method guides each client to learn aligned intermediate feature representations during local training, effectively mitigating the resulting adverse impact on model generalization. Experiments conducted on a real-world RF dataset demonstrate that the proposed method achieves higher identification accuracy and improved stability compared with representative federated baselines, including FedAvg and FedProx. The highest identification accuracy reaches 90.83%, and the performance gains are accompanied by generally reduced variance across different client configurations, highlighting the robustness and generalization capability of the proposed approach in heterogeneous wireless environments.

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Sum-Rate Maximization in Irregular STAR-RIS-Driven V2X Systems

Ganeshkumar L., Ramachandran R., Ohtsuki T.

IEEE Wireless Communications Letters 15   1504 - 1508 2026年

ISSN  21622337

　概要を見る

The reconfigurable intelligent surface (RIS) plays a crucial role in enabling sustainable communication infrastructure for 6G networks. This letter presents a long-term net weighted sum-rate maximization problem with queue stability for irregular simultaneous transmission and reflection RIS (ISTAR-RIS)-assisted vehicular networks. The model incorporates time-varying and mobility-induced channel dynamics. A joint optimization problem of the road side unit (RSU) beamforming vector, topological selection and the phase shift matrix of ISTAR-RIS elements is formulated. The simulation results demonstrate the effectiveness of the proposed approach in maximizing the sum-rate while comparing with existing benchmark methods in ISTAR-RIS-aided wireless and device-to-device (D2D) communication.

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Ship Classification in SAR Images Based on Full-Attention ViT and Image Reconstruction

Wang L., Zhao C., Wang T., Sun B., Qi Y., Qi B., Ohtsuki T.

Lecture Notes in Electrical Engineering 1366 LNEE   501 - 509 2026年

ISSN  18761100

　概要を見る

Classification of ships in synthetic aperture radar (SAR) images plays a critical role in maritime search and rescue as well as national defense security. Over the past few years, the swift advancement rapid of deep learning has opened up innovative technological directions for precise classification of SAR ship images. However, the unique imaging mechanism of SAR images, resulting in a lack of ship samples and insufficient feature information, continues to make accurate classification of SAR ships challenging. In this study, we propose an improved vision transformer (ViT) method using a full attention (FA) mechanism, incorporating channel attention after Self-Attention, dynamically selecting channels through reweighting to enhance comprehensive feature representation. To address the issue of insufficient training samples, we designed an image reconstruction module based on SRGAN, which generates high-resolution and detailed image data through adversarial learning, effectively compensating for the sample quantity in the dataset. Ultimately, the results section demonstrates that the method introduced delivers enhanced classification accuracy relative to current methods.

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Self-Supervised Federated Learning for UAV-IoT Systems With Dynamic Non-IID Data via Model Correlation

Li Z., Bouazizi M., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 13 （ 4 ） 5827 - 5841 2026年

　概要を見る

Federated learning (FL) offers significant advantages in preserving data privacy and enhancing communication efficiency, making it especially suitable for Internet of Things (IoT) networks supported by uncrewed aerial vehicles (UAVs). However, most existing FL approaches rely on assumptions of uniformly distributed, well-labeled, and large-scale datasets—conditions that rarely hold in practical UAV-based IoT scenarios. These environments typically feature small-scale, nonindependent and identically distributed (non-IID), and dynamically changing data. To address these challenges, we propose a novel self-supervised federated unsupervised learning (FUL) framework tailored for UAV-assisted IoT systems. The proposed framework comprises three key components: 1) a realistic UAV data collection model that considers limited onboard storage and mobility constraints; 2) a robust local training strategy that incorporates self-supervised regularization and a centered kernel alignment (CKA)-based similarity loss to mitigate the effects of data heterogeneity and rapid distribution shifts; and 3) an importance-aware hybrid normalized aggregation method at the global server, which leverages model divergence-based metrics to evaluate local model reliability and integrates both current and historical gradient information for stable model updates. Experimental results demonstrate that our framework achieves classification accuracies of 30.5%, 62.8%, and 70.5% under memory constraints of 500, 1000, and 2000 samples, respectively—outperforming the best baseline by 18.1%, 26.5%, and 9.1% under the same conditions. These results highlight the effectiveness of the proposed FUL framework in handling data heterogeneity and dynamic sample variations inherent in realistic UAV-enabled IoT applications.

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R2D-SLAM: A Real-Time and Robust Visual SLAM Framework for Autonomous Navigation in Dynamic Environments

Sun J., Xutian Y., Shen X., Yin Y., Zhao H., Ohtsuki T., Gui G.

IEEE Transactions on Vehicular Technology 2026年

ISSN  00189545

　概要を見る

Classical Simultaneous Localization and Mapping (SLAM) algorithms, fundamental to autonomous navigation, often assume a static environment, an assumption that fails in many real-world scenarios, resulting in significant trajectory drift. This creates a long-standing trade-off in dynamic SLAM: high-accuracy methods are typically too slow for real-time deployment, while real-time systems often suffer from reduced robustness. To address this challenge, we propose R²D-SLAM, a novel real-time visual SLAM framework that achieves both high accuracy and efficiency in dynamic environments. Our system integrates a lightweight and effective dynamic object detector, Detection Transformer (DETR), with an enhanced matching strategy, termed DEIM_Dyna, into the robust ORB-SLAM3 framework. A dedicated parallel thread performs dynamic region detection concurrently with the main SLAM pipeline, enabling early rejection of contaminated features during tracking. This ensures that pose estimation is conducted on a purified, static feature set, significantly improving robustness and accuracy. Extensive experiments on the challenging TUM and BONN dynamic datasets show that R²D-SLAM achieves new state-of-the-art performance in terms of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE), while maintaining real-time processing speeds exceeding 25 FPS. R²D-SLAM effectively bridges the gap between accuracy and efficiency, offering a practical and robust solution for dynamic scene perception in autonomous vehicles and Unmanned Aerial Vehicles (UAVs).

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Optimal Carbon Emission Reduction Modeling Considering Energy Consumer Satisfaction in Cyber-Physical Energy System

Wang C., Guan X., Wang N., Chen H., Ohtsuki T., Han Z.

IEEE Internet of Things Journal 13 （ 4 ） 6508 - 6521 2026年

　概要を見る

Renewable energy has become a viable alternative to fossil fuels owing to its environmental benefits. However, its inherent uncertainty pose significant challenges. Demand response (DR) mechanisms have been developed to address these issues, facilitating renewable energy integration through consumer-side flexible resources. However, these mechanisms often affect consumer satisfaction, necessitating precise measurement and control of these impacts. In this article, we propose a two-stage electricity trading and load dispatch optimization model aimed at reducing carbon emission by promoting renewable energy accommodation, and the proposed optimization model takes into account multicategory energy consumer satisfaction. We begin by classifying consumers into distinct categories and designing tailored satisfaction functions that reflect their unique power consumption pReferences. The electricity trading and load dispatch processes are formulated as a two-stage optimization problem, which is then transformed into Markov decision processes (MDPs). A model-free framework applying two state-of-the-art deep reinforcement learning (DRL) algorithms is proposed to solve the optimization problem without requiring complex environmental modeling and prior knowledge. Numerical results demonstrate that the proposed framework outperforms benchmark algorithms regarding both consumer satisfaction preservation and carbon emission reduction.

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Energy-Efficient Resource Allocation Scheme Based on Reinforcement Learning in Distributed LoRa Networks †

Ariyoshi R., Li A., Hasegawa M., Ohtsuki T.

Sensors 25 （ 16 ）  2025年08月

　概要を見る

The rapid growth of Long Range (LoRa) devices has led to network congestion, reducing spectrum and energy efficiency. To address this problem, we propose an energy-efficient reinforcement learning method for distributed LoRa networks, enabling each device to independently select appropriate transmission parameters, i.e., channel, transmission power (TP), and bandwidth (BW) based on acknowledgment (ACK) feedback and energy consumption. Our method employs the Upper Confidence Bound (UCB)1-tuned algorithm and incorporates energy metrics into the reward function, achieving lower power consumption and high transmission success rates. Designed to be lightweight for resource-constrained IoT devices, it was implemented on real LoRa hardware and tested in dense network scenarios. Experimental results show that the proposed method outperforms fixed allocation, adaptive data rate low-complexity (ADR-Lite), and (Formula presented.) -greedy methods in both transmission success rate and energy efficiency.

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A Novel Deep Learning Model for Human Skeleton Estimation Using FMCW Radar †

Rantelinggi P.H., Shi X., Bouazizi M., Ohtsuki T.

Sensors 25 （ 13 ）  2025年07月

　概要を見る

Human skeleton estimation using Frequency-Modulated Continuous Wave (FMCW) radar is a promising approach for privacy-preserving motion analysis. However, the existing methods struggle with sparse radar point cloud data, leading to inaccuracies in joint localization. To address this challenge, we propose a novel deep learning framework integrating convolutional neural networks (CNNs), multi-head transformers, and Bi-LSTM networks to enhance spatiotemporal feature representations. Our approach introduces a frame concatenation strategy that improves data quality before processing through the neural network pipeline. Experimental evaluations on the MARS dataset demonstrate that our model outperforms conventional methods by significantly reducing estimation errors, achieving a mean absolute error (MAE) of 1.77 cm and a root mean squared error (RMSE) of 2.92 cm while maintaining computational efficiency.

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Model-Driven Meta-Learning-Aided Fast Beam Prediction in Millimeter-Wave Communications

Lu W., Jiang X., Cao Y., Ohtsuki T., Bai E.

Electronics Switzerland 14 （ 13 ）  2025年07月

　概要を見る

Beamforming plays a key role in improving the spectrum utilization efficiency of multi-antenna systems. However, we observe that (i) conventional beam prediction solutions suffer from high model training overhead and computational latency and thus cannot adapt quickly to changing wireless environments, and (ii) deep-learning-based beamforming may face the risk of catastrophic oblivion in dynamically changing environments, which can significantly degrade system performance. Inspired by the above challenges, we propose a continuous-learning-inspired beam prediction model for fast beamforming adaptation in dynamic downlink millimeter-wave (mmWave) communications. More specifically, we develop a meta-empirical replay (MER)-based beam prediction model. It combines empirical replay and optimization-based meta-learning. This approach optimizes the trade-offs between transmission and interference in dynamic environments, enabling effective fast beamforming adaptation. Finally, the high-performance gains brought by the proposed model in dynamic communication environments are verified through simulations. The simulation results show that our proposed model not only maintains a high-performance memory for old tasks but also adapts quickly to new tasks.

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Heterogeneous multi-agent deep reinforcement learning based low carbon emission task offloading in mobile edge computing

Zhou X., Guan X., Sun D., Zhang X., Zhang Z., Ohtsuki T.

Computer Communications 234 2025年03月

ISSN  01403664

　概要を見る

Mobile edge computing is an emerging computing paradigm in the Internet of Things. Task offloading is a critical method in mobile edge computing to alleviate computational resource constraints. Nowadays, the rising number of tasks is placing greater demands on computing resources. The increasing consumption of computing resources leads to high carbon emission. Achieving environmentally friendly mobile edge computing while effectively managing low-carbon task offloading poses a significant challenge. Recently, deep reinforcement learning has made certain progress in many research fields. However, there are few deep reinforcement learning methods that consider the carbon emission in task offloading. In this paper, we propose a deep reinforcement learning based low carbon emission task offloading algorithm for minimizing carbon emission in mobile edge computing. Firstly, since different base stations exist in the mobile edge computing environment, we consider the mobile edge computing environment with multiple heterogeneous agents. Secondly, to minimize carbon emission, we consider the carbon intensity of the base station as an optimization factor. We conclude the task offloading strategy to minimize carbon emission, consequently achieving the minimization of carbon emission. Moreover, our proposed algorithm allows user devices to decide their own preference for task offloading. Based on the specific requirements and preferences of user devices, our proposed algorithm can dynamically adjust the weights of delay, energy consumption, and carbon emission, respectively. Experiments indicate that the proposed algorithm can accurately and quickly conclude the task offloading strategy to minimize carbon emission.

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Accurate Cardiac Duration Detection for Remote Blood Pressure Estimation Using mm-Wave Doppler Radar †

Wang S., Bouazizi M., Yang S., Ohtsuki T.

Sensors 25 （ 3 ）  2025年02月

　概要を見る

This study introduces a radar-based model for estimating blood pressure (BP) in a touch-free manner. The model accurately detects cardiac activity, allowing for contactless and continuous BP monitoring. Cardiac motions are considered crucial components for estimating blood pressure. Unfortunately, because these movements are extremely subtle and can be readily obscured by breathing and background noise, accurately detecting these motions with a radar system remains challenging. Our approach to radar-based blood pressure monitoring in this research primarily focuses on cardiac feature extraction. Initially, an integrated-spectrum waveform is implemented. The method is derived from the short-time Fourier transform (STFT) and has the ability to capture and maintain minute cardiac activities. The integrated spectrum concentrates on energy changes brought about by short and high-frequency vibrations, in contrast to the pulse-wave signals used in previous works. Hence, the interference caused by respiration, random noise, and heart contractile activity can be effectively eliminated. Additionally, we present two approaches for estimating cardiac characteristics. These methods involve the application of a hidden semi-Markov model (HSMM) and a U-net model to extract features from the integrated spectrum. In our approach, the accuracy of extracted cardiac features is highlighted by the notable decreases in the root mean square error (RMSE) for the estimated interbeat intervals (IBIs), systolic time, and diastolic time, which were reduced by 87.5%, 88.7%, and 73.1%. We reached a comparable prediction accuracy even while our subject was breathing normally, despite previous studies requiring the subject to hold their breath. The diastolic BP (DBP) error of our model is (Formula presented.) mmHg (mean absolute difference ± standard deviation), and the systolic BP (SBP) error is (Formula presented.) mmHg.

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Lightweight CSI-Based Human Activity Recognition for Multitask IoT Applications

Liu C., Miao F., Chen Y., Wu J., Wan H., Tang T., Ohtsuki T., Gui G., Sari H.

IEEE Internet of Things Journal 12 （ 17 ） 35817 - 35827 2025年

　概要を見る

As the global population continues to age and technologies, such as the Internet of Things (IoT) and edge computing advance rapidly, indoor human activity recognition (HAR)-based on Wi-Fi channel state information (CSI) has gained significant research attention. However, the high computational complexity of existing HAR methods limits their deployment on resource-constrained devices. To address this challenge, we propose a lightweight HAR method using branch decision lightweight two-stream convolution-augmented transformer (BLTHAT) model, which integrates depthwise separable convolutions (DSC) and an improved framework structure to enhance computational efficiency. Additionally, we introduce the branch fusion network (BFN), a decision-making module designed to optimize feature processing and improve model robustness. Further enhancements in attention mechanisms and regularization strategies contribute to reducing complexity while maintaining high recognition accuracy. Comprehensive experiments were conducted on a multilabel dataset. The results demonstrate that our proposed HAR method achieves high computational efficiency with minimal complexity, making it well-suited for IoT applications. Ablation studies further confirm that the multibranch structure of the BFN module enhances feature extraction without significantly increasing computational overhead.

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Large Model Empowered Task Offloading for Multi- Access Edge Computing in the Internet of Vehicles

Zhou X., Guan X., Wang N., Chen H., Ohtsuki T., Zhang Y., Han Z.

IEEE Transactions on Vehicular Technology 74 （ 11 ） 18012 - 18025 2025年

ISSN  00189545

　概要を見る

The rapid advancement of the Internet of Vehicles (IoV) has created significant challenges in large-scale data processing and low-latency decision-making. Multi-access edge computing substantially enhances data processing capabilities and system response times by pushing computational resources to the network edge. Task offloading technology transfers vehicle computing tasks to edge servers, alleviating the computational burden on user devices. Traditional offloading methods struggle with the challenges posed by the rapid growth of IoV. With the advancement of deep learning, large models have emerged as powerful tools for handling complex tasks. In this paper, we propose a task offloading algorithm that integrates a Transformer-based large model with deep reinforcement learning. We utilize the Transformer-based large model to replace the action generation network in deep reinforcement learning. The proposed method utilizes extensive historical data and real-time task information to dynamically adjust task allocation schemes. This approach enables the conclusion of task offloading strategies that minimize energy consumption and latency. Additionally, we propose a task classification framework to further reduce offloading delays and energy consumption. The framework categorizes tasks based on key attributes such as computational complexity, data size, and time sensitivity. We propose a reward function based on task adaptability to conclude the task offloading strategy that minimizes both offloading delay and energy consumption. Experimental results demonstrate that the proposed algorithm can accurately and quickly conclude the task offloading strategy to minimize energy consumption and latency.

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Introduction to Activities of AI and Digital Twin WG in XGMF 6G Radio Technology Project

Yamamoto T., Yamazaki T., Otsuki T., Suyama S., Liu L., Osawa N., Yamamoto H., Watanabe T., Kuriyama K., Koshimizu T., Tsukamoto Y., Tomeba H., Nagao T., Chang Y., Morita K., Kitano H.

2025 IEEE Vts Asia Pacific Wireless Communications Symposium Apwcs 2025 2025年

　概要を見る

This paper introduces activities of AI and Digital Twin working group (AI-DT WG) in XGMF 6G Radio Technology Project. Especially in the fiscal year 2024, AI-DT WG has been engaged in activities for getting the beyond 5G white paper "AI/ML and Digital Twin Technologies"ready for the world. The AI-DT WG also launched the discussion for establishing a shared dataset for AI training. This paper provides an overview of the white paper and the status of the discussion for shared dataset establishment.

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GA-CBLN: A Generative Augmented Cascade Broad Learning Network for Efficient Spectrum Prediction with Missing Data Handling

Ji N., Feng Z., Wang S., Zhang Y., Otsuki T., Gui G., Sari H.

2025 IEEE International Symposium on Dynamic Spectrum Access Networks Dyspan 2025 2025年

　概要を見る

With the advancements in wireless communication and the growth of Internet of Things (IoT), spectrum scarcity has become a key bottleneck. Spectrum prediction enhances resource utilization and supports dynamic spectrum access (DSA). However, current models struggle with missing data and prioritize accuracy over efficiency, limiting practical use. To address this, we propose a generative augmented cascade broad learning network (GA-CBLN) for spectrum prediction, combining a deep learning (DL) pre-trained model with an online broad learning (BL) process. First, a DL-based regressor fills missing values by learning spatiotemporal patterns. Next, a generative adversarial network (GAN) generates synthetic samples to enrich the training set, improving adaptability. Finally, the cascade BL network (CBLN) uses the combined data for efficient prediction. Simulations on real datasets show that GA-CBLN significantly outperforms traditional methods in accuracy and efficiency, even with high missing data rates.

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Enhancing Dementia Detection: Augmenting the Face Mesh

Zheng C., Bouazizi M., Ohtsuki T., Kitazawa M., Horigome T., Kishimoto T.

Proceedings International Symposium on Biomedical Imaging 2025年

ISSN  19457928

　概要を見る

Alzheimer's disease (AD) stands as a prevalent form of dementia, primarily impacting the elderly, and presently lacking any known cure. Early screening and intervention have demonstrated efficacy for both current and prospective treatments. With the global population aging, early detection of AD has become imperative. Language-based approaches for dementia detection have received considerable attention in research due to dementia's nature of hurting cognitive and language functions. Facial features, on the other hand, have been comparatively neglected despite their potential in identifying AD, due to a lack of usable dataset. AD patients show different patterns while displaying facial expressions because of Apathy, a phenomenon common in AD patients. This makes facial features non-negligible in dementia detection. The face mesh, comprising 478 landmarks, serves as a rich and informative facial representation. This study concentrates on leveraging face mesh data for AD detection. We explored data augmentation techniques to address the challenges of over-fitting, particularly impactful in training neural networks on a small dataset. Deep learning methods, including transformer, Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, were employed to classify dementia patients and healthy subjects. By augmenting the dataset by rotation, shift, and scaling, we improved the performance of the deep learning methods. The combined use of 1D CNN networks and data augmentation method reported the highest accuracy of 76%. These results confirmed the effectiveness of our data augmentation methods. They also underscore the potential of face-related features in enhancing early detection approaches for AD.

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Enhancing Cross-Domain Robustness in Wi-Fi-Based Human Activity Recognition via Attention-Driven Deep Learning

Miao F., Takyu O., Shan L., Ohtsuki T., Gui G.

IEEE Vehicular Technology Conference 2025年

ISSN  10903038

　概要を見る

Wi-Fi-based Human Activity Recognition (HAR) using Channel State Information (CSI) has received significant attention due to its non-intrusive nature and wide applicability in smart cities, healthcare, and smart home systems. Despite its potential, real-world deployment faces a major challenge: ensuring the model's ability to generalize across different users, who exhibit distinct behaviors and encounter diverse environmental factors. This issue, termed Cross-User Generalization (CUG), arises from distribution shifts caused by variations in users' body shapes, postures, movements, and environmental conditions. To address this challenge, we propose a cross-domain generalization framework based on an Attention-based Bidirectional Long Short-Term Memory (ABLSTM) network. The proposed framework enhances the generalization capabilities of HAR models by leveraging multi-source training and incorporating attention mechanisms for effective temporal feature learning. Specifically, the model learns user-invariant features across diverse training users and adapts to new users without requiring direct exposure to their data. Extensive experiments on a multi-user CSI dataset demonstrate that the ABLSTM model improves average test accuracy by 2.0%-6.7% compared to BiLSTM and GRU models, and over 18.0% compared to MLP-based models, while achieving the lowest training and testing loss. This methodology offers a robust and scalable solution for Wi-Fi-based HAR, promoting reliable deployment in complex, multi-user environments encountered in smart cities, healthcare monitoring, and security surveillance.

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Energy Efficient Transmission Parameters Selection Method Using Reinforcement Learning in Distributed LoRa Networks

Airiyoshi R., Hasegawa M., Ohtsuki T., Li A.

IEEE Wireless Communications and Networking Conference Wcnc 2025年

ISSN  15253511

　概要を見る

With the increase in demand for Internet of Things (IoT) applications, the number of IoT devices has drastically grown, making spectrum resources seriously insufficient. Transmission collisions and retransmissions increase power consumption. Therefore, even in long-range (LoRa) networks, selecting appropriate transmission parameters, such as channel and transmission power, is essential to improve energy efficiency. However, due to the limited computational ability and memory, traditional transmission parameter selection methods for LoRa networks are challenging to implement on LoRa devices. To solve this problem, a distributed reinforcement learning-based channel and transmission power selection method is proposed, which can be implemented on the LoRa devices to improve energy efficiency in this paper. Specifically, the channel and transmission power selection problem in LoRa networks is first mapped to the multi-armed-bandit (MAB) problem. Then, an MAB-based method is introduced to solve the formulated transmission parameter selection problem based on the acknowledgment (ACK) packet and the power consumption for data transmission of the LoRa device. The performance of the proposed method is evaluated by the constructed actual LoRa network. Experimental results show that the proposed method performs better than fixed assignment, adaptive data rate low-complexity (ADR-Lite), and e-greedy-based methods in terms of both transmission success rate and energy efficiency.

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Efficient WiFi Device Recognition via Blueprint Separable Residual Network with SE Module

Liu T., Cai Z., Wang Q., Ohtsuki T., Sari H., Gui G.

IEEE Vehicular Technology Conference 2025年

ISSN  10903038

　概要を見る

With the rapid advancement of wireless communication technologies, WiFi signals have become essential for modern connectivity across diverse applications. However, their widespread deployment introduces significant security vulnerabilities, including unauthorized access, data leakage, and interference. Accurate identification of WiFi transmitters is crucial for mitigating these threats. While existing methods perform well in ideal conditions, their effectiveness degrades in real-world scenarios, particularly in environments with low signal-to-noise ratios (SNRs). To address this limitation, we propose a novel transmitter identification framework that integrates blueprint separable convolution (BSC) and a squeeze-and-excitation (SE) module. The BSC extracts critical features efficiently, while the SE module dynamically enhances feature representations. Simulation results demonstrate that the proposed approach achieves competitive or superior accuracy compared to state-of-the-art models. Moreover, the framework exhibits strong robustness, maintaining high recognition performance even in challenging transmission conditions with low SNRs.

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Efficient and Effective Fine-tuning Method for Depression Detection from Conversation

Ikeuchi K., Kishimoto T., Nakai F., Horigome T., Kitazawa M., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2025年

ISSN  1557170X

　概要を見る

Depression has a significant impact on society. Early detection of depression is essential because timely treatment can mitigate its negative impact. Recent studies have reported that models based on Large Language Models (LLMs), such as Chat GPT-4, can achieve high accuracy in detecting depression when prompt-engineering techniques are applied. In contrast, fine-tuning methods require more memory for training and have yet to demonstrate comparable accuracy to prompting-engineering in detecting depression from conversations. The objective of this study is to achieve higher accuracy than prompt-engineered models with low GPU memory through fine-tuning. To accomplish this, we first quantized the parameters of the LLM to minimize memory requirements during training. Instead of adding a classification layer, we retained the original structure of the LLM and trained it as a generative model. Our evaluation on the DAIC-WOZ dataset demonstrated an F1 score of 84% for depression detection with low GPU memory, which is superior to the performance of prompt-engineered models. Additionally, when tested on the private PROMPT dataset, the proposed model achieved an F1 score of 82%. This result suggests that the model's strong performance is not solely dependent on publicly available data that may have been included in the pre-training.

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Distributed Gossip-GAN for Low-Overhead CSI Feedback Training in FDD mMIMO-OFDM Systems

Cao Y., Liu G., Ohtsuki T., Yang H.H., Quek T.Q.S.

IEEE Internet of Things Journal 12 （ 19 ） 40176 - 40189 2025年

　概要を見る

The deep autoencoder (DAE) framework has turned out to be efficient in reducing the channel state information (CSI) feedback overhead in massive Multiple-Input Multiple-Output (mMIMO) systems. However, these DAE approaches presented in prior works rely heavily on large-scale data collected through the base station (BS) for model training, thus rendering excessive bandwidth usage and data privacy issues, particularly for mMIMO systems. When considering users’ mobility and encountering new channel environments, the existing CSI feedback models may often need to be retrained. Returning back to previous environments, however, will make these models perform poorly and face the risk of catastrophic forgetting. To solve the above challenging problems, we propose a novel gossiping generative adversarial network (Gossip-GAN)-aided CSI feedback training framework. Notably, Gossip-GAN enables the CSI feedback training with low-overhead while preserving users’ privacy. Specially, each user collects a small amount of data to train a GAN model. Meanwhile, a fully distributed gossip learning strategy is exploited to avoid model overfitting, and to accelerate the model training as well. Simulation results demonstrate that Gossip-GAN can: 1) achieve a similar CSI feedback accuracy as centralized training with real-world datasets; 2) address catastrophic forgetting challenges in mobile scenarios, and 3) greatly reduce the uplink bandwidth usage. Besides, our results show that the proposed approach possesses an inherent robustness.

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DFusion-SLAM: A Lightweight Semantic Fusion Framework for Robust Visual SLAM in Dynamic Environments

Sun J., Huang H., Shen X., Zhao H., Yin Y., Ohtsuki T., Gui G.

IEEE Internet of Things Journal 12 （ 24 ） 55682 - 55692 2025年

　概要を見る

In dynamic and cluttered environments, traditional simultaneous localization and mapping (SLAM) systems often suffer from degraded localization accuracy and unstable map construction due to the presence of moving objects and occlusions. To address these challenges, we propose DFusion-SLAM, a lightweight and robust SLAM framework that integrates an enhanced object detection module into ORB-SLAM3. The detection module is based on an improved D-Fine architecture, in which the original Transformer is replaced with a more efficient PolaLinearAttention mechanism. Furthermore, a MetaFormer-based semantic fusion structure is introduced to strengthen multiscale feature representation. These architectural improvements jointly enhance detection accuracy while reducing model complexity, achieving a performance increase from 42.8% to 43.7% mean average precision (mAP). Experimental evaluations on dynamic RGB-D sequences from the TUM and Bonn datasets demonstrate that DFusion-SLAM significantly improves localization accuracy and mapping stability under dynamic conditions, while maintaining high computational efficiency. These results highlight the framework's strong potential for real-time deployment in IoT-oriented mobile and robotic platforms operating in complex environments.

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Deep Learning-Based Human Joint Localization Using mmWave Radar and Sequential Frame Fusion

Rantelinggi P.H., Shi X., Bouazizi M., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2025年

ISSN  1557170X

　概要を見る

Unlike conventional techniques relying typically on RGB and depth cameras, millimeter wave (mmWave) radars, especially Frequency-Modulated Continuous Wave (FMCW) radar, have significant advantages in human monitoring. These advantages are more pronounced when detecting human joint positions, as FMCW radars rely solely on collected point cloud data. Compared with camera-based systems, this technology is efficient because it can operate in low-light conditions, and does not present a privacy threat. However, conventional approaches employing FMCW radar, such as voxel-based gathering techniques, still have limitations in optimally capturing spatiotemporal features, making it difficult to detect human joint points with high accuracy. To overcome this obstacle, we propose a novel framework that introduces sequential concatenation of multiple frames in the preprocessing stage to maintain spatiotemporal continuity, thereby improving feature representation. Furthermore, we design a deep learning architecture consisting of Convolutional Neural Network (CNN) for spatial feature extraction, Transformer for capturing long-term dependencies, and Bidirectional Long Short-Term Memory (Bi-LSTM) for temporal sequence modeling. The proposed approach makes optimal use of spatiotemporal data, improves the accuracy and reliability in detecting human joint positions, and accelerates model convergence. Through experimental validation, this method shows a Mean Absolute Error (MAE) of 1.77 cm and a Root Mean Squared Error (RMSE) of 2.92 cm between the actual human joint positions and the predicted ones. This reaffirms the effectiveness of the model in estimating human joint positions.

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Correlation Analysis of Objective Features and Online Meeting Quality

Oba R., Zheng C., Akahori W., Yamada M., Yokoyama M., Ishii M., Ohtsuki T.

Lecture Notes in Computer Science 15805 LNCS   172 - 183 2025年

ISSN  03029743

　概要を見る

Meetings are vital for team activities, supporting both decision-making and information sharing, making their quality a critical factor in boosting productivity and reducing costs. Therefore, establishing objective methods for measuring meeting quality is essential to identify specific issues and verify the effectiveness of solutions. However, no well-defined objective features have yet been identified to accurately assess meeting quality. In this study, we explored features that can be extracted from meeting video and audio data to objectively assess the quality of meetings. We conducted online meetings and extracted features from video data, including Action Units, Mel-Frequency Cepstral Coefficients (MFCC) and utterances. We then examined the correlations between these features and participant responses regarding psychological safety and perceived meeting effectiveness. As a result, we found that certain features such as the number of utterances and the Action Units such as Lip Part and Dimpler showed significant correlations with psychological safety and perceived meeting effectiveness.

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Channel-Robust Few-Shot Specific Emitter Identification Using Meta-Feature Augmentation

Fu X., Meneghello F., Wang Y., Ohtsuki T., Yuen C., Gui G., Sari H.

IEEE Wireless Communications and Networking Conference Wcnc 2025年

ISSN  15253511

　概要を見る

The rapid increase in wireless devices has raised significant security and privacy concerns, positioning Specific Emitter Identification (SEI) as a crucial physical-layer security technology. While Deep Learning (DL) has been widely applied to SEI, it often requires large amounts of high-quality signal examples, which are laborious and expensive to obtain. Moreover, the DL-enabled SEI models have difficulties in extracting features from the signal examples in the testing process that are consistent with those from the signal examples in the training phase due to the wireless channel variations, further resulting in a significant reduction in identification performance. To address these challenges, we propose a channel-robust Few-Shot SEI (FS-SEI) method based on Meta-Feature Augmentation (MFA). Our approach utilizes datasets from base emitters to construct a meta-feature embedding function that can extract generalizable features from a few signal examples of target emitters. We then calculate and calibrate the statistics of these extracted features to describe the feature distribution of target emitters. A Multi-Layer Perceptron (MLP) is subsequently trained on both original and augmented features derived from this distribution, achieving a robust FS-SEI model. Experiments conducted on a Wi-Fi dataset comprising 16 emitter categories - 10 as base emitters and 6 as target emitters - demonstrate that our method achieves 93.75% identification accuracy with only 5 examples per target emitter, maintaining 92.56% accuracy even under varying wireless channel conditions. Code is available at https://github.com/lovelymimola/MFA-based-FS-SEI.

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Beamforming and Trajectory Planning Method Under Fixed-Footprint Conditions for Multi-HAPS Systems

Mori T., Ohtsuki T., Pan M., Han Z.

IEEE International Conference on Communications    2236 - 2241 2025年

ISSN  15503607

　概要を見る

High Altitude Platform Station (HAPS) is a new airborne communications platform that provides wide-area communications services from the stratosphere and has the potential for coverage extension in 6G networks. In particular, it is a practical scenario where multiple HAPS work together to provide communications in densely populated urban areas and extensive regions. However, changes in ground coverage due to HAPS movement cause frequent handovers, which pose challenges to communication quality and stability. To solve this problem, 'footprint fixation,' which maintains constant ground coverage even when the HAPS moves, is expected to reduce handovers and improve communication quality. How we should design beamforming and trajectory under fixed footprint conditions has not been clarified. In this paper, we propose a beamforming and trajectory planning method under fixed-footprint conditions for multiple HAPS systems. For footprint fixation, beamforming dynamically adapts to HAPS motion to eliminate ground coverage shifts, reduce handover frequency, and improve communication stability. Under fixed-footprint conditions, trajectory planning aims to improve the throughput of UEs with low (5th percentile) and medium (50th percentile) communication quality simultaneously through sequential multi-objective optimization. Simulation results in major Japanese cities with different UE distributions show that the proposed method improves both low and medium communication quality UEs, achieving a 20 - 36% improvement in 5th percentile throughput and an 8-20% improvement in 50th percentile throughput compared to other methods. In addition, the footprint fixation increases coverage stability, reducing outage probability to less than 2% and significantly reducing handover frequency.

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A Plug-and-Play Module for Enhancing Fault-Tolerant Distributed Inference Based on Gaussian Dropout

Hou Z., Ohtsuki T.

IEEE International Conference on Communications    4553 - 4558 2025年

ISSN  15503607

　概要を見る

Deploying deep neural network (DNN) models to the Internet of Things (IoT) for distributed inference (DI) is becoming an increasingly common requirement for AI applications. When IoT transmissions occur over lossy networks, data loss can significantly affect the accuracy of inference tasks. Therefore, we propose a plug-and-play module called Loss-Adapter, which can improve the accuracy of DI on lossy networks. To simulate network packet loss, we design a Gaussian distribution sampling dropout. This idea is utilized during the training of Loss-Adapter to acquire fault-tolerant inference capability on unreliable communication links. Moreover, we use the Tree-structured Parzen Estimator algorithm to optimize the parameters of the Gaussian dropout layer. To avoiding catastrophic forgetting, we implement freeze training. In addition, as a plug-and-play module, it is very easy to train and use. Our experiments show that by applying the Loss-Adapter, the inference accuracy of DNN models on lossy IoT is significantly improved.

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A Novel Physical Spoofing Technique Using Radio Frequency Fingerprint Emulation and Model Fitting

Yao Z., Wang Y., Gui G., Otsuki T., Mao S., Wang X., Sari H.

IEEE International Conference on Communications    2629 - 2634 2025年

ISSN  15503607

　概要を見る

With the increasing demand for secure communication in 5G and beyond, authentication of wireless devices has become a crucial task for communication security. Radio frequency fingerprint identification (RFFI) leverages the hardware-specific features in radio frequency (RF) signals, known as radio frequency fingerprints (RFF), to achieve highprecision device identification. However, the dependence of RFFI on the physical characteristics of devices makes it vulnerable to physical spoofing attacks. This paper proposes an innovative physical spoofing attack framework that combines spoofed transmitter and legitimate transmitter models. It performs RFF modeling, RFF concealment (RFFC), and RFF spoofing (RFFS) sequentially to achieve precise spoofing of the original baseband signal. We validate the effectiveness of the proposed physical spoofing mechanism through simulations of seven types of transmitters using MATLAB Simulink. The performance is further evaluated on an RFFI model based on complexvalued convolutional neural networks (CVCNN). Experimental results show that neural networks (NN) significantly outperform the generalized memory polynomial (GMP) model in nonlinear data fitting and temporal relationship modeling. Consequently, NN-based physical spoofing methods exhibit superior attack effectiveness. Specifically, under the signal-to-noise ratio (SNR) of 15 dB, the NN-based physical spoofing method achieves a target attack success rate (TSR) as high as 98%, which is superior to adversarial attack methods. NN-based methods also enhanced performance in terms of stealthiness metrics.

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A Novel MIMO FMCW Radar-Based Approach for Heart Rate Estimation Using Positional Feature Selection

Nakatani S., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications    916 - 921 2025年

ISSN  15503607

　概要を見る

This paper proposes a heart rate estimation method using Variational Mode Decomposition (VMD) employing Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar. The proposed method first estimates the human position within the radar's coverage area, then reduces noise by focusing on the signal from these positions. The signal is decomposed into Intrinsic Mode Function (IMF) signals using VMD, and only the IMF signal related to the heartbeat is retained. Heart rate signal is reconstructed by weighting IMF signals based on their energy within the specific spatial area in which the human is located. The reconstructed signals are utilized for heart rate estimation through peak detection. The estimation is done over consecutive time windows. Until the fourth time window, among the estimated heart rates from the different cells, the selection is based on energy and periodicity. From the fifth time window onwards, the heart rate closest to the average of the previous four estimates is chosen. This approach considers the gradual transition of heart rate estimation over time to mitigate extraneous variations. Validation experiments involved 4 subjects being seated and stationary within the radar coverage area, with heart rate observed via MIMO FMCW radar. Results showed an average Mean Absolute Error (MAE) of 2.54 BPM, with an exclusion rate of 2.12%.

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A Deep Transfer Learning-Based Low-Overhead Beam Prediction in Vehicle Communications

Xiao Z., Cao Y., Bouazizi M., Ohtsuki T., Mumtaz S.

IEEE Wireless Communications Letters 14 （ 12 ） 4147 - 4151 2025年

ISSN  21622337

　概要を見る

Existing transfer learning-based beam prediction approaches primarily rely on simple fine-tuning. When there is a significant difference in data distribution between the target domain and the source domain, simple fine-tuning limits the model’s performance in the target domain. To tackle this problem, we propose a transfer learning-based beam prediction method that combines fine-tuning with domain adaptation. We integrate a domain classifier into fine-tuning the pre-trained model. The model extracts domain-invariant features in adversarial training with domain classifier, which can enhance model performance in the target domain. Simulation results demonstrate that the proposed transfer learning-based beam prediction method achieves better achievable rate performance than the pure fine-tuning method in the target domain, and close to those when the training is done from scratch on the target domain.

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Malware Traffic Classification via Expandable Class Incremental Learning With Architecture Search

Zhang X., Wang Y., Ohtsuki T., Gui G., Yuen C., Di Renzo M., Sari H.

IEEE Transactions on Information Forensics and Security 20   6074 - 6085 2025年

ISSN  15566013

　概要を見る

Malware traffic classification (MTC) is a crucial step in network intrusion detection, which is significant for network security and management. With the continuous evolution of malware traffic, traditional MTC methods are difficult to adapt efficiently to new traffic categories, and manually designed neural network structures suffer from performance bottlenecks and low design efficiency. Hence, we propose an enhanced MTC method based on expandable class incremental learning (CIL) with architecture search. The architecture search can automatically design the optimal neural network structure tailored to different network traffic characteristics, avoiding the limitations of manually designing network structures and improving classification performance. Meanwhile, expandable CIL allows the MTC model to gradually learn new traffic categories without forgetting previous knowledge, avoiding the computational overhead and efficiency loss caused by frequent retraining of the model. The experimental results demonstrate that the proposed CIL-MTC approach surpasses advanced incremental learning methods on both the Edge-IIoTset and ISCX VPN-nonVPN datasets, achieving superior classification performance while maintaining lower average trainable parameters and training costs. Especially, it achieves an average incremental accuracy of 98.55% and 99.09% on the Edge-IIoTset dataset with incremental tasks of 5 and 2, respectively.

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Model-Based DRL for Dynamic Antenna Beamforming in HAPS Communication System

Yang S., Bouazizi M., Ohtsuki T.

2025 International Symposium on Antennas and Propagation Isap 2025 2025年

　概要を見る

High Altitude Platform Station (HAPS) systems require dynamic beamforming to handle platform mobility and uneven user distributions. However, traditional reinforcement learning (RL) and evolutionary algorithms run the risk of degrading throughput during exploration. We propose a model-based deep RL (DRL) framework that pre-trains an environment model to map antenna parameters and HAPS states to SINR distributions. This simulated feedback allows efficient parameter exploration without reducing average throughput below its initial level. Simulation results show that our method consistently maintains higher throughput than conventional RL approaches and swiftly adapts to network changes.

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User Clustering and Trajectory Optimization for UAV-Assisted Wireless Sensor Network

Zhang X., Yin Y., Ohtsuki T.

2025 30th Asia Pacific Conference on Communications Apcc 2025 2025年

　概要を見る

With the advances in flexibility, wide coverage, and cost-effectiveness, integrating unmanned aerial vehicles (UAVs) with wireless sensor networks (WSNs) has been regarded as a promising technology in data collection and transmission within complex environments. Due to the limited battery capacity of sensor nodes (SNs) and UAVs, UAV-assisted WSNs face challenges in achieving energy efficiency and optimizing resource allocation. To reduce the total energy consumption of UAV-assisted WSNs, we design a multifactor fitness function which includes the distance between SNs, energy levels of SNs, and UAV trajectory length. Based on that, we propose a balanced clustering and cluster head (CH) selection strategy. As for UAV trajectory design, we employ the branch and bound method according to the selection of CHs. Simulation results show that the proposed algorithm reduces the average energy consumption of all SNs and achieves short UAV trajectory length compared to Low-Energy Adaptive Clustering Hierarchy (LEACH), Hybrid Energy-Efficient Distributed clustering (HEED), Particle Swarm Optimization (PSO), and Stable Election Protocol (SEP).

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Towards Robust and Real-Time State Estimation: A Neural Kalman Filter Framework with Mamba Architecture

Sun J., Shen J., Tong J., Tang T., Yin Y., Ohtsuki T., Gui G.

IEEE Transactions on Vehicular Technology 2025年

ISSN  00189545

　概要を見る

The rapid advancement of intelligent perception systems and autonomous navigation technologies imposes increasingly stringent requirements on state estimation algorithms regarding modeling capacity, robustness, and real-time performance. While traditional Kalman Filter (KF) demonstrates computational efficiency and mathematical interpretability for linear Gaussian systems, their effectiveness diminishes in nonlinear, non-Gaussian, and non-stationary dynamic scenarios. Recent developments in neural state space modeling offer new perspectives for recursive estimation, yet existing approaches suffer from structural complexity, training difficulties, and inadequate real-time capability. This paper presents Mamba-KF, a novel state estimation framework integrating deep modeling with recursive filtering. Leveraging the cutting-edge Mamba state space neural network, our method dynamically generates state transition parameters while establishing a differentiable covariance propagation pathway, enabling end-to-end optimization. We further design a lightweight multi-modal fusion architecture tailored for high-frequency sensory data processing in real-time applications. Evaluated on the EuRoC Micro Aerial Vehicle (MAV) dataset, Mamba-KF attains the lowest errors across states, reducing position Root Mean Square Error (RMSE) by up to 33.3% relative to Extended Kalman Filter (EKF) and 24.1% relative to Unscented Kalman Filter (UKF) at 10 dB Signal-to-Noise Ratio (SNR), with an average velocity RMSE of 0.097 m/s and an orientation error of 2.3°. It also achieves superior consistency, with a mean Normalized Estimation Error Squared (NEES) of 6.02 and 93.7% of estimates within the χ²-based 95% confidence interval. Under robustness evaluations, Mamba-KF yields the lowest RMSEs and suppresses divergence observed in EKF. Computationally, it maintains near real-time performance with 0.65 ms per step, offering a favorable accuracy-efficiency trade-off for visual-inertial navigation and autonomous systems, while providing significant theoretical insights and promising engineering applications.

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Stress Detection Using Eye Activity via Recurrent Neural Networks

Bouazizi M., Feghoul R., Feghoul K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2025年

ISSN  1557170X

　概要を見る

Chronic stress has become a significant issue in modern society, negatively affecting individuals' overall well-being and potentially leading to severe physiological and psychological disorders. Most existing methods for automatic stress detection rely on physiological data, which requires direct skin contact with sensors. Not only does this make them intrusive, but it also limits their practicality for everyday use due to accuracy constraints. In this paper, we propose a novel approach using eye data captured by a standard video camera -specifically, eye gaze direction and eye landmarks- for stress detection. We use two types of Recurrent Neural Networks (RNNs), namely a Long Short-Term Memory (LSTM) network and a Gated Recurrent Unit (GRU) network. Through experiments on the UBFC-Phys dataset, we show that our method achieves a stress detection accuracy of up to 83.69% using gaze data, while eye landmarks further improve accuracy to 90.12%. Combined, these two modalities reach accuracy of 90.83%. These results highlight the effectiveness of our approach in enabling accurate and non-intrusive stress detection.

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Stress Detection Through Eye Tracking Incorporating Custom Lasso Stress Index

Meng X., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications    3174 - 3179 2025年

ISSN  15503607

　概要を見る

Stress has become an increasingly common issue, significantly impacting individuals' health and productivity. Traditional methods for assessing stress levels often rely on physiological signals, such as heart rate and skin conductance. However, these methods can be impractical for continuous, non-invasive monitoring. To address these limitations, we propose a machine learning approach for stress assessment using eye-tracking data. We collected eye-tracking and Electrocardiogram (ECG) data under different stress conditions. From this dataset, we extracted relevant features and conducted preliminary analysis. We further proposed a novel stress index, the Least Absolute Shrinkage and Selection Operator Stress Index (Lasso Stress Index), specifically tailored for stress detection in our dataset. In previous studies, researchers commonly used predefined subjective stress index or Baevsky's Stress Index (BSI) for stress classification. We compared these methods and found that our proposed Lasso Stress Index yielded the highest accuracy, achieving up to 90.6% in stress classification.

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Source Channel Model Selection for Downlink CSI Feedback Estimation using Transfer Learning in Massive MIMO System

Jingyu L., Bouazizi M., Ohtsuki T.

2025 International Conference on Computing Networking and Communications Icnc 2025    276 - 281 2025年

　概要を見る

Transfer learning-based methods for Channel State Information (CSI) feedback can achieve a good CSI reconstruction performance with small amounts of computation time and cost. However, for higher efficacy, such methods need to fine-tune a properly selected source channel model each time. This is because fine-tuning a model trained on drastically different data will suffer from slow convergence and non-optimal performance, which defies the purpose of transfer-learning. In this paper, we use a one-stage algorithm utilizing k-nearest neighbors (k-NN) and Deep Neural Network (DNN) methods to classify the downlink Clustered Delay Line (CDL) channels into one of 5 channel models with the help of a selected set of features. Based on the classification results, the appropriate source channel model for fine-tuning is selected, and the model is fine-tuned on the received data. To demonstrate the usefulness of our method, we compare its results to those of existing methods. Through simulation, we show that the overall classification accuracy for 5 CDL channel models using the one-step algorithm is 80.73%. Upon classification, and by selecting the proper source model, our method can reach a better normalized mean square error (NMSE) of CSI feedback performance in a shorter time and with fewer samples.

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Semantic Communication in Vehicular Networks: A Multi-Modal Approach for Faithful Image Transmission

Bouazizi M., Nagase R., Yin Y., Yang S., Ohtsuki T.

IEEE Vehicular Technology Conference 2025年

ISSN  15502252

　概要を見る

Semantic communication (SC) has emerged as a promising paradigm to address the bandwidth limitations of traditional wireless communication systems by transmitting only the essential meaning of data. This paper investigates an advanced SC framework for vehicular communication, employing diverse feature extraction techniques to encode multi-modal information, such as textual descriptions, object poses, semantic segmentation, and sketches, into compact semantic representations. These semantic encoders are evaluated based on their output size, faithfulness of reconstruction, and resilience to data loss. The proposed system model considers vehicular communication scenarios where vehicles transmit important information extracted from camera-collected data to other vehicles and road users, in bandwidth-constrained environments. Simulation results show the effectiveness of the proposed SC framework, with a reconstruction performance reaching 17.24 in Fréchet Inception distance (FID) and a an RMSE equal to 0.029 between the transmitted image and the reconstructed one. This performance is achieved while the data saving between the size of the original image and the transmitted semantics is equal to 92.25%.

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Remote RRI Estimation: A Signal Reconstruction Approach with Temporal Modeling

Wang S., Bouazizi M., Ohtsuki T.

2025 30th Asia Pacific Conference on Communications Apcc 2025 2025年

　概要を見る

We propose a neural network-based framework for radar-based heart rate monitoring and inter-beat interval (IBI) estimation that reconstructs the underlying cardiac waveform, rather than relying on Conventional peak detection methods. Conventional approaches typically estimate IBI by detecting peaks in radar signals after extensive signal processing, which can be sensitive to noise and transient signal degradation. Building on our previous work, which utilized a U-Net architecture with cross-channel attention, we introduce a enhanced model by incorporating Bidirectional Long Short-Term Memory (BiLSTM) layers and temporal self-attention into the bottleneck of the U-Net. This modification allows the network to explicitly model temporal dependencies and heartbeat periodicity, improving resilience to transient input degradation and physiological variability. The results are evaluated on a public radar-Electrocardiogram(ECG) dataset under Valsalva, Tilt Up, and Tilt Down conditions, our model achieves an average IBI estimation error of 22.54 ms Root Mean Square Error(RMSE), which outperforming both conventional peak-based methods and our prior U-Net-based design. These results demonstrate improved temporal generalization and signal stability, advancing the feasibility of radar-based cardiac monitoring in clinical and home settings.

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Reinforcement Learning With Clustering Optimization for Antenna Parameter Adjustment in HAPS Networks

Bouazizi M., Yang S., Li Z., Yin Y., Ohtsuki T.

IEEE Access 13   172062 - 172079 2025年

　概要を見る

High-Altitude Platform Stations (HAPS) promise to extend broadband coverage over wide areas by acting as quasi-stationary aerial base stations. However, wind-induced movements cause antenna misalignment and unbalanced user distributions, leading to throughput degradation. This work presents a low-complexity, equal clustering (EC) -aided deep Q-network (DQN) algorithm for dynamic antenna control in HAPS networks. The proposed EC algorithm partitions users into balanced clusters by identifying low-density sectors in a single pass, avoiding the iterative refinements of other clustering algorithms such as K-Means. A DQN then fine-tunes the antenna tilt angles and beam widths based on the EC results to maximize the throughput of low-throughput users. The proposed method’s performance is evaluated through simulations under rotation and shift scenarios and benchmarked against three Reinforcement Learning (RL)-based approaches and a K-Means clustering baseline. Compared with these methods, the EC-aided DQN delivers higher throughput, especially for users in the low-throughput tail, while reducing computational complexity. For instance, the percentage of low-throughput users was reduced by over 10% in the case of 5 km shift in the regions of Osaka and Sendai, compared to K-means. Similarly, in the case of 30° rotation, the percentage is reduced by 4.1% and 6.5% for the same regions. Statistical significance tests confirm that the improvements are not due to random simulation noise.

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Nullforming Strategy Based on User Distribution for Spectrum Sharing Between High-Altitude Platforms and Terrestrial Networks

Fontaine K., Kalør A.E., Ohtsuki T., Ishikawa T.

IEEE Vehicular Technology Conference 2025年

ISSN  10903038

　概要を見る

High-Altitude Platform Stations (HAPSs) enable wide-area coverage in 6G networks but introduce interference when sharing spectrum with terrestrial networks (TNs). Null-forming is a technique to mitigate this interference by directing low-power beams (nulls) toward terrestrial users. Traditional nullforming methods, such as Null-Sweeping, rely on changing null directions across the resource blocks (RB) to improve the impact of nullforming. Yet these null directions are predefined for uniform user distributions and may not fully account for nonuniform deployments. We propose a user-aware nullforming approach that leverages K-means clustering to adapt null positions to dense user regions in the terrestrial cells, while time-frequency resources are allocated proportionally to user density. Simulations show that our method reduces HAPS interference for terrestrial users and improves fairness in interference distribution.

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Non-contact Heart Rate Estimation Using VMD with Spectrogram-Based Preprocessing via CW Radar

Wang X., Bouazizi M., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2025年

ISSN  1557170X

　概要を見る

Vital signs such as heart rate are important indicators for assessing human health. In particular, noninvasive heart rate measurement using non-contact devices such as continuous wave (CW) radar has attracted attention as a promising technique for estimating heart rate by detecting small cardiovascular movements. However, existing estimation methods have difficulty in accurately estimating heart rate when strongly affected by noise such as respiration and body movement. In this research, we propose a heart rate estimation method with high robustness against noise by combining a pre-processing method using spectrogram and signal decomposition based on Variational Mode Decomposition (VMD). In the proposed method, preprocessing based on the spectrogram is first performed before applying VMD. The obtained integrated spectrum containing the heartbeat-related components is then processed using VMD, which decomposes it into multiple intrinsic mode function (IMF) signals. To select the IMF signals containing heartbeat components, the autocorrelation function and the frequency variation characteristic of heartbeat signals are used as criteria for selection. To evaluate the characteristics of the proposed method, measurements were performed on 10 subjects in a sitting position while electrocardiogram (ECG) was simultaneously recorded as a reference signal. Experimental results showed that the proposed method achieved a low mean absolute error (MAE) of 5.46 bpm compared to the conventional methods, effectively suppressing the effect of noise and improving the accuracy of heart rate estimation.

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NoisyNet-DDQN-Based Sequential Handoff Algorithm for UAV Networks With End-to-End Network Slicing

Yang F., Li M., Ohtsuki T., Wu W., Si P., Sun T.

IEEE Transactions on Vehicular Technology 74 （ 12 ） 19496 - 19512 2025年

ISSN  00189545

　概要を見る

End-to-end network slicing in uncrewed aerial vehicle (UAV) networks has attracted significant attention for delivering differentiated services tailored to the quality of service (QoS) requirements of user terminals (UTs). In a sliced UAV network, the dual mobility of both UTs and UAVs, combined with the three-layer association of UT-UAV-network slice (NS), makes handoff a critical and challenging problem for maintaining the communication performance of UTs. This paper proposes a sequential handoff process where a central controller makes handoff decisions for each UT sequentially according to the UT queue. This process is modeled as a Markov decision process (MDP). In the MDP, the state space includes NS available resources, UTs' QoS requirements, and UT queue information. Additionally, the reward function accounts for UTs' QoS and handoff overhead. The double deep Q-network (DDQN) technique is then employed to design a handoff algorithm. This algorithm achieves the optimization goal of ensuring UTs' QoS requirements while avoiding frequent handoffs by maximizing the total reward of the MDP. Additionally, a state-dependent exploration technique with adaptive intensity adjustment, called noisy network (NoisyNet), is utilized to drive the DDQN for effective exploration, thereby enhancing the performance of the handoff algorithm. Finally, the runtime complexity of the proposed NoisyNet-DDQN-based sequential handoff (NoisyNet-DDQN-SH) algorithm is analyzed. The simulation results show that the NoisyNet-DDQN-SH algorithm converges well. Moreover, it exhibits strong robustness. Additionally, the NoisyNet technique effectively enhances the algorithm's performance, making the NoisyNet-DDQN-SH algorithm superior to the vanilla DDQN (with an \varepsilon-greedy strategy), greedy, maximum signal-to-interference-plus-noise ratio (Max-SINR), and random algorithms.

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Multi-Dimensional Spectrum Prediction Method Based on Efficient Adaptive Broad Learning

Ji N., Wang S., Zhang Y., Peng Y., Otsuki T., Niyato D., Gui G.

IEEE Wireless Communications and Networking Conference Wcnc 2025年

ISSN  15253511

　概要を見る

Multi-dimensional spectrum prediction is essential for spectrum sharing and dynamic spectrum access (DSA), tack-ling spectrum scarcity and improving wireless communication. Traditional methods often use machine learning (ML), which requires manual feature extraction, or deep learning (DL), which demands high computational resources. This paper proposes a lightweight multi-dimensional spectrum prediction model using an adaptive broad learning network (ABLN). The model employs a sliding window to preprocess data and establishes input layers using randomly generated feature and enhancement nodes. The weights of broad learning are determined by solving the pseudo-inverse, and the structure is incrementally extended without retraining, reducing computational complexity. An adaptive node increment module optimizes hyperparameters efficiently. Experimental results demonstrate that ABLN reduces computational overhead while maintaining robust prediction performance across various scenarios.

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More is Better: Channel-Robust Radio Frequency Fingerprinting with Random Overlay Augmentation

Wang Y., Meneghello F., Wang S., Otsuki T., Yuen C., Gui G., Wang X.

IEEE Wireless Communications and Networking Conference Wcnc 2025年

ISSN  15253511

　概要を見る

Radio Frequency Fingerprinting (RFF) is a critical technology for enhancing physical-layer security by leveraging the unique RF characteristics of hardware, enabling authentication and anti-counterfeiting for wireless communication devices. In recent years, Deep Learning (DL) has been extensively applied in RFF, significantly improving identification accuracy and efficiency. However, DL- based RFF methods still encounter challenges regarding robustness, particularly in cross-channel scenarios. To address these challenges, we propose a channel-robust RFF method based on a Multi-Scale Convolutional Attention Network (MSCAN) with Random Overlay Augmentation (ROA). Specifically, MSCAN extracts and fuses features at different scales, allowing it to capture more comprehensive signal characteristics. Additionally, ROA is a combinatorial data augmentation (DA) strategy designed to simulate diverse characteristics of wireless propagation environments, thereby enhancing the adaptability and robustness of RFF in complex channel conditions. Experiments conducted on the ORACLE dataset demonstrate that our proposed method achieves over 92 % accuracy in cross-channel scenarios, outperforming the previously proposed DA strategy. The codes will be published in GitHub¹¹https://github.com/BeechburgPieStar/SDG-for-Robust-SEI

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MobiLLM: Enabling On-Device Fine-Tuning of Billion-Sized LLMs via Server-Assisted Side-Tuning

Li L., Yang X., Wu W., Wang H., Ohtsuki T., Fu X., Pan M., Shen X.

IEEE Journal on Selected Topics in Signal Processing 19 （ 7 ） 1251 - 1265 2025年

ISSN  19324553

　概要を見る

On-device fine-tuning of large language models (LLMs) has attracted a lot of attention because of its tailoring personalized models while retaining user data locally on the mobile device. However, it faces significant challenges due to prohibitive memory requirements and slow training speeds. In this paper, we propose MobiLLM, a novel scheme enabling memory-efficient LLM fine-tuning on a single mobile device via server-assisted side-tuning. Particularly, MobiLLM strategically offloads backpropagation computations to an edge server while allowing the resource-constrained mobile device to retain merely a pretrained backbone model with frozen parameters during finetuning. It constructs a backpropagation bypass via parallel adapters decoupled from the backbone. During forward propagation, the device employs low bitwidth quantization for transmitting intermediate activations to the server to reduce communication overhead. The advantage of MobiLLM lies in: 1) confining training data strictly to the mobile device, and 2) eliminating on-device backpropagation while overlapping local computations with server execution. Collectively, MobiLLM ensures the data never leaves the local mobile device while significantly reducing mobile memory and computational burdens. We implement MobiLLM on several popular mobile devices, including NVIDIA Jetson Xavier NX and CPU-only laptops. Extensive experimental results demonstrate that MobiLLM can enable a resource-constrained mobile device to fine-tune billion-sized LLMs, achieving up to 4x memory reduction and 2.3x faster convergence as compared to state-of-the-art baselines.

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mmPoint-Attention: A Unified Attention Framework for Human Pose and Activity Recognition From mmWave Radar Point Clouds

Shi X., Bouazizi M., Ohtsuki T.

IEEE Sensors Journal 25 （ 14 ） 26794 - 26805 2025年

ISSN  1530437X

　概要を見る

Millimeter-wave (mmWave) radar-based pose estimation and activity recognition provide robust, precise tracking of human movement, unaffected by lighting or environmental challenges. However, existing human pose estimation methods using point cloud data typically rely on single-frame inputs or apply voxelization, both of which have inherent limitations. Moreover, the conventional methods for activity recognition using point cloud often rely on multistep processes that lose contextual information and depend on pose accuracy. To address these issues, in this paper, we propose mmPoint-Attention, a unified attention and Transformer-based model that directly utilizes multiframe point cloud data for both pose estimation and activity recognition without voxelization. Leveraging temporal context and attention mechanisms, our model achieves accurate 3-D joint estimation and activity classification, with postprocessing to enhance sequence consistency. This approach enables high recognition accuracy and computational efficiency across diverse activities compared with conventional methods.

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Memristor-Based Meta-Learning for Fast mmWave Beam Prediction in Non-Stationary Environments

Cao Y., Lu W., Ohtsuki T., Maghsudi S., Jiang X.Q., Tsimenidis C.C.

IEEE International Conference on Communications    1262 - 1267 2025年

ISSN  15503607

　概要を見る

Traditional machine learning techniques have achieved great success in improving data-rate performance and reducing latency in millimeter wave (mmWave) communications. However, these methods still face two key challenges: (i) their reliance on large-scale paired data for model training and tuning, which limits performance gains and makes beam predictions outdated, especially in multi-user mmWave systems with large antenna arrays, and (ii) meta-learning (ML)-based beamforming solutions are prone to overfitting when trained on a limited number of tasks. To address these issues, we propose a memristorbased meta-learning (M-ML) framework for predicting mmWave beam in real time. The M-ML framework generates optimal initialization parameters during the training phase, providing a strong starting point for adapting to unknown environments during the testing phase. By leveraging memory to store key data, M-ML ensures the predicted beamforming vectors are wellsuited to episodically dynamic channel distributions, even when testing and training environments do not align. Simulation results show that our approach delivers high prediction accuracy in new environments, without relying on large datasets. Moreover, MML enhances the model's generalization ability and adaptability.

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Max-Min Strategy for Handover in LEO Based Non-Terrestrial Networks

Nagase R., Yang S., Ohtsuki T.

IEEE Vehicular Technology Conference 2025年

ISSN  15502252

　概要を見る

In Low-Earth Orbit (LEO)-based Non-Terrestrial Networks (NTN), stable and continuous connectivity through efficient handover (HO) mechanisms should be ensured, especially given the high mobility and dynamic satellite coverage in these networks. Conventional HO optimization approaches, such as the Service Continuity Dynamic Programming (SCDP) strategy, optimize HO but may leave some User Equipments (UEs) experiencing low link rates, reduced service time, or frequent handovers. To address this, we propose the Service Continuity Max-Min (SCMM) strategy, which employs the MaxMin algorithm to reduce the number of UEs that have poor connectivity experiences, using a uniquely defined reward function that incorporates link rate, service availability time, and HO frequency. The simulation results demonstrate that SCMM improves the minimum reward across UEs while maintaining an overall performance comparable to that of the existing SCDP methods. This approach offers a robust solution to improve service continuity and user satisfaction in NTN, making it an effective strategy for next-generation satellite networks.

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Heart Rate Estimation Considering Reconstructed Signal Features Based on Variational Mode Decomposition via Multiple-Input Multiple-Output Frequency Modulated Continuous Wave Radar

Nakatani S., Bouazizi M., Ohtsuki T.

Sensors 24 （ 21 ）  2024年11月

　概要を見る

Accurate heart rate estimation using Doppler radar and Frequency Modulated Continuous Wave (FMCW) radar is highly valued for privacy protection and the ability to measure through clothing. Conventional methods struggle to isolate the heartbeat from respiration and body motion. This paper introduces a novel heart rate estimation method using Variational Mode Decomposition (VMD) via Multiple-Input Multiple-Output (MIMO) FMCW radar. The proposed method first estimates human positions within the radar’s coverage, reducing noise by focusing on signals from these positions. The signal is then decomposed into multiple Intrinsic Mode Function (IMF) signals using VMD, and the heartbeat-specific IMF is extracted based on its center frequency. The heart rate signal is reconstructed using weighted addition of IMF signals for each radar cell, with cells defined by specific angles and distances within the coverage area. Peak detection is used to estimate heart rate from these reconstructed signals. To ensure accuracy, the method selects the heart rate estimate with the highest energy and periodicity for the first four time windows. From the fifth time window onward, it selects the estimate closest to the average of the previous four, minimizing extraneous variations. Experiments conducted with one and two subjects showed promising results. In case 1, with one subject, the method achieved a Mean Absolute Error (MAE) of 2.54 BPM and an exclusion rate of 0.94% using MIMO FMCW radar, compared to 4.72% with Doppler radar. In case 2, with two subjects, the method achieved an MAE of 2.28 BPM, confirming accurate simultaneous heart rate estimation.

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Time-Efficient Neural-Network-Based Dynamic Area Optimization Algorithm for High-Altitude Platform Station Mobile Communications †

Takabatake W., Shibata Y., Hoshino K., Ohtsuki T.

Future Internet 16 （ 9 ）  2024年09月

　概要を見る

There is a growing interest in high-altitude platform stations (HAPSs) as potential telecommunication infrastructures in the stratosphere, providing direct communication services to ground-based smartphones. Enhanced coverage and capacity can be realized in HAPSs by adopting multicell configurations. To improve the communication quality, previous studies have investigated methods based on search algorithms, such as genetic algorithms (GAs), which dynamically optimize antenna parameters. However, these methods face hurdles in swiftly adapting to sudden distribution shifts from natural disasters or major events due to their high computational requirements. Moreover, they do not utilize the previous optimization results, which require calculations each time. This study introduces a novel optimization approach based on a neural network (NN) model that is trained on GA solutions. The simple model is easy to implement and allows for instantaneous adaptation to unexpected distribution changes. However, the NN faces the difficulty of capturing the dependencies among neighboring cells. To address the problem, a classifier chain (CC), which chains multiple classifiers to learn output relationships, is integrated into the NN. However, the performance of the CC depends on the output sequence. Therefore, we employ an ensemble approach to integrate the CCs with different sequences and select the best solution. The results of simulations based on distributions in Japan indicate that the proposed method achieves a total throughput whose cumulative distribution function (CDF) is close to that obtained by the GA solutions. In addition, the results show that the proposed method is more time-efficient than GA in terms of the total time required to optimize each user distribution.

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Hypersphere Projection-Guided Radio Frequency Fingerprinting Authentication in the Open World

Fu X., Wang Y., Lin Y., Zhang Q., Gui G., Ohtsuki T., Sari H.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

In this paper, we introduce an innovative Radio Frequency Fingerprinting (RFF)-based device authentication scheme for the Internet of Things (IoT), a network marked by extensive interconnections and interactions among various entities. Our approach, designed for an open and dynamic communication environment, not only identifies devices encountered during training but also effectively rejects those not previously seen. The scheme employs a hypersphere projection for feature embedding, strategically avoiding the need to optimize intra-device variations in the radial direction. It uses a K-Means-based binary classifier for initial device assessment based on cosine similarity scores, followed by a SoftMax classifier for precise identification of known devices. Our extensive numerical analysis confirms that this method delivers superior performance, setting a new benchmark in RFF authentication for IoT security.

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Fully Autonomous Distributed Transmission Parameter Selection Method for Mobile IoT Applications Using Deep Reinforcement Learning

Sugiyama S., Makizoe K., Arai M., Hasegawa M., Otsuki T., Li A.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

With the rapid increase in Internet of Things (IoT) devices, packet collision has become a serious problem in Long Range (LoRa) communication. Furthermore, the rising demand for mobile IoT applications necessitates parameter allocation methods that take into account the mobility of End Devices (EDs). However, the existing transmission parameter selection methods considering mobility are almost centralized designs, which may increase the probability of interference and system latency. The decentralized design is few and requires prior information on ED, which may cause scalability issues, e.g., adding ED to an existing network. To solve the problems above, a method through a fully autonomous distributed design supposing the mobility of ED using Double Deep Q Network (DDQN) is proposed in this paper. In the proposed method, ED trains the model based on ACKnowledge (ACK) packets and its location information without any prior information. Performance evaluation results show that the proposed method can achieve a higher packet delivery rate than other autonomous distributed methods in mobile IoT scenarios.

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Enhanced User Clustering and Pairing Scheme for NOMA-Aided UAV Networks

Yin Y., Bouazizi M., Yang S., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

The increasing demand for spectral efficiency and system capacity in communication networks has driven the integration of Non-Orthogonal Multiple Access (NOMA) technology with Unmanned Aerial Vehicle (UAV) networks. In this paper, we propose an enhanced user clustering and pairing scheme for NOMA-aided UAV networks. Our study aims to maximize the minimum user throughput by proposing an Advanced Balanced K-Means (ABKM) algorithm, based on the traditional K-Means (KM) and Balanced K-Means (BKM) algorithms. The ABKM algorithm addresses the issue in the BKM algorithm where some users are assigned to sub-optimal clusters, resulting in increased distances to the cluster centroids, while simultaneously ensuring balanced clustering. Through extensive numerical simulations, we demonstrate that the ABKM algorithm significantly outperforms KM and BKM algorithms in terms of the minimum user throughput. The results highlight the potential of the proposed ABKM algorithm to enhance system performance and ensure fair resource allocation in NOMA-aided UAV networks, making it a promising solution for future 6G wireless communication systems.

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Enhanced Resource Allocation in Vehicular Networks via Multi-Agent Reinforcement Learning

Zhang Y., Wang S., Hua M., Zhang Y., Wang Y., Tomoaki O., Sari H., Gui G.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

The rapid changes in high-mobility vehicle environments make it challenging for base stations (BS) to obtain comprehensive channel state information. Furthermore, road and traffic safety require communication with low latency and high reliability, posing significant challenges to spectrum resource allocation in vehicular networks. To address these challenges, this paper proposes a method combining dueling double deep-Q network (D3QN) reinforcement learning (RL) with long short term memory (LSTM) network. By using a Manhattan Grid Layout City Model as the foundational environment, a multi-agent model is constructed, with each vehicle-to-vehicle (V2V) link acting as an individual agent. These agents collaborate and interact with the environment, receiving feedback, and then determining the optimal resource allocation to ensure both superior mobile service and a safe driving environment. The experimental results indicate that our proposed method outperforms the conventional D3QN network in both the vehicle-to-infrastructure (V2I) links and the V2V links.

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ECGDiff: Conditional Diffusion Model for ECG Reconstruction from Doppler Signals

Feghoul K., Bouazizi M., Feghoul R., Ohtsuki T.

IEEE International Conference on Communications    232 - 237 2024年

ISSN  15503607

　概要を見る

Electrocardiogram (ECG) measurement is a fundamental diagnostic and monitoring tool in cardiology and healthcare. It plays a crucial role in identifying and managing cardiac conditions, assessing heart function, and improving patient outcomes through timely interventions and treatment adjustments. Conventional approaches for ECG measurement are often unsuitable for everyday use due to their invasiveness, inaccessibility, or limited accuracy. In this context, we introduce ECGDiff, a novel approach based on conditional denoising diffusion models for ECG signals reconstruction from Doppler signals. ECGDiff provides a non-intrusive and accessible method for non-contact ECG measurement. Our method represents a pioneering use of denoising diffusion models for time series trans-lation. In the proposed framework, ECG signals are iteratively reconstructed from random noise using preprocessed signals derived from a Doppler sensor as input conditions. Extensive experiments have been conducted on a dataset comprising 19 healthy individuals, consisting of pairs of synchronized Doppler and ECG signals. Our experimental results demonstrate that the proposed ECGDiff outperforms other state-of-the-art methods by a large margin for the task of ECG signals reconstruction. Specifically, we achieved a DTW score of 8.32, a Frechet distance score of 3.10, and a Pearson correlation coefficient of 0.93. These results highlight the effectiveness of our approach for reconstructing ECG signals from Doppler signals.

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DyFormer: A CNN-Transformer Network Combining Local and Global Attention for Optical Remote Sensing Object Detection

Huang Y., Wang Q., Jiao D., Wang S., Wang Y., Lu Z., Ohtsuki T., Gui G.

2024 4th International Conference on Artificial Intelligence Virtual Reality and Visualization Aivrv 2024    145 - 149 2024年

　概要を見る

Remote sensing image (RSI) object detection is crucial for natural disaster management, urban planning, and resource exploration. However, due to significant differences between RSIs and natural images (NIs), existing object detectors designed for NIs are not directly applicable to RSIs. CNN-based models typically require specialized attention modules to relate small targets to global contexts, while Transformer-based models often add modules to enhance detail extraction, increasing computational costs for edge deployment. To address these challenges, we propose DyFormer, a hybrid CNN-Transformer model that combines local and global attention mechanisms for effective feature fusion. Additionally, a feature residual pyramid network (FRPN) is introduced to enhance multi-scale feature integration. We conduct experiments on the open source NWPU VHR-10 dataset to evaluate the proposed DyFormer, achieving a mAP@0.5 of 61.7%, outperforms existing sort-of-the-art methods, demonstrating its effectiveness for RSI object detection tasks.

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DTL-Based CSI Feedback Combined with Continual Learning in FDD Massive MIMO Systems

Inoue M., Ohtsuki T., Bouazizi M.

2024 International Conference on Computing Networking and Communications Icnc 2024    1077 - 1081 2024年

　概要を見る

The Deep Transfer Learning (DTL)-based Channel State Information (CSI) approach exploits the Deep Neural Network (DNN) model to provide low-cost CSI feedback for the target channel. It involves offline training on a given CSI dataset, followed by fine-tuning for a new environment using a small amount of collected CSI data, reducing feedback costs. However, in practical scenarios, the fine-tuned model performs worse on the source channel compared to the original source model. This leads to continued use of the poorly performing target model until it's fine-tuned again with newly acquired data. To address this, we propose combining DTL-based CSI feedback with continual learning. We introduce elastic weight consolidation (EWC) into the loss function during fine tuning. Simulations show that our method significantly reduces the degradation of the target model on the source channel, as measured by NMSE, compared to a method without continual learning.

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Deep Sub-Image Sampling Based Defense Against Spatial-Domain Adversarial Steganography

Huang X., Cao Y., Ohtsuki T.

2024 IEEE International Conference on Acoustics Speech and Signal Processing Workshops Icasspw 2024 Proceedings    383 - 387 2024年

　概要を見る

Deep steganalyzer combined with neural networks has achieved great success in image classification over recent years. However, it suffers from the following persistent challenges: i) Deep steganalyzer is extremely vulnerable and has the risk of being attacked via adversarial steganography when performing the image classification tasks; ii) Pre-processing based methods aiming to remove adversarial perturbations from cover images jeopardize the accuracy performance, as the involved steganographic signal will be wiped off as well. In this context, to defend against adversarial attacks by an adversary, we propose an adversarial steganography detection scheme based on the pre-processing and feature migration. In brief, sub-images are sampled to obtain the dimensionality of the extracted features, which are usually used to expand them while reducing the effect brought by adversarial perturbations. In particular, by computing statistical features together with normalizing the features, our approach can improve the classification accuracy of the samples. Our experimental results show that the proposed approach is capable of detecting adversarial steganographic image with an accuracy gain of up to 35.9% over the state-of-the-art methods.

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Deep Learning-based Open-set Person Identification using Radar Extracted Cardiac Signals

Xing Z., Bouazizi M., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS 2024年

ISSN  1557170X

　概要を見る

Person identification based on radar-extracted vital signs has become increasingly popular due to its non-contact measurement capabilities. This paper introduces a novel deep learning-based person identification algorithm leveraging radar- extracted vital signs. While current studies mainly focus on closeset conditions with consistent training and testing categories, real-world scenarios often involve open-set circumstances, in which there are more data categories in the testing data. The algorithm involves extracting heart pulse signals from Doppler radar echoes, training two Convolutional Neural Network (CNN)-based models using transfer learning, and utilizing a distribution model for calibration. By combining the models' outputs through a strategic decision-making process, we achieve superior person identification results. Experimental results on a public radar vital signs dataset demonstrate an identification accuracy of 99.61% in close-set conditions and 94.35% in openset conditions, surpassing existing approaches.

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Cramér-Rao Lower Bound and Fairness Optimization in STAR-RIS Assisted ISAC Systems

Wang R., Cao Y., Ohtsuki T., He J.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

This paper studies user fairness of an integrated sensing and communication (ISAC) system adopting both simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NO-MA). Note that optimizing user fairness of an ISAC system is critical, since the performance trade-off in terms of spectrum resource utilization between communication users and sensing users is hard to balance and improve, particularly at the low signal-to-noise ratio (SNR) regimes. However, such optimization problem is a coupled, non-convex, and is NP-hard in general. To solve this challenging problem, a low-complexity algorithm based on both the successive convex approximation and the semi-deterministic programming techniques is proposed. Notably, the proposed approach can maximize the signal-to-interference-plus-noise ratio (SINR) for the weakest communication user, while guaranteeing the superior sensing performance characterized by the cramér-rao lower bound (CRLB) for the sensing user. Simulation results demonstrate that our approach is capable of enabling superior sum-rate performance than the STAR-RIS with orthogonal multiple access (OMA), the conventional-RIS with NOMA, and the conventional-RIS with OMA schemes.

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Coverage and Rate Performance for Distributed Multi-RIS-Assisted mmWave Communications

Cao Y., Wu X., He J., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

Millimeter-wave (mmWave) communication, which operates at high frequencies, has gained extensive research interest due to its significantly wide spectrum and short wavelengths. However, mmWave communication suffers from the notable drawbacks as follows: i) The mmWave signals are sensitive to the blockage, which is caused by the weak diffraction ability of mmWave propagation. ii) The mmWave communication incorporating with reconfigurable intelligent surfaces (RISs) is efficient in overcoming the performance degradation caused by severe path loss. Nevertheless, the location of users and RISs as well as their densities impose a significant impact on the coverage and rate performance. Motivated by the challenges above and based on the stochastic geometry concept, we first construct distributed multi-RIS-aided mmWave communication system over Nakagami-m fading channel. Afterward, we analyze the end-to-end (E2E) signal-to-interference-plus-noise-ratio (SINR) coverage and rate performance of the system. To improve the system performance in term of the E2E SINR coverage probability and achievable rate, we study the optimization of the phase-shifting control of the distributed RISs and optimize the E2E SINR coverage particularly when installing a large number of reflecting elements in RISs. To facilitate the study, we optimize the dynamic association criterion between the source and destination. Our simulation results demonstrate that the deployment of distributed RISs can significantly improve the E2E SINR coverage probability and achievable rate of the system compared to the selected benchmarks.

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Chirp Correction and Phase Accumulation-Linear Interpolation-assisted ICEEMDAN-based Heart Rate Estimation from MIMO FMCW Radar

Mutsukawa M., Shi X., Ohtsuki T.

2024 IEEE International Conference on E Health Networking Application and Services Healthcom 2024 2024年

　概要を見る

Non-contact heart rate measurement is anticipated to be used in various scenarios such as medical and disaster sites, driver monitoring, and smart homes. Millimeter-wave radar-based non-contact heart rate measurement leverages the chest wall displacement signal, which is a superposition of heartbeat and respiration. However, the chest wall displacement signals are susceptible to higher harmonics of respiration and other intermodulation harmonics, which can impede heart rate measurement. In this paper, we investigate a heart rate estimation method utilizing MIMO (Multiple-Input Multiple-Output) FMCW (Frequency Modulated Continuous Wave) radar. Conventional methods that utilize mode decomposition are not robust enough, as the accuracy of the results is highly sensitive to the SNR (Signal-to-Noise Ratio) of the selected IMFs (Intrinsic Mode Functions). Therefore, we propose performing chirp correction in addition to the conventional method that combines PA-LI (Phase Accumulation-Linear Interpolation) and ICEEMDAN (Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to improve the accuracy of heart rate estimation. Our experimental results show that this approach enhances the SNR of the decomposed signal containing heart rate information and improves the accuracy of heart rate estimation.

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Camera-based Stress Detection using Face-related and Emotion-related Features

Ogasawara R., Bouazizi M., Ohtsuki T.

2024 IEEE International Conference on E Health Networking Application and Services Healthcom 2024 2024年

　概要を見る

Mental stress is something we experience on a daily basis. However, when it becomes chronic, it has various negative effects on our body. Although stress monitoring methods based on physiological signals are effective, they may not provide a comfortable experience for users, as they require sensors to be worn or attached to the body. As such, the need for non-invasive, non-contact and comfortable methods arises. In this paper, we propose a stress detection method using face-related and emotion-related features, all of which can be acquired by using a camera. Face-related features include action units and face embedding. Emotion related features include valence, arousal and emotion-related labels. Through these features and their fusions we achieved accuracy of 1.000 for stress detection, 0.891 for task recognition and 0.883 for stress level classification, respectively, with the sample sizes of 111, 316, and 319, respectively. These results demonstrate that our proposed method using face-related and emotion-related features and their fusion is effective in stress monitoring and detection.

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Beamforming Design using UE Positions and 3D Terrain-Building in HAPS System

Li Z., Yang S., Yin Y., Bouazizi M., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

High Altitude Platform Stations (HAPS) are instrumental in wireless communications, providing enhanced connectivity and extensive coverage by complementing ground-based infrastructure where its expansion is limited. HAPS enhance wireless communications by employing advanced beamforming technology, traditionally based on 2D with free space path loss models. Addressing the inadequacy of these models in different areas, our research introduces a novel beamforming strategy that incorporates detailed 3D geographic and architectural information. This approach models line-of-sight (LOS) and non-line-of-sight (NLOS) conditions with 3D information and optimizes beamforming patterns using Deep Reinforcement Learning (DRL). Our results indicate that by incorporating 3D information, the beamforming performance in terms of average throughput and SINR is markedly enhanced across all user equipments (UEs), compared to traditional 2D approaches. By taking 3D information into account, beamforming in HAPS systems becomes more equitable.

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Age-Based Federated Learning Approach to In-Network Caching: An Online Scheduling Policy

Cao Y., Maghsudi S., Ohtsuki T.

IEEE International Conference on Communications    1437 - 1442 2024年

ISSN  15503607

　概要を見る

We develop an accurate real-time scheduling framework for federated learning (FL) in wireless caching networks to guarantee the successful delivery of files at a low cost and with a short delay. The following persisting challenges motivated our work: i) Enforcing an excessive number of FL model update per communication round is infeasible due to the limited backhaul spectrum; ii) Naive scheduling policy accounting for FL model update renders service backlogs, thus leading to network parameter staleness and in-network caching utility (ICU) deterioration. Optimal scheduling in FL is challenging, as the mobile users' preferences for content, request patterns, and network traffic are dynamic and unknown. To tackle that challenge, we first formulate an instantaneous ICU optimization problem against the stale FL models. Afterward, based on the concept of age-of-update (AoU), we propose a federated learning with an unsatisfactory set selection (FedUSS) approach capable of executing the multiple-tasks of short-term predictions and making cache replacement decisions at low cost. Theoretical and numerical analyses manifest the effectiveness of our approach.

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A Low-Complexity Clustering-Aided DQN Method for Dynamic Antenna Control in HAPS

Bouazizi M., Yang S., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

This paper aims to address the issue of low through-put for users, caused by the random movement of High-Altitude Platform Stations (HAPS) due to winds. The proposed solution involves developing an Equal Clustering (EC) approach that groups users into high-density clusters, ensuring an equal num-ber of users in each cluster while maintaining low complexity. To further enhance the system's throughput performance, we fine-tune the antenna parameters using a Deep Q-Network (DQN) and the results of the EC clustering. To evaluate the effectiveness of the proposed method, we compare it with three Reinforcement Learning (RL)-based approaches and a K-Means clustering-based method. Simulation results indicate that both the EC method and the EC-aided DQN method successfully enhance the Cumulative Distribution Function (CDF) performance of throughput distribution when compared to the RL-based method for both rotation and shift scenarios. Furthermore, the EC-aided DQN method outperforms the K-Means clustering-based method in terms of the CDF of throughput performance.

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A CSI-based Cross-subject Transfer Learning Method Using Network Freezing

He Z., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    3515 - 3520 2024年

ISSN  23340983

　概要を見る

Currently, the field of wireless sensing is moving towards non-contact and easy-to-deploy passive sensing technologies. Among these, sensing techniques based on WiFi channel state information (CSI) have emerged as highly promising due to their excellent performance and suitability for current sensing needs. However, despite the potential of WiFi CSI-based sensing technologies, there are still pressing issues that need to be addressed. Traditional WiFi CSI-based methods face challenges such as weak generalization ability and poor robustness, especially when the sensing target and environment change, which hinders the further development. To address this problem, this paper introduces the transfer learning method and combines it with the deep neural network to propose a novel cross-subject WiFi sensing method. Specifically, this method utilizes the source domain-target domain partitioning approach in transfer learning. In the source domain, deep neural networks are trained to obtain the source domain feature model, which is then transferred to the target domain. Fine-tuning is performed using techniques such as network freezing, aiming to meet the faster and more accurate sensing task demands of cross-subject. From the final experimental results, the novel cross-subject transfer learning method proposed in this paper achieved higher recognition accuracy and shorter training time. Moreover, the implementation of network freezing further enhanced the performance and efficiency of cross-subject, achieving up to 86% performance on the dataset used in this paper.

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A Comparative Study of Methods of Person Identification Using Radar-Extracted Heartbeat Signals

Liu K., Bouazizi M., Xing Z., Ohtsuki T.

IEEE Access 12   152196 - 152211 2024年

　概要を見る

In recent years, non-contact biometric identification has garnered significant attention owing to its flexibility and its capacity to ensure privacy and confidentiality. Previous research has predominantly centered on harnessing cardiac radar signals detected by radar systems. This paper aims to compare two methods for person identification utilizing heartbeat signals extracted from radar data. In the first approach, we directly employ the heartbeat time series data as input for a deep learning model called InceptionTime dedicated to person identification. The second method explores the viability of using spectrograms, which are images generated from cardiac radar heartbeat signals, combined with other deep learning models, ResNet and a small convolutional neural network (CNN) model built by us, to achieve the same goal. Additionally, we assessed the robustness of both methods by introducing white Gaussian noise into the signals and evaluating their performance under different noise levels. For the second method, we implemented three image denoising methods: autoencoder, deep image prior (DIP), and Dilated-Residual U-Net (DRU-Net) to denoise the spectrograms after adding noise in the time domain. Experimental results reveal that the accuracy of person identification using spectrograms is 100% without adding noise based on the public dataset. The identification accuracy is the same as that using time series data. Following the introduction of noise, the accuracy of both methods decreases; however, the method based on spectrograms exhibits a more pronounced decline in accuracy compared to the approach using time series data directly especially for the small CNN model we built. The image-based method with DIP image denoiser shows outstanding performance when the value of noise is high compared with InceptionTime.

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3D-LiDAR-Based Fall Detection by Dynamic Non-Linear Mapping with LSTM

Meng X., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications    818 - 823 2024年

ISSN  15503607

　概要を見る

With the aging of the population, the health of the elderly people has become a concern for many families. Monitoring of elderly people and the detection of their activities have become hot research topics. Several approaches have been proposed in the literature to detect such activity (e.g., falls), but their reliance on RGB cameras poses a serious threat to the privacy of the elderly. Therefore, Light Detection and Ranging (LiDAR) has attracted the attention of researchers. In this paper, we propose a novel method for fall detection using transfer learning and dynamic non-linear mapping to extract the human skeletons from depth images and implement a fall detection method using a Long Short-Term Memory (LSTM) neural network. We collected RGB images and depth images using 3D-LiDAR. The RGB images are used as a ground-truth for annotation and evaluation, whereas depth images are used as input to our proposed method. For fall detection, we achieved 98.5% accuracy, 97.0% precision, 100% recall, and 98.5% F1 score.

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Low Complexity CSI Feedback Method Using Reformer

Saito Y., Bouazizi M., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

To leverage antenna diversity effectively in Massive Multiple-Input Multiple-Output (MIMO) systems, Channel State Information (CSI) utilization at the base station (BS) is crucial. In Frequency Division Duplexing (FDD) MIMO setups, downlink CSI is acquired by user equipment (UE) and transmitted back to the BS. However, this feedback overhead diminishes communication throughput, necessitating CSI compression technologies. Methods such as traditional compression or more advanced ones such as Transformer-based neural networks which aim to address this issue suffer from low compression rates or high computational cost. In this context, Reformers emerge as a potential solution as they employ Locality-Sensitive Hashing (LSH) attention, offering a comparable accuracy to Transformers but with reduced computational demands. In this paper, we propose a CSI feedback method using Reformer. Specifically, by replacing the multi-head attention of the CSI feedback method using Transformer with the LSH attention of Reformer, we aimed to achieve similar CSI reconstruction accuracy while reducing computational complexity. Computer simulations confirm that the CSI feedback method using Reformer reduces computational complexity by about 12% while achieving comparable CSI reconstruction accuracy compared to the CSI feedback method using Transformer.

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Toward Building Trust in Machine Learning Models: Quantifying the Explainability by SHAP and References to Human Strategy

Li Z., Bouazizi M., Ohtsuki T., Ishii M., Nakahara E.

IEEE Access 12   11010 - 11023 2024年

　概要を見る

Local model-agnostic Explainable Artificial Intelligence (XAI), such as LIME or SHAP, has recently gained popularity among researchers and data scientists for explaining black box Machine Learning (ML) models. In the industry, practitioners focus not only on how these explanations can validate their models but also on how they can help maintain trust from end-users. Some studies attempted to measure this ability by quantifying what they refer to as the explainability or interpretability of ML models. In this paper, we introduce a new method for measuring explainability with reference to an approximated human model. We develop a human-friendly interface to strategically collect human decision-making and translate it into a set of logical rules and intuitions, or simply annotations. These annotations are then compared with the local explanations derived from common XAI tools. Through a human survey, we demonstrate that it is possible to quantify human intuition and empirically compare it to a given explanation, enabling a practical quantification of explainability. By relying on this new method, we identified several potential flaws in today's ML selection process. Furthermore, we demonstrate how our method can help to better evaluate ML models.

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Source CSI Dataset for Multi-Task CSI Feedback

Inoue M., Ohtsuki T.

Proceedings IEEE Consumer Communications and Networking Conference Ccnc    1042 - 1043 2024年

ISSN  23319860

　概要を見る

The muti-task Channel State Information (CSI) feedback is a downlink CSI feedback approach. It focuses on Clustered Delay Line (CDL) channel models as the channel environments. There are five different CDL channel models, from CDL-A to CDL-E. In this method, the autoencoder (source model) is pre-trained using the CDL-ALL dataset, which is an equal mixture of each CDL channel dataset (source data) rather than a single CDL channel dataset. The decoder at the BS is subsequently fine-tuned using a small amount of CSI data from the target channel environment (target data) to generate a target model. The source model has a significant influence on the reconstruction performance of the target model, the amount of data, and the number of training epochs required to obtain it. This paper investigates the mixing ratio of each CDL channel dataset used as source data to enhance the CSI reconstruction performance of the source model in the muti-task CSI feedback. Furthermore, we explore the possibility of enhancing the CSI reconstruction performance of the target model by employing the improved source model for fine-tuning the target model. To determine the mixing ratio, two distinct criteria for source model performance are employed. The simulation results identified several mixing ratios of source data in these criteria that improve the reconstruction performance of both the source and the target models compared to the use of CDL-ALL as a source dataset in the multi-task CSI feedback.

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Rough-to-Fine Model-based Non-contact Heart Rate Estimation using MIMO FMCW Radar

Shi X., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    920 - 925 2024年

ISSN  23340983

　概要を見る

MIMO FMCW Radar, a non-contact heart rate (HR) monitoring technology, has emerged as one of the superior alternatives to contact-based sensors, providing precise HR estimation without the drawbacks of discomfort or privacy concerns, and excelling in diverse environmental conditions. Recent HR estimation studies using conventional methods have achieved high accuracy but face challenges with lengthy processing times and sensitivity to experimental conditions, affecting real-time application and robustness. A previous Deep learning (DL)-based approach improves on these aspects but is limited by the range resolution of SISO FMCW Radar and the requirement for close positioning of subjects. Additionally, this method has inability to utilize contextual information from adjacent time windows restricts its effectiveness for time series analysis. Thus, our proposed method combines a Curve-Length (CL) approach with a DL rough-to-fine model, addressing the limitations of previous studies and improving HR estimation accuracy and robustness. This integrated process begins with human location detection through a CL-based approach and is followed by a two-phase rough-to-fine HR estimation. The experimental results show significant improvement over the conventional methods.

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Remote Inter-beat Interval Estimation: A Signal Reconstruction Approach with Multi-channel Input and Channel-Wise Attention Mechanism

Wang S., Bouazizi M., Ohtsuki T.

2024 IEEE International Conference on E Health Networking Application and Services Healthcom 2024 2024年

　概要を見る

This paper presents a radar-based heart rate monitoring and Inter-beat interval (IBI) estimation. Traditional IBI estimation relies on peak detection combined with signal processing techniques. Our approach uses signal reconstruction with a neural network, which significantly improves the accuracy and robustness of IBI estimation compared to conventional methods. By utilizing multi-channel input data, our approach effectively mitigates various sources of noise and interference, resulting in highly accurate and reliable IBI predictions. Instead of relying solely on peak detection, we use the U-net architecture and cross-channel attention mechanism to reconstruct the triangular waveform generated from the ground truth ECG signals. The incorporation of multi-channel input and channel-wise attention mechanism improves our model’s ability to discriminate and emphasize critical features from different input channels, further improving IBI estimation accuracy. To evaluate the accuracy and robustness of our proposed method, we trained our model on the open-source dataset [1], and then performed leave-one-subject-out validation, which ensures our approach to be evaluated only on unseen independent subjects. We have achieved a Root Mean Square Error (RMSE) of 26.7 ms for the IBI of each heart-beat. Our results demonstrate the transformative potential of adopting signal reconstruction methods supported by state-of-the-art deep learning techniques. This shift in perspective promises more accurate and robust heart rate and IBI estimation, opening new avenues for improving the accuracy and reliability of cardiac monitoring systems.

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Object Detection on SAR Images Via YOLOv10 and Integrated ACmix Attention Mechanism

Wang Z., Miao F., Huang Y., Lu Z., Ohtsuki T., Gui G.

2024 6th International Conference on Robotics Intelligent Control and Artificial Intelligence Ricai 2024    756 - 760 2024年

　概要を見る

With the rapid advancement of deep learning techniques, object detection on Synthetic Aperture Radar (SAR) images has shown increasing potential in recent years. However, due to the unique microwave imaging mechanism of SAR, targets can easily be confused with background structures like harbors and aircraft aprons, leading to poor recognition accuracy. In this paper, we propose YOLOv10n-ACmix, an enhanced object detection model that integrates the ACmix attention mechanism into the YOLOv10 framework to improve feature extraction and detection precision. Through experiments conducted on the SAR-aircraft and MSAR datasets, YOLOv10n-ACmix consistently outperformed YOLOv5, YOLOv8 and YOLOv10 in precision, recall, and mean Average Precision (mAP). The inclusion of the ACmix mechanism enables the model to focus more effectively on critical features, resulting in more accurate detections. Despite a slight increase in computational complexity, the significant improvements in detection performance make YOLOv10n-ACmix a robust choice for high-precision object detection in SAR images. This work highlights the potential of attention mechanisms to enhance object detection in complex, large-scale SAR image datasets.

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Multi-UAV-Assisted Emergency Caching Networks: UAV Location and NOMA Power Optimization

Yin Y., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

In this paper, we propose an unmanned aerial vehicles (UAVs) assisted emergency caching framework based on non-orthogonal multiple access (NOMA), involving a primary UAV and several secondary UAVs. The primary UAV obtains rescue information from a remote base station (BS) and sends it to secondary UAVs hovering at designated locations to serve ground users. Specifically, a multi-objective optimization problem is formulated to maximize the number of files that can be successfully decoded in the primary UAV and minimize the number of user clusters for secondary UAVs. The multi-objective optimization problem is decomposed into three sub-problems: primary UAV deployment, power allocation, and secondary UAV deployment. First, a primary UAV deployment scheme is proposed to maximize the number of received files subject to the constraint of the demand for the line-of-sight (LoS) link probability between the primary UAV and the ground users. Subsequently, to achieve the same goal, a closed-form solution for the NOMA power allocation factor of the files is derived. Finally, due to the limited range of services of the secondary UAVs, we propose a secondary UAV deployment scheme to minimize the number of their flights, which can save energy and reduce user latency. Extensive numerical results demonstrate that the proposed algorithms outperform the random scheme and the worst scheme.

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Multi-Dimensional Representation for Semantic Communication: A New Horizon for Customized Visualization of Shared Knowledge

Bouazizi M., Ohtsuki T.

IEEE Vehicular Technology Conference 2024年

ISSN  15502252

　概要を見る

Semantic communication plays a crucial role in human interactions, allowing for the exchange of complex ideas and concepts. In this paper, we introduce a novel approach to semantic communication leveraging image generative Artificial Intelligence (AI) models, specifically stable diffusion models. Unlike conventional works, our system enables the transmission of images through a physical channel by transforming them into multi-dimensional semantic representations consisting of text descriptions, low-resolution sketches, and pose information. At the receiver's end, these semantic representations are used to reconstruct the original image using a trained stable diffusion model. The benefits of our approach include reduced transmission bandwidth requirements, flexibility in reconstruction styles, adaptability to multiple receivers' preferences, and the ability to omit unwanted image elements. We present preliminary results demonstrating the feasibility and effectiveness of our method. The similarity score between the transmitted images and reconstructed ones reach values ranging between 0.015 and 0.029 in Root Mean Square Error (RMSE) and between 0.993 and 0.998 using a Siamese network.

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mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud

Al M.A., Shi X., Mondher B., Ohtsuki T.

IEEE International Conference on Communications    2161 - 2166 2024年

ISSN  15503607

　概要を見る

Pose estimation and human action recognition (HAR) are pivotal technologies spanning various domains. While the image-based pose estimation and HAR are widely admired for their superior performance, they lack in privacy protection and suboptimal performance in low-light and dark environments. This paper exploits the capabilities of millimeter-wave (mmWave) radar technology for human pose estimation by processing radar data with Graph Neural Network (GNN) architecture, coupled with the attention mechanism. Our goal is to capture the finer details of the radar point cloud to improve the pose estimation performance. To this end, we present a unique feature extraction technique that exploits the full potential of the GNN processing method for pose estimation. Our model mmGAT demonstrates remarkable performance on two publicly available benchmark mmWave datasets and establishes new state of the art results in most scenarios in terms of human pose estimation. Our approach achieves a noteworthy reduction of pose estimation mean per joint position error (MPJPE) by 35.6% and PA-MPJPE by 14.1% from the current state of the art benchmark within this domain.

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Massive MIMO Belief Propagation Detection Using DIP with DNN-Trained Scaling Factor

Tachibana J., Bouazizi M., Ohtsuki T.

IEEE Wireless Communications and Networking Conference Wcnc 2024年

ISSN  15253511

　概要を見る

Belief propagation (BP) detection is a technique for separating and detecting incoming signals with minimal complexity in massive multiple-input multiple-output (MIMO) systems. However, because of the interference and noise that remain in the received signals even after attempting to remove them through conventional techniques, errors manifest in the transmitted messages. Due to the MIMO channel's numerous loops, a message containing mistakes spreads across the factor graph leading to a degradation in the BP's convergence properties and detection performance. In this paper, we propose a BP detection using deep image prior (DIP) with deep neural network (DNN)-trained scaling factor. By applying DIP to the BP detection algorithm, we achieve a reduction in residual interference and noise. Post DIP application, there is a modification in the variance of both interference and noise components. To align it more accurately with its true value and enhance message reliability, we adjust the variance using scaling factors trained through DNN-based damped BP (DNN-dBP). Using computer simulations, we demonstrate that applying DIP helps decrease the power of the residual interference and noise after removing the interference at each iteration in the BP detection. It is also shown that the proposed method improves the detection performance compared to the normal BP detection, BP detection without DIP when training the scaling factors of the variance, and BP detection to which DIP is applied without training the scaling factors of the variance.

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Joint Optimization of User Pairing, Power Allocation and Content Server Deployment in NOMA-Assisted Wireless Caching Networks

Yin Y., Ohtsuki T., Gui G., Yuen C., Wu H.C., Sari H.

IEEE Transactions on Vehicular Technology 72 （ 12 ） 16866 - 16870 2023年12月

ISSN  00189545

　概要を見る

In this paper, we propose a joint optimization algorithm of user pairing, power allocation, and content server (CS) deployment in non-orthogonal multiple access (NOMA) assisted wireless caching networks (WCN). The main purpose of the proposed algorithm is to maximize the hit probability of users, which is determined by the hit probability of CSs and the decoding success probability of users. Since the optimization problem is non-convex and difficult to solve directly, it is decomposed into three sub-problems, user pairing, power allocation, and CS deployment. First, users are served by CSs in a hybrid NOMA manner and paired according to the power demand coefficient. Secondly, we derive the closed-form solution of the power allocation factor in the Rayleigh fading channels. Thirdly, the simulated annealing algorithm is used to optimize the deployment of CSs. Finally, simulation results confirm that the proposed joint optimization scheme outperforms the benchmark scheme.

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DRL based low carbon economic dispatch by considering power transmission safety limitations in internet of energy

Zhu R., Guan X., Zheng J., Wang N., Jiang H., Cui C., Ohtsuki T.

Internet of Things Netherlands 24 2023年12月

　概要を見る

Economic dispatch, as a crucial method for ensuring the normal operation of power systems, is typically modeled as an optimization problem and solved using solvers. The introduction of low-carbon requirements has increased the complexity of the optimization problem, as economic dispatch now needs to consider effectively reducing the emission of CO<inf>2</inf>. The utilization of renewable energy can mitigate carbon emissions during power system operation, but its inherent uncertainty poses safety risks to transmission lines between microgrids and the main grid. Hence, this paper explores the issue of low-carbon economic dispatch to ensure the reliability of power transmission. We introduce a two-stage low-carbon economic dispatch model grounded in deep reinforcement learning. In the first stage, we incorporate the transmission power limits between microgrids and the main grid as constraints, generate day-ahead dispatch strategies, and determine the weights of factors in the reward function. In the second stage, we employ two methods: adjusting the weight of the penalty for exceeding the transmission limits and adding conservative safety limit constraints, to solve the problem of transmission power exceeding limits caused by uncertainties of renewable energy and prediction errors. Three deep reinforcement learning algorithms, all rooted in the Actor–Critic structure, are employed to implement the two-stage economic dispatch model. Experimental results affirm the efficacy of the proposed model in mitigating the risk of transmission power exceeding its limit, reducing carbon emissions, and minimizing operational costs.

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An Intelligent Path Planning Mechanism for Firefighting in Wireless Sensor and Actor Networks

Panahi F.H., Panahi F.H., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  10 （ 11 ） 9646 - 9661 2023年06月

　概要を見る

Forests have an important role in environmental preservation and maintenance. The primary threat is forest fires, which have disastrous repercussions. As a result, it is critical to identify and extinguish a fire before it spreads and destroys resources. To that end, we propose a forest fire detection and fighting mechanism using wireless sensor and actor networks (WSANs). Temperature sensors are utilized to detect fires, and actors (robots) are employed to extinguish them. Sensors and robots are distributed at random throughout the forest, forming clusters. Clustering, sleep/active scheduling for the sensors, and energy harvesting (EH)/moving modes for the robots, are used to extend and maximize the sensors/robots lifetime in the WSAN. In such a network, robots should move to the fire site as quickly as possible. To do this, we further propose a robot routing mechanism that focuses on determining the shortest path for each firefighting robot. In particular, each firefighting robot equipped with on-board processing uses a fuzzy Q-learning (FQL)-based trajectory mechanism to learn the shortest path to the fire zone in the least amount of time. Simulations are conducted to demonstrate the benefits of employing the proposed framework for rapid and effective fire response. When compared to the traditional Q-learning, the total approaching rate (a measure of how quickly the firefighting robots can reach the fire) to the fire spot is greater when utilizing the proposed FQL-based strategy.

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Semisupervised Federated-Learning-Based Intrusion Detection Method for Internet of Things

Zhao R., Wang Y., Xue Z., Ohtsuki T., Adebisi B., Gui G.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  10 （ 10 ） 8645 - 8657 2023年05月

　概要を見る

Federated learning (FL) has become an increasingly popular solution for intrusion detection to avoid data privacy leakage in Internet of Things (IoT) edge devices. Existing FL-based intrusion detection methods, however, suffer from three limitations: 1) model parameters transmitted in each round may be used to recover private data, which leads to security risks; 2) not independent and identically distributed (non-IID) private data seriously adversely affect the training of FL (especially distillation-based FL); and 3) high communication overhead caused by the large model size greatly hinders the actual deployment of the solution. To address these problems, this article develops an intrusion detection method based on a semisupervised FL scheme via knowledge distillation. First, our proposed method leverages unlabeled data via distillation method to enhance the classifier performance. Second, we build a model based on convolutional neural networks (CNNs) for extracting deep features of the traffic packets, and take this model as both the classifier network and discriminator network. Third, the discriminator is designed to improve the quality of each client's predicted labels, and to avoid the failure of distillation training caused by a large number of incorrect predictions under private non-IID data. Moreover, the combination of the hard-label strategy and voting mechanism further reduces communication overhead. The experiments on the real-world traffic data set with three non-IID scenarios show that our proposed method can achieve better detection performance as well as lower communication overhead than state-of-the-art methods.

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Graph Learning Empowered Situation Awareness in Internet of Energy With Graph Digital Twin

Sui L., Guan X., Cui C., Jiang H., Pan H., Ohtsuki T.

IEEE Transactions on Industrial Informatics （IEEE Transactions on Industrial Informatics）  19 （ 5 ） 7268 - 7277 2023年05月

ISSN  15513203

　概要を見る

Internet of energy (IoE) is one of the most complex industrial systems, and its stable operation is very important. Situation awareness (SA) has been proposed to ensure the stable operation for IoE and making full use of the relationships between components has become the key point for designing an efficient SA model. In this article, graph digital twin (GDT) is proposed by combining digital twin technology with graph theory, to describe the logical relationships between physical entities more accurately in digital space, and then a novel SA model for IoE based on GDT is proposed. In order to make full use of the relationship between nodes, two classifiers based on graph convolution network are designed for fault location and stability prediction. The experimental results show that the proposed SA model can localize the multiple fault components with high accuracy, and can accurately predict the stability of the system.

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A 2D-Lidar-Equipped Unmanned Robot-Based Approach for Indoor Human Activity Detection

Bouazizi M., Lorite Mora A., Ohtsuki T.

Sensors （Sensors）  23 （ 5 ）  2023年03月

ISSN  14248220

　概要を見る

Monitoring the activities of elderly people living alone is of great importance since it allows for the detection of when hazardous events such as falling occur. In this context, the use of 2D light detection and ranging (LIDAR) has been explored, among others, as a way to identify such events. Typically, a 2D LIDAR is placed near the ground and collects measurements continuously, and a computational device classifies these measurements. However, in a realistic environment with home furniture, it is hard for such a device to operate as it requires a direct line of sight (LOS) with its target. Furniture will block the infrared (IR) rays from reaching the monitored person thus limiting the effectiveness of such sensors. Nonetheless, due to their fixed location, if a fall is not detected when it happens, it cannot be detected afterwards. In this context, cleaning robots present a much better alternative given their autonomy. In this paper, we propose to use a 2D LIDAR mounted on top of a cleaning robot. Through continuous movement, the robot is able to collect distance information continuously. Despite having the same drawback, by roaming in the room, the robot can identify if a person is laying on the ground after falling, even after a certain period from the fall event. To achieve such a goal, the measurements captured by the moving LIDAR are transformed, interpolated, and compared to a reference state of the surroundings. A convolutional long short-term memory (LSTM) neural network is trained to classify the processed measurements and identify if a fall event occurs or has occurred. Through simulations, we show that such a system can achieve an accuracy equal to 81.2% in fall detection and 99% in the detection of lying bodies. Compared to the conventional method, which uses a static LIDAR, the accuracy reaches for the same tasks 69.4% and 88.6%, respectively.

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Distributed Feature Selection Considering Data Pricing Based on Edge Computing in Electricity Spot Markets

Hu Y., Guan X., Hu B., Liu Y., Chen H., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  10 （ 3 ） 2231 - 2244 2023年02月

　概要を見る

With the rapid development of information technology, the multisource heterogeneous data containing meaningful information have been significantly generated by various edge devices in Internet of Energy, which is one of essential foundations of many knowledge discovery tasks based on edge computing. For some complicated tasks, essential features are owned by different data sellers offering data by blockchains. With limited budgets, buying features are crucial steps in knowledge discovery tasks in electricity spot markets, especially for learning-based algorithms. However, there are lack of proper data pricing mechanisms tailored to dynamic learning processes. Besides, existing methods cannot efficiently employ edge computing servers to obtain optimal policies for selecting features according to dynamic pricing with limited budgets. To overcome such drawbacks, a data pricing mechanism is proposed in this article, which consists of static and dynamic pricing parts. Based on this mechanism, given limited budgets, a feature selection (FS) algorithm considering multiple new factors is proposed, which offers near-optimal solutions for FS at different scenarios. Numeric results show the effectiveness of the proposed algorithms.

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Machine Learning in 6G Wireless Communications

Ohtsuki T.

IEICE Transactions on Communications （IEICE Transactions on Communications）  E106B （ 2 ） 75 - 83 2023年02月

ISSN  09168516

　概要を見る

Mobile communication systems are not only the core of the Information and Communication Technology (ICT) infrastructure but also that of our social infrastructure. The 5th generation mobile communication system (5G) has already started and is in use. 5G is expected for various use cases in industry and society. Thus, many companies and research institutes are now trying to improve the performance of 5G, that is, 5G Enhancement and the next generation of mobile communication systems (Beyond 5G (6G)). 6G is expected to meet various highly demanding requirements even compared with 5G, such as extremely high data rate, extremely large coverage, extremely low latency, extremely low energy, extremely high reliability, extreme massive connectivity, and so on. Artificial intelligence (AI) and machine learning (ML), AI/ML, will have more important roles than ever in 6G wireless communications with the above extreme high requirements for a diversity of applications, including new combinations of the requirements for new use cases. We can say that AI/ML will be essential for 6G wireless communications. This paper introduces some ML techniques and applications in 6G wireless communications, mainly focusing on the physical layer.

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Deep Reinforcement Learning Evolution Algorithm for Dynamic Antenna Control in Multi-Cell Configuration HAPS System

Yang S., Bouazizi M., Ohtsuki T., Shibata Y., Takabatake W., Hoshino K., Nagate A.

Future Internet （Future Internet）  15 （ 1 ）  2023年01月

　概要を見る

In this paper, we propose a novel Deep Reinforcement Learning Evolution Algorithm (DRLEA) method to control the antenna parameters of the High-Altitude Platform Station (HAPS) mobile to reduce the number of low-throughput users. Considering the random movement of the HAPS caused by the winds, the throughput of the users might decrease. Therefore, we propose a method that can dynamically adjust the antenna parameters based on the throughput of the users in the coverage area to reduce the number of low-throughput users by improving the users’ throughput. Different from other model-based reinforcement learning methods, such as the Deep (Formula presented.) Network (DQN), the proposed method combines the Evolution Algorithm (EA) with Reinforcement Learning (RL) to avoid the sub-optimal solutions in each state. Moreover, we consider non-uniform user distribution scenarios, which are common in the real world, rather than ideal uniform user distribution scenarios. To evaluate the proposed method, we do the simulations under four different real user distribution scenarios and compare the proposed method with the conventional EA and RL methods. The simulation results show that the proposed method effectively reduces the number of low throughput users after the HAPS moves.

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Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval

Nakatani S., Yamamoto K., Ohtsuki T.

Bioengineering （Bioengineering）  10 （ 1 ）  2023年01月

　概要を見る

Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to detect a heartbeat. Fetal arrhythmia can be detected based on the heartbeat detection results from FECG signals such as heartbeat intervals. However, the accuracy of arrhythmia detection easily degrades depending on the accuracy of heartbeat detection. In this paper, we propose a deep learning-based fetal arrhythmia detection method using FECG signals. Recently, arrhythmia detection methods using adult ECG signals have achieved a high arrhythmia detection accuracy based on deep learning. Motivated by this fact, in the proposed method, the acquired FECG signals are segmented, and the segments are input into a deep learning model that classifies them into normal or arrhythmia ones. Based on the classification results of multiple segments, a subject is judged as a healthy or arrhythmia subject. Each segment of the training data is divided into three categories based on the estimated heartbeat interval: (i) normal, (ii) arrhythmia, and (iii) a segment that could be both normal and arrhythmic. Only segments labeled as normal or arrhythmia are used for training a deep learning model to achieve a higher classification accuracy of the model. Through these procedures, the proposed method detects fetal arrhythmia with fewer effects of heartbeat detection results. The experimental results show that the proposed method achieves 96.2% accuracy, 100% specificity, and 100% recall, improving the values of conventional methods based on heartbeat detection and feature detection.

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Unsupervised Learning-Based Non-Invasive Fetal ECG Muti-Level Signal Quality Assessment

Shi X., Yamamoto K., Ohtsuki T., Matsui Y., Owada K.

Bioengineering （Bioengineering）  10 （ 1 ）  2023年01月

　概要を見る

Objective: To monitor fetal health and growth, fetal heart rate is a critical indicator. The non-invasive fetal electrocardiogram is a widely employed measurement for fetal heart rate estimation, which is extracted from the electrodes placed on the surface of the maternal abdomen. The qualities of the fetal ECG recordings, however, are frequently affected by the noises from various interference sources. In general, the fetal heart rate estimates are unreliable when low-quality fetal ECG signals are used for fetal heart rate estimation, which makes accurate fetal heart rate estimation a challenging task. So, the signal quality assessment for the fetal ECG records is an essential step before fetal heart rate estimation. In other words, some low-quality fetal ECG signal segments are supposed to be detected and removed by utilizing signal quality assessment, so as to improve the accuracy of fetal heart rate estimation. A few supervised learning-based fetal ECG signal quality assessment approaches have been introduced and shown to accurately classify high- and low-quality fetal ECG signal segments, but large fetal ECG datasets with quality annotation are required in these methods. Yet, the labeled fetal ECG datasets are limited. Proposed methods: An unsupervised learning-based multi-level fetal ECG signal quality assessment approach is proposed in this paper for identifying three levels of fetal ECG signal quality. We extracted some features associated with signal quality, including entropy-based features, statistical features, and ECG signal quality indices. Additionally, an autoencoder-based feature is calculated, which is related to the reconstruction error of the spectrograms generated from fetal ECG signal segments. The high-, medium-, and low-quality fetal ECG signal segments are classified by inputting these features into a self-organizing map. Main results: The experimental results showed that our proposal achieved a weighted average F1-score of 90% in three-level fetal ECG signal quality classification. Moreover, with the acceptable removal of detected low-quality signal segments, the errors of fetal heart rate estimation were reduced to a certain extent.

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Heart action monitoring from pulse signals using a growing hybrid polynomial network

Wang L., Zhao C., Mathiopoulos P.T., Ohtsuki T.

Engineering Applications of Artificial Intelligence （Engineering Applications of Artificial Intelligence）  117 2023年01月

ISSN  09521976

　概要を見る

Electrocardiogram (ECG) is a common used noninvasive test to quickly detect the heart problem with high precision. However, devices used to collect continuous ECG waveforms under free-living conditions have several operational difficulties. As an alternative, photoplethysmography (PPG) signals can be conveniently collected by pulse sensors, which can be mounted onto wearable devices. In this paper, we study the relation between ECG waveforms and PPG signals by proposing a novel growing hybrid polynomial network (GHPN)). Based on the projections between adjacent layers, the network is designed to approximate the target signals with the recorded inputs and the multi-scale dynamics of the input series are explored gradually through the growth of layers. Two public datasets are employed to evaluate the performance of the proposed approach on the accuracy of waveform reconstruction and heart rate (HR) detection with the widely used metrics. Compared with the reference ECG waveforms, the normalized mean square error (NMSE) of the proposed approach is 0.248 and 0.216 for PPG-Dalia and CapnoBase datasets, which is smaller than the comparison approaches. The average absolute value (AAE) between the detected HR and reference HR is 0.93 and 1.05 for PPG-Dalia and CapnoBase datasets, which exhibit higher HR detection accuracy. Experimental results obtained from benchmark datasets clearly show that the proposed method can achieve higher similarity on the reconstructed morphology with higher HR detection accuracy. Moreover, the proposed approach make it possible to employ PPG sensors for long-term monitoring of the heart actions with higher precision.

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Communication Efficient Heterogeneous Federated Learning based on Model Similarity

Li Z., Ohtsuki T., Gui G.

IEEE Wireless Communications and Networking Conference, WCNC （IEEE Wireless Communications and Networking Conference, WCNC）  2023-March 2023年

ISSN  15253511

　概要を見る

Federated Learning is now widely used to train neural networks under distributed datasets. One of the main challenges in Federated Learning is to address network training under local data heterogeneity. Existing work proposes that taking similarity into account as an influence factor in federated learning can improve the speed of model aggregation. We propose a novel approach that introduces Centered Kernel Alignment (CKA) into loss function to compute the similarity of feature maps in the output layer. Compared to existing methods, our method enables fast model aggregation and improves global model accuracy in non-IID scenario by using Resnet50.

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A Reinforcement Learning-Based Fire Warning and Suppression System Using Unmanned Aerial Vehicles

Panahi F.H., Panahi F.H., Ohtsuki T.

IEEE Transactions on Instrumentation and Measurement （IEEE Transactions on Instrumentation and Measurement）  72 2023年

ISSN  00189456

　概要を見る

This article discusses the use of unmanned aerial vehicles (UAVs) to detect, warn, and suppress forest fires. The proposed system consists of: 1) an energy harvesting-assisted surveillance UAV (SUAV) that detects and reports fires and 2) multiple ground-based firefighting UAVs (FUAVs) that travel to fire spots to suppress the fire. First, the SUAV carries out the forest fire detection using the onboard color and infrared cameras. The SUAV then uses fire alarm signals to warn/inform the ground-based FUAVs to begin the firefighting mission. The goal is to inform as many FUAVs as possible as the required task (extinguishing the fire) can be completed more effectively and faster with more FUAVs. To do this, the SUAV with onboard processing capabilities makes use of the advantages of reinforcement learning. In particular, we propose a double Q-learning-based trajectory design problem that enables the energy-constrained SUAV to find the optimal sequence of FUAVs to visit (optimal flying trajectory) in order to maximize the number of informed FUAVs while considering the limited execution time, as the crucial issue is to control fire from the beginning to prevent uncontrolled spread of fire.

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Evaluation of Source Data Selection for DTL Based CSI Feedback Method in FDD Massive MIMO Systems

Inoue M., Ohtsuki T., Yamamoto K., Gui G.

Proceedings - IEEE Consumer Communications and Networking Conference, CCNC （Proceedings - IEEE Consumer Communications and Networking Conference, CCNC）  2023-January   182 - 187 2023年

ISSN  23319860

　概要を見る

In frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO), the downlink channel state information (CSI) feedback method based on deep transfer learning (DTL) has been proposed to obtain the downlink CSI at the Base Station (BS). In the CSI feedback method based on DTL, a target model for one channel environment is obtained by fine-tuning the parameters of a source model trained on a large number of the CSI dataset (source data) of another channel environment. The fine-tuning is done with a small number of the CSI dataset (target data) of the target channel environment. Thus, a target model can be obtained at a low learning cost. However, the performance of the target model could highly depend on the source data. In this paper, we investigate two metrics as criteria for selecting source data to obtain a target model with a high CSI reconstruction performance: (i) Jensen-Shannon Divergence (JSD), which represents the similarity between target and source data, and (ii) entropy, which represents the diversity of source data. The simulation results showed when the target channel model is non line-of-sight (NLOS), the source data with high entropy and low JSD tend to provide higher CSI reconstruction performance of the target model. These results indicate that the JSD and the entropy could be a source data selection metric.

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SAR Image Change Detection in Spatial-Frequency Domain Based on Attention Mechanism and Gated Linear Unit

Zhao C., Ma L., Wang L., Ohtsuki T., Mathiopoulos P.T., Wang Y.

IEEE Geoscience and Remote Sensing Letters （IEEE Geoscience and Remote Sensing Letters）  20 2023年

ISSN  1545598X

　概要を見る

Change detection based on synthetic aperture radar (SAR) images is an important application in the remote-sensing technology field. However, the lack of labeled data has been a difficult problem in SAR image detection, especially for pixel-level change detection. In this letter, we propose a novel unsupervised change detection algorithm, which improves the detection accuracy by exploring features from both spatial and frequency domains of SAR images. In particular, first clustering is used as preclassification to obtain pseudo-labels and then by incorporating classifiers and pseudo-labels in terms of feature learning, a novel unsupervised detection algorithm is proposed. To improve the sensitivity of the algorithm to changed details and enhance the antinoise ability of the change detection network, the attention mechanism (AM) is integrated into the network to fully extract important spatial structure information. Moreover, a multidomain fusion module is proposed to integrate spatial and frequency domain features into complementary feature representations. This module contains multiregion features weighted by the channel-spatial AM and deep features filtered out by the gated linear units (GLUs) in the frequency domain. To verify the effectiveness of the proposed algorithm, it is compared against the other four SAR image change detection algorithms using three real datasets. The experimental results show that the proposed method outperforms the other four algorithms in terms of percent correct classification (PCC) and Kappa coefficient (KC).

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Multi-Person Position Estimation Based on Correlation between Received Signals Using MIMO FMCW Radar

Endo K., Ishikawa T., Yamamoto K., Ohtsuki T.

IEEE Access （IEEE Access）  11   2610 - 2620 2023年

　概要を見る

By using Multiple-Input Multiple-Output (MIMO) Frequency Modulated Continuous Wave (FMCW) radar, a range-Angle map can be obtained that displays the signal strength in two dimensions with respect to distance and angular directions. In this process, a threshold algorithm such as Constant False Alarm Rate (CFAR) is used to detect positions where the signal strength exceeds a certain threshold value, enabling position estimation of targets. Furthermore, a method has been proposed to use the curve length of the trajectory of the I/Q signal to emphasize the signal of the person's position on the range angle map. However, the conventional CFAR may cause false alarms when noise and clutter signals are strong. In this paper, we propose a new method for human location estimation using the MIMO FMCW radar. Comparing the reflected signals from people and clutter, we see that I/Q signal variations associated with human motion have little correlation with clutter from stationary objects or noise, and I/Q signal variations associated with clutter often have high correlation with signals at other positions. We focus on the fact that there are many positions where the clutter component signal has a high correlation, compared to the signal caused by a person. Using the correlation map that expresses the correlation between the received I/Q signals on the range-Angle plane, we evaluated the characteristics of noise and clutter components from walls on the correlation map by standard deviation. As a result, we can remove strong noise and clutter components caused from near walls, thus improving the accuracy of object location estimation.

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Max-Min Fair 3D Trajectory Design and Transmission Scheduling for Solar-Powered Fixed-Wing UAV-Assisted Data Collection

Sun C., Xiong X., Zhai Z., Ni W., Ohtsuki T., Wang X.

IEEE Transactions on Wireless Communications （IEEE Transactions on Wireless Communications）  2023年

ISSN  15361276

　概要を見る

This paper presents a new three-dimensional (3D) trajectory design approach for a solar-powered, fixed-wing unmanned aerial vehicle (UAV) to harvest solar energy and collect data from multiple smart devices (SDs). The trajectory is optimized based on max-min fairness to balance the total amount of uploaded data and the fairness among the SDs. The key idea is that we develop non-trivial variable substitution and successive convex approximation (SCA) techniques to convexify data transmission, UAV energy consumption and mobility, and energy harvesting constraints under a persistent round-robin transmission schedule of the SDs. The resulting algorithm guarantees a locally optimal trajectory satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that we further jointly optimize the transmission schedule along with the trajectory, and prove that absolute fairness in terms of uploaded data can be achieved among the SDs under the max-min fairness. The new algorithms apply to both line-of-sight (LoS)-dominant and probabilistic SD-UAV channels. Numerical results show that a 3D trajectory increases the uploaded data by 111%, compared to a two-dimensional (2D) trajectory. The proposed algorithms can balance the energy harvesting and data collection, and achieve fairness in both LoS-dominant and probabilistic SD-UAV channels.

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Investigation of Data Leakage in Deep Learning-based Blood Pressure Estimation using Photoplethysmogram/ Electrocardiogram

Yoshizawa R., Yamamoto K., Ohtsuki T.

IEEE Sensors Journal （IEEE Sensors Journal）  2023年

ISSN  1530437X

　概要を見る

Cuff-less blood pressure (BP) estimation methods using a deep learning model from photoplethysmogram (PPG) or electrocardiogram (ECG) have been actively studied in recent years. However, we found that most previous studies incur data leakage, where segments or records measured from the same subject appear in both the training and test datasets. Furthermore, many previous studies are suspected to have misinterpreted a record in the public dataset used for their evaluations as a subject. To investigate data leakage in BP estimation methods, this paper first organizes previous studies in terms of data leakage. We then quantitatively evaluate the effect of data leakage caused by the segment-level and the record-level train-test split using the public dataset, Cuff-Less Blood Pressure Estimation Data Set. Our experimental results showed that the segment-level split and record-level split erroneously improved the estimation accuracy of mean blood pressure from the (quasi-)subject-level split by the Pearson&#x2019;s correlation coefficient of 0.56 and 0.40 when using PPG, and 0.82 and 0.69 when using ECG, respectively. These results confirmed that the train-test split used in many previous studies, including the one that described its evaluation as causing no data leakage, causes a high level of data leakage, and that a record in the Cuff-Less Blood Pressure Estimation Data Set, often misinterpreted as a subject in previous studies, is not a subject and that a high level of data leakage occurs when the record is considered as a subject.

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Intelligent Cellular Offloading with VLC-enabled Unmanned Aerial Vehicles

Panahi F.H., Panahi F.H., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  2023年

　概要を見る

This paper discusses a cellular network assisted by an energy-and spectral-efficient unmanned aerial vehicle (UAV), in which the UAV is deployed to serve mobile users in the cellular network and enable mobile data offloading from a ground base station (GBS) by taking a circular flight route. We explore a visible light communication (VLC)-enabled UAV, in which a light-emitting diode (LED) is mounted on a rotary-wing UAV to offer communications to the users. Our aim is to simultaneously optimize both energy efficiency (EE) and spectral efficiency (SE) of the VLC-enabled UAV by jointly optimizing the common throughput of all users as well as the UAV&#x2019;s trajectory and flying speed. We employ a unified metric, called resource efficiency (RE), and explore the RE optimization to obtain an adaptive EE-SE tradeoff. The problem posed is seen in a complex and non-convex shape, making it hard to solve. Motivated by the enormous achievement of deep reinforcement learning (DRL) in solving complex control problems, we propose a DRL-based approach to handle this non-convex and complicated optimization. The findings of the simulation reveal that the developed framework achieves a substantial performance in terms of the solution convergence as well as the promising quality of the solutions.

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Intelligent Cellular Offloading with VLC-enabled Unmanned Aerial Vehicles

F. H. Panahi, F. H. Panahi, and T. Ohtsuki

IEEE Internet of Things Journal 2023年

研究論文（学術雑誌）, 最終著者

Detecting Dementia from Face-Related Features with Automated Computational Methods," Bioengineering

C. Zheng, M. Bouazizi,T. Ohtsuki, M. Kitazawa, T. Horigome, and T. Kishimoto

Bioengineering 2023年

研究論文（学術雑誌）, 責任著者

K-Means Clustering-Aided Dynamic Multi-Cell Optimization Algorithm for HAPS

Dai W., Yang S., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    5635 - 5640 2023年

ISSN  23340983

　概要を見る

High Altitude Platform Station (HAPS), functioning as a flying base station (BS) positioned in the stratosphere, has gradually captured more interest in the field of mobile communications. HAPS holds the potential to deliver extensive coverage areas and resilient networks in the face of disasters. Using beamforming techniques, a HAPS can configure multiple cells within its coverage area to serve a large user population. However, the position of HAPS and the distribution of user equipment (UE) changes with time, resulting in changes in the relative position between HAPS and UEs. Consequently, the maximum throughput of UE decreases and some UEs can suffer network outages. A previously proposed method treats this as an optimization problem and tries to maximize throughput based on an objective function. However, the computational complexity is still too high for real-time control. To help beams respond faster to position changes, some AI-based methods are proposed but they cannot guarantee that low throughput UEs are well minimized. In this paper, we propose a K-means clustering-aided particle swarm optimization (PSO) algorithm that can adjust the cell configuration to minimize the number of low throughput UEs. Taking advantage of K-means clustering, this method redefines the search range for each parameter to help PSO quickly converge to the optimum. By running simulations using realistic UE distributions in some cities, we demonstrate the superiority of our proposed method in terms of computational complexity and ability to reduce low throughput UEs.

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Heterogeneous Image Change Detection Based on Deep Image Translation and Feature Refinement-Aggregation

Zhao T., Wang L., Zhao C., Liu T., Ohtsuki T.

Proceedings International Conference on Image Processing Icip    1705 - 1709 2023年

ISSN  15224880

　概要を見る

Remote sensing change detection (CD) has been widely studied, and the CD of heterogeneous images based on cross-sensor acquisition has significant research significance. However, the scarcity of heterogeneous data and the difficulty in obtaining high-quality change maps remain significant challenges. To address these issues, we propose a deep image translation-based feature refinement-aggregation change detection network (FRAN) designed for heterogeneous images, such as optics and SAR images. First, we use data augmentation to increase the number of available images and a no-independent-component-for-encoding GAN (NICE-GAN) to translate the features from the optical domain to the SAR image domain, enabling direct comparison of images from different domains. Finally, we introduce feature refinement module and feature aggregation module to extract more accurate change information and obtain an accurate change region. Our experiments on two public datasets demonstrate that the proposed FRAN's re-detection accuracy is superior to that of four other heterogeneous detection methods.

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HAPS Trajectory Optimization Based on Throughput Heat Map

Mori T., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    7291 - 7296 2023年

ISSN  23340983

　概要を見る

High altitude platform station (HAPS) is a platform that provides direct communication to wireless devices such as smart phones by equipping unmanned aerial vehicles (UAVs) flying in the stratosphere with cellular base station functions. In this paper, we propose a HAPS trajectory optimization method based on a throughput heat map, where the HAPS trajectory is designed considering the trajectory constraints of HAPS as an aircraft. The HAPS trajectory is optimized according to the throughput characteristics to be improved, such as the average throughput of each UE (user equipment). Computer simulations using actual UE distributions for Tokyo, Osaka, and Nagoya showed that the sub-optimized figure-8 trajectory improved the 50th percentile throughput by 15-16% and the 5th percentile throughput by 3-20% for each city, respectively, compared to the non-optimized circular trajectory. Trajectories aimed at improving the 50th, 75th, and 95th percentiles improved throughput characteristics around the 50th percentile, around the 75th-95th percentile, and above the 96th percentile, respectively.

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HAPS Cell Design Method for Coexistence on Terrestrial Mobile Networks

Shibata Y., Takabatake W., Hoshino K., Nagate A., Ohtsuki T.

IEEE Vehicular Technology Conference 2023-June 2023年

ISSN  15502252

　概要を見る

High-altitude platform stations (HAPSs), which directly deliver communication services to smartphones on the ground, are attracting much attention as novel mobile communication platforms for ultrawide coverage areas. A multi-cell configuration is needed to enhance communication capacity in wide areas. Assuming the use of a phased-array antenna, we proposed an optimization method for controlling antenna parameters, such as beamwidth and beam direction, to improve spectral efficiency. HAPSs are also promising disaster-resilient networks, as they can recover coverage immediately when coverage holes appear due to the breaking down of terrestrial base stations (BSs). Users can recover connections immediately during disasters through HAPSs that use the same frequency band as terrestrial networks. However, in this scenario, HAPSs become a source of interference in terrestrial networks, so network operators should avoid affecting existing terrestrial networks through HAPSs. However, cell optimization in this coexistence scenario between HAPSs and terrestrial networks has not been investigated thoroughly. In this paper, we propose a design scheme that optimizes antenna parameters for each cell to maximize HAPS coverage in this coexistence scenario using a genetic algorithm (GA). The proposed method can effectively protect terrestrial networks through optimization by setting the initial direction of each beam to avoid terrestrial BSs. Using this scheme, the GA can start optimization with candidate combinations that satisfy the interference constraints of terrestrial networks, which can expedite optimization for better coverage. Simulation results show that the proposed scheme can achieve better coverage than the conventional scheme, which only controls beam direction. Furthermore, they show that the initial beam direction setting effectively reduces the number of performed combinations required for GA to converge.

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Dynamic Antenna Control for HAPS Using Mean Field Reinforcement Learning in Multi-Cell Configuration

Yang S., Bouazizi M., Ohtsuki T.

IEEE International Conference on Communications 2023-May   5670 - 5676 2023年

ISSN  15503607

　概要を見る

In this paper, we propose a Mean Field reinforcement learning (MFRL) method for dynamic antenna control in High-Altitude-Platform-Station (HAPS) communication system with Multi-Cell Configuration. HAPS works at stratospheric altitudes of about 20 km to provide an ultra-wide coverage area. However, the wind pressure caused HAPS movement leads to the degradation of users' throughput. Considering the multi-antenna arrays in the HAPS, to find the optimal antenna parameters of all antenna arrays for reducing the number of low-throughput users, we formulate the antenna control problem into stochastic game equilibrium. Usually, solving the stochastic to find the equilibrium needs very high computation complexity to calculate the transition probability for getting the Q-value under a certain state and action. Therefore, we use the reinforcement learning (RL) named Deep Q-Network (DQN) to learn the transition probability and predict the Q-value according to the reward fed backed from the environment. Besides, we employ the Mean field Game theory in conjunction with RL during the training phase of DQN to reduce the complexity of the interactions among agents. To evaluate the proposed method, we compare the proposed method with a genetic algorithm (GA) named Particle Swarm Optimization (PSO), Q-learning, Fuzzy Q-learning, and conventional DQN under four realistic user distribution scenarios. The simulation results show that the proposed method achieves comparable throughput performance with a high convergence rate.

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Depression Detection via User Behavior and Tweets

Ji N., Bouazizi M., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    1985 - 1990 2023年

ISSN  23340983

　概要を見る

Depression is a common mental illness and the second leading cause of disability worldwide. Traditional depression diagnosis requires communication with patients and subjective cooperation of patients, which consumes a lot of manpower, material resources, and time costs. With the accumulation of user data in social media and the development of natural language processing, computer-aided diagnosis is realized, better and objective analysis is provided, and a new idea for the diagnosis of depression is provided. We propose a multimodal model based on EmoBERTa and Transformer and a text preprocessing method for a specific pre-trained model and application context. We use user behavior information and past tweets to detect whether a user has depression. Our model achieves state-of-the-art performance on a publicly available multimodal Twitter dataset.

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Deep Reinforcement Learning Aided Online Trajectory Optimization of Cellular-Connected UAVs with Offline Map Reconstruction

Hao Q., Zhao H., Huang H., Gui G., Ohtsuki T., Adachi F.

IEEE Vehicular Technology Conference 2023-June 2023年

ISSN  15502252

　概要を見る

To reduce the outage of the connection between unmanned aerial vehicles (UAVs) and cellular networks in complex real-time channel state, and reduce the energy consumption of UAV during flight mission, an online trajectory optimization scheme of UAV based on outage probability knowledge map reconstruction is proposed. The outage probability knowledge map is a database that simulates the connection between UAV and the cellular network during real hovers. The UAV first samples sparsely from the target area and calculates the outage probability of the sampling point, and then uses the Kriging algorithm to reconstruct the outage probability knowledge map. Based on the reconstructed outage probability knowledge map, with the goal of minimizing the energy consumption of UAV task execution, the UAV trajectory optimization problem is established, and a trajectory optimization algorithm based on deep reinforcement learning (DRL) is proposed to solve it. Numerical results show that the proposed online trajectory optimization scheme based on outage probability knowledge map can obtain great returns in terms of maintaining connectivity, reducing task completion time and energy consumption.

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Automatic Modulation Classification Based on Decentralized Learning and Model Averaging

Xu Y., Ma M., Fu X., Wang Y., Gui G., Ohtsuki T.

IEEE INFOCOM 2023 Conference on Computer Communications Workshops INFOCOM Wkshps 2023 2023年

　概要を見る

Automatic modulation classification (AMC) is a promising technology to identify the modulation mode of the received signal in non-cooperative communication scenarios. Recently, benefitting from the outstanding classification performance of deep learning (DL), various deep neural networks (DNNs) have been introduced into AMC methods. Most current AMC methods are based on local framework (LocalAMC) where there is only one device, or centralized framework (CentAMC) where multiple local devices (LDs) upload their data to the only one central server (CS). LocalAMC may not get ideal results due to insufficient data and finite computational power. CentAMC carries a significant risk of privacy leakage and the final data for training model in CS is quite massive. In this paper, we propose an AMC method based on decentralized learning with residual network (ResNet). Simulation results show that ResNet-based decentralized AMC (DecentAMC) method achieves similar classification performance to CentAMC while improving training efficiency and protecting data privacy.

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Arterial Blood Pressure Waveform Estimation from Photoplethysmogram Under Inter-Subject Paradigm Using Subject-Distinguishable Dataset by U-Net and Domain Adversarial Training

Yoshizawa R., Yamamoto K., Ohtsuki T.

IEEE International Conference on Communications 2023-May   3401 - 3406 2023年

ISSN  15503607

　概要を見る

Blood pressure (BP) estimation methods using photoplethysmogram (PPG) based on deep learning models have been actively studied. These methods are also the basis of non-contact BP estimation methods using a camera or a Doppler radar. However, most previous studies are under data leakage, where subjects are not separated between training and test data. In this paper, we propose a method for BP estimation from PPG under the condition that subjects are separated between training and test data (inter-subject paradigm) using a subject-distinguishable large public dataset. Our BP estimation method estimates an 8-second BP waveform called arterial BP (ABP) from an 8-second PPG segment using U-Net. From the estimated ABP, systolic BP (SBP), diastolic BP (DBP), and mean BP (MBP) are calculated, which are the discrete single values for the 8-second ABP. In addition, we apply domain adversarial training, which facilitates the BP estimation model to extract subject-invariant features to improve the BP estimation accuracy under the inter-subject paradigm. Moreover, we use a considerably larger amount of training data than the previous studies and evaluate our model with smaller model sizes for better generalizability. Our experimental results showed that our method can estimate BP with moderate accuracy under the inter-subject paradigm, particularly MBP. The mean absolute errors for the estimated SBP, DBP, MBP, and ABP were 15.21, 7.12, 8.20, and 10.14 mmHg, respectively. The Pearson's correlation coefficients between the true and estimated values were 0.49, 0.39, 0.54, and 0.84, respectively.

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An LSTM-Based Approach for Fall Detection Using Accelerometer-Collected Data

Uotani Y., Yamamoto K., Ye C., Bouazizi M., Ohtsuki T.

Proceedings 2023 28th Asia Pacific Conference on Communications Apcc 2023    250 - 255 2023年

　概要を見る

Over the past few years, there has been a significant rise in the number of fall accidents occurring among elderly individuals, a problem that has been accentuated with to the aging population. Researchers and developers have focused their efforts on investigating and creating various fall detection methods that utilize an accelerometer. However, conventional fall detection methods typically target specific positions where accelerometers are placed. In addition, they suffer from low accuracy which can be attributed to the fact that the classification algorithms commonly employed, such as the support vector machine (SVM) and the random forest (RF), are not specialized in making predictions based on time series data. In this paper, we propose the fall detection method based on a long short-term memory (LSTM) neural network, using an accelerometer. In the proposed method, four kinds of possession positions are set: (i) in hand, (ii) inside a chest pocket, (iii) inside a waist pocket, and (iv) in a bag. The acceleration data collected are classified using the LSTM classifies into one of four classes: (i) standing, (ii) walking, (iii) falling, and (iv) lying down. The results of the multi-class classification are further reclassified into two classes, i.e., fall and non-fall. The experimental results demonstrate that our approach outperforms the conventional methods in terms of fall detection accuracy.

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Adversarial Learning Aided Malicious Network Traffic Detection Based on EDE Framework

Zhang L., Wang Y., Ohtsuki T., Adebisi B., Wu H.C., Gui G.

2023 IEEE Cic International Conference on Communications in China Iccc 2023 2023年

　概要を見る

With the increasing number of new malicious software and network attacks, anomalous network traffic detection technology needs to continuously identify unknown attacks in order to adapt to complex network environments. In this paper, we propose a novel adversarial learning aided malicious network traffic detection (AMD) method based on encoder-decoder-encoder (EDE) framework. The proposed AMD method detects unknown anomalies in a single type of semi-supervised form and converts network traffic data into pictures for later processing. First, an anomalous network traffic detection model consisting of two encoders, a decoder and a discriminator is introduced. Second, by using the visibility of normal samples and the unrecognizability of unknown anomaly samples, the difference between the two samples is enlarged to facilitate effective sample detection. Finally, experiments show that the proposed model detects anomalies in targeted space and is robust to complex and noisy network environments. At the same time, compared to traditional machine learning methods, AMD's shows performance improvement of up to 40%.

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A Robust Multi-Frame mmWave Radar Point Cloud-Based Human Skeleton Estimation Approach with Point Cloud Reliability Assessment

Shi X., Ohtsuki T.

Proceedings of IEEE Sensors 2023年

ISSN  19300395

　概要を見る

Millimeter-Wave (mmWave) radar-based skeleton estimation has gained significant attention in the field of human motion analysis and sensing. It offers distinct advantages over RGB camera-based, depth camera-based, and inertial sensor-based approaches. It operates independently of lighting conditions. Additionally, it overcomes limitations of depth camera-based methods such as reflections and occlusions, and provides precise real-time tracking, enhancing its overall performance. However, existing human skeleton estimation methods utilizing point cloud data mostly rely on single-frame inputs or incorporate voxelization, which introduces drawbacks. This paper proposes a novel Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM)-based model for multi-frame point cloud data without voxelization. Furthermore, an Long Short-Term Memory (LSTM)-based neural network is introduced to assess point cloud reliability, enhancing robustness. Experimental results demonstrate improved accuracy and robustness in human skeleton estimation compared with the conventional single frame-based methods.

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A Novel Approach for Activity, Fall and Gait Detection Using Multiple 2D LiDARs

Bouazizi M., Feghoul K., Lorite A., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    1997 - 2002 2023年

ISSN  23340983

　概要を見る

A key concept in health monitoring systems for elderly people is the continuous and non-intrusive detection of their activities to identify when hazardous events such as sudden falling occur/are about to occur. The existence of obstacles in the environment largely limits the detection performance of existing approaches of activity detection relying on non-contact sensors. A simple, yet effective, approach to address this issue is the use of multiple sensors which collaborate with one another. In this paper, we propose an approach that relies on 2D Light Detection and Ranging (LiDAR) technology for activity detection. We employ multiple 2D LiDARs placed at different locations in a single room with difference obstacles (e.g., furniture) and working in coordination to construct a fuller representation of the activities being performed. Our approach transforms the concatenation of the different LiDAR data into a more comprehensible data format (i.e., images). The generated images are then processed using a Convolutional LSTM Neural Network to perform the classification. For 3 different tasks, namely activity detection, fall detection, and unsteady gate detection, our proposed approach reaches an accuracy equal to 96.10%, 99.13% and 93.13%, respectively.

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A GAN-Based Approach for ECG Reconstruction from Doppler Sensor Signals

Bouazizi M., Yu D., Feghoul K., Ohtsuki T.

Proceedings IEEE Global Communications Conference Globecom    3867 - 3872 2023年

ISSN  23340983

　概要を見る

An Electrocardiograms (ECG) is a recording of the heart's electrical activity. It can help detect problems with one's heart rate or heart rhythm. Traditional methods to measure and collect ECG are not practical for daily use due to their invasive nature and inaccessibility. Thus, a less intrusive and more accessible method is needed. In this paper, we propose a method that uses attention-based Wasserstein Generative Adversarial Networks-Gradient Penalty (aWGAN-GP) to reconstruct ECG signals from ones collected using a Doppler sensor. GANs can capture pertinent features in the Doppler signals to enable such reconstruction. Our approach uses the integrated spectrum of the Doppler signal to identify R-peaks, which are then employed to train the aWGAN-GP to reconstruct the ECG signal. We evaluated this method on 19 healthy subjects and obtained a correlation coefficient of 0.88 between the reconstructed and actual ECG signals, outperforming the conventional CNN-based method which reached only 0.35. Our results demonstrate that it is possible to reconstruct an ECG signal from a heartbeat signal collected via a Doppler sensor using aWGAN-GP.

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A Few-Shot Malware Traffic Classification Method Using Knowledge Transfer Learning

Qin M., Zhang X., Wang Y., Ohtsuki T., Sari H., Gui G.

International Conference on Communication Technology Proceedings ICCT    15 - 19 2023年

ISSN  25767844

　概要を見る

Malware traffic classification (MTC) has become one of the important tasks to protect network and system security. However, it is a difficult task to obtain large-scale labeled malware traffic data in practice due to the wide variety and ever-changing malware. Existing MTC techniques require a large number of samples for training, the classification performance is poor when the training samples are few. Therefore, classification techniques based on few-shot learning (FSL) have attracted much attention in MTC. In this paper, we propose a few-shot MTC method based on knowledge transfer learning. Specifically, generic malware feature representation can be learned by pre-training the deep learning model on source tasks. Then, the parameters of the pre-trained model are used as initial parameters to fine-tune on the target task to adapt to feature distribution of the target task. The simulation results show that the few-shot MTC method based on knowledge transfer learning proposed in this paper has a 20-shot average accuracy of 95.23% when using GoogLeNet network as the backbone network, compared with CNN, VGG, and ResNet models, GoogLeNet has the best performance.

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Multilayer Attention Mechanism for Change Detection in SAR Image Spatial-Frequency Domain

Ma L., Wang L., Zhao C., Jiahui E., Ohtsuki T.

Proceedings International Conference on Image Processing Icip    2110 - 2114 2023年

ISSN  15224880

　概要を見る

Change detection based on synthetic aperture radar (SAR) images is a challenging task in the field of remote sensing image analysis due to the influence of noise and the lack of labeled data. In this paper, we propose a new unsupervised change detection algorithm based on deep learning, which explores the spatial and frequency domain features of SAR images in parallel to improve detection performance. Our proposed method first obtains pseudo-labels by clustering and then combines them with neural networks for unsupervised detection. To reduce the impact of noise and improve sensitivity to changes, we integrate an attention mechanism (AM) into the network. We also use complementary features to integrate the spatial and frequency domain features. These complementary features include a multi-regional feature weighted by channel-spatial AM and a deep feature filtered out by a gated linear unit (GLU). Experimental results demonstrate that the proposed method improves the detection accuracy.

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Semi-Supervised Malware Traffic Classification Via Consistency Regularization and Pseudo-Labeling

Jiao Q., Zhang X., Zhang W., Miao F., Ohtsuki T., Adebisi B., Wang Y., Gui G.

International Conference on Communication Technology Proceedings ICCT    1008 - 1012 2023年

ISSN  25767844

　概要を見る

Malware traffic classification (MTC) technology plays a crucial role in cyber security by serving as the first step of network intrusion detection system. Recently, deep learning has been successfully introduced into the field of network intrusion detection. However, its performance relies heavily on the quantity and quality of labeled samples, which are often difficult to obtain in real-world applications. To address these challenges, we propose semi-supervised MTC method based on consistency regularization and pseudo-labeling. This method utilizes unlabeled malware traffic data to improve the classification performance of the model. Specifically, we train the model using labeled malware traffic data. Then, weak data augmentation is performed on unlabeled traffic data and pseudo labels are generated through the model. Finally, the model predicts unlabeled data after strong data augmentation and compares it with pseudo labels. Model parameters are updated using losses from labeled and unlabeled samples. Experimental results demonstrate that our proposed method outperforms other classical classification approaches, including ladder networks, virtual adversarial training (VAT), convolutional neural network (CNN), and support vector machine (SVM), when the ratios of labeled samples range from 1% to 5%.

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Radio Frequency Signal Dataset Generation Based on LTE System and Variable Channels

Zhang S., Zhang Y., Zhang X., Peng Y., Sun J., Gui G., Ohtsuki T.

IEEE INFOCOM 2023 Conference on Computer Communications Workshops INFOCOM Wkshps 2023 2023年

　概要を見る

Deep learning-based radio frequency fingerprinting (RFF) identification has the potential to enhance the security performance of the physical layer. In recent years, a number of RFF datasets have been proposed to meet the large-scale data requirements for deep learning. However, these datasets are collected from similar channel environments and only contain receiver data. This paper employs different software radio peripherals to generate radio signals. Hence, it is able to adjust the signal's parameters, such as frequency band, modulation style, antenna gain, etc. In this paper, we propose a radio frequency signal dataset based on LTE system and variable channels to more properly characterize the generated signals in the real world. We collect signals at transmitters and receivers to construct the RFF dataset. Moreover, we confirm the dataset's dependability using various machine learning and deep learning methods. The dataset and reproducible code of this paper can be downloaded from GitHub11GitHub link: https://github.com/njuptzsp/XSRPdataset.

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Radio Frequency Fingerprint Identification Based on Length-Robust Representation and Incremental Learning

Shi F., Feng Z., Miao F., Fu X., Adebisi B., Ohtsuki T., Sari H., Gui G.

International Conference on Communication Technology Proceedings ICCT    236 - 240 2023年

ISSN  25767844

　概要を見る

Radio frequency fingerprinting identification (RFFI) is a technology that uses signal processing techniques to extract the characteristics of collected wireless signals to enable the individual identification of devices. Over the past few years, deep learning (DL) has made great achievements in the field of signal identification, and the research on the RFFI methods has made great progress. To address the issue of fixed-size input data required by many neural networks, such as convolutional neural network (CNN) and multilayer perceptron (MLP), this paper proposes a transformation method that converts variable-length signals into contour stella constellation (CSC). In addition, in the most DL-based the RFFI methods, a global analysis of the entire radio frequency signal is required, which demands a large amount of calculation and is easily affected by changes in the propagation environment. We propose an incremental learning (IL)-based the RFFI approach, which enables dynamic updating of the model and improves its identification and generalization capabilities. Experimental results show that our proposed method shows excellent performance in processing data streams in real time and the identification accuracy finally can reach 89.14%. The codes of this paper can be downloaded from https://github.com/sfuaena/CSC-KD.

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Non-Contact Blood Pressure Estimation Using Accurate Cardiac Movements Extracted by Hidden Semi-Markov Model

Wang S., Boazizi M., Ohtsuki T.

IEEE International Conference on Communications 2023-May   5508 - 5514 2023年

ISSN  15503607

　概要を見る

This paper presents a radar-based, non-contact Blood Pressure (BP) estimation model based on accurate detection of cardiac activities, which enables BP monitoring to be performed in a touch-free and continuous manner. Cardiac movements have been regarded as essential factors for BP estimation. However, accurately obtaining these movements by a radar system remains a challenging problem because these movements are too inconspicuous and could be easily hindered by respiration and random noise. In this paper, we propose a method that mainly focuses on cardiac feature extraction in radar-based BP monitoring. First, we employ an integrated-spectrum waveform. It is derived from short-time Fourier transform (STFT) and is capable of recording and preserving minor cardiac activities. Compared with the pulse-wave signals used in previous works, the integrated-spectrum focuses on energy changes introduced by short and high-frequency vibrations. It can eliminate the interference of respiration and random noise, and cardiac contractile movement can be reserved accurately. Second, we propose a cardiac features estimation method in which a hidden semi-Markov model (HSMM) is applied to the integrated-spectrum for feature extraction. Compared to the pulse wave signal, the Root-Mean-Square Error (RMSE) of the estimated interbeat intervals (IBI), Systolic time, and Diastolic time is reduced by 44.2%, 73.3%, and 76.7% respectively. The estimated accurate cardiac features are further used as inputs for a Random Forest model for BP prediction. Though previous work required the subject to hold one's breath, we achieved a comparable prediction accuracy even when our subject is breathing normally. The Diastolic BP (DBP) error of our model is 4.27± 5.84 mmHg (Mean Absolute Difference ± Standard Deviation), and the Systolic BP (SBP) error is 6.63± 8.95 mmHg.

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NASEI: Neural Architecture Search-Based Specific Emitter Identification Method

Huang Y., Zhang X., Wang Y., Jiao D., Gui G., Ohtsuki T.

IEEE Vehicular Technology Conference 2023-June 2023年

ISSN  15502252

　概要を見る

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, these methods highly rely on expert experience to design network structures. These hand-designed fixed network structures lack flexibility, which often leads to insufficient model generalization. Neural architecture search (NAS) can be seen as a subdomain of automatic machine learning (AutoML), which can automatically adjust network structure and parameters according to a specific task. In this paper, we propose a neural architecture search-based SEI method, which can achieve an efficient search of the architecture with the use of a gradient descent algorithm. Experimental results show that the proposed NASEI method both improves the accuracy and reduces the parameter quantity when compared with state-of-the-art methods. Code available at https://github.com/huangyuxuan11/NASEI.git.

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Edge Device Identification Based on Federated Learning and Network Traffic Feature Engineering

He Z., Yin J., Wang Y., Gui G., Adebisi B., Ohtsuki T., Gacanin H., Sari H.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  8 （ 4 ） 1898 - 1909 2022年12月

　概要を見る

With the ubiquitous deployment and applications of Internet of Things (IoT), security issues pose a critical challenge to IoT devices. External attackers often utilize vulnerable IoT devices to invade the target's internal network and then further cause a security threat to the whole network. To prevent such attacks, it is necessary to develop a security mechanism to control the access of suspicious IoT devices and manage the internal devices. In recent years, deep learning (DL) algorithm has been widely used in the field of edge device identification (EDI), and has made great achievements. However, these previous methods are essentially centralized learning-based EDI (CentEDI) that trains all data together, which can not guarantee data security and not conducive to deployment on edge devices. To address this problem, we introduce a federated learning-based EDI (FedeEDI) method via network traffic to automatically identify edge devices connected to the whole network. Experimental results show that the training efficiency of our proposed FedeEDI method is much higher than that of the CentEDI method, although its classification accuracy is slightly reduced. In contrast to the CentEDI method, the proposed FedeEDI method has two main advantages: faster training speed and safer training process.

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Make Smart Decisions Faster: Deciding D2D Resource Allocation via Stackelberg Game Guided Multi-Agent Deep Reinforcement Learning

Shi D., Li L., Ohtsuki T., Pan M., Han Z., Poor H.V.

IEEE Transactions on Mobile Computing （IEEE Transactions on Mobile Computing）  21 （ 12 ） 4426 - 4438 2022年12月

ISSN  15361233

　概要を見る

Device-to-Device (D2D) communication enabling direct data transmission between two mobile users has emerged as a vital component for 5G cellular networks to improve spectrum utilization and enhance system capacity. A critical issue for realizing these benefits in D2D-enabled networks is to properly allocate radio resources while coordinating the co-channel interference in a time-varying communication environment. In this paper, we propose a Stackelberg game (SG) guided multi-agent deep reinforcement learning (MADRL) approach, which allows D2D users to make smart power control and channel allocation decisions in a distributed manner. In particular, we define a crucial Stackelberg Q-value (ST-Q) to guide the learning direction, which can be calculated based on the equilibrium achieved in the Stackelberg game. With the guidance of the Stackelberg equilibrium, our approach converges faster with fewer iterations than the general MADRL method and thereby exhibits better performance in handling the network dynamics. After the initial training, each agent can infer timely D2D resource allocation strategies with distributed execution. Extensive simulations are conducted to validate the efficacy of our proposed scheme in developing timely resource allocation strategies. The results also show that our method outperforms the general MADRL based approach in terms of the average utility, channel capacity, and training time.

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A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar

Endo K., Yamamoto K., Ohtsuki T.

Sensors （Sensors）  22 （ 23 ）  2022年12月

ISSN  14248220

　概要を見る

A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power that exceeds a threshold using an algorithm, such as Constant False Alarm Rate (CFAR). However, noise and multipath components often exist over the range-angle map, which could produce false alarms for an undesired location depending on the threshold setting. In other words, the threshold setting is sensitive in noisy range-angle maps. Therefore, if the noise is reduced, the threshold can be easily set to reduce the number of false alarms. In this paper, we propose a method that improves the CFAR threshold tolerance by denoising a range-angle map using Deep Image Prior (DIP). DIP is an unsupervised deep-learning technique that enables image denoising. In the proposed method, DIP is applied to the range-angle map calculated by the Curve-Length (CL) method, and then the object location is detected over the denoised range-angle map based on Cell-Averaging CFAR (CA-CFAR), which is a typical threshold setting algorithm. Through the experiments to estimate human locations in indoor environments, we confirmed that the proposed method with DIP reduced the number of false alarms and estimated the human location accurately while improving the tolerance of the threshold setting, compared to the method without DIP.

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Constructing Mobile Crowdsourced COVID-19 Vulnerability Map With Geo-Indistinguishability

Chen R., Li L., Ma Y., Gong Y., Guo Y., Ohtsuki T., Pan M.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 18 ） 17403 - 17416 2022年09月

　概要を見る

Preventing COVID-19 disease from spreading in communities will require proactive and effective healthcare resource allocations, such as vaccinations. A fine-grained COVID-19 vulnerability map will be essential to detect the high-risk communities and guild the effective vaccine policy. A mobile-crowdsourcing-based self-reporting approach is a promising solution. However, an accurate mobile-crowdsourcing-based map construction requests participants to report their actual locations, raising serious privacy concerns. To address this issue, we propose a novel approach to effectively construct a reliable community-level COVID-19 vulnerability map based on mobile crowdsourced COVID-19 self-reports without compromising participants' location privacy. We design a geo-perturbation scheme where participants can locally obfuscate their locations with the geo-indistinguishability guarantee to protect their location privacy against any adversaries' prior knowledge. To minimize the data utility loss caused by location perturbation, we first design an unbiased vulnerability estimator and formulate the location perturbation probability generation into a convex optimization. Its objective is to minimize the estimation error of the direct vulnerability estimator under the constraints of geo-indistinguishability. Given the perturbed locations, we integrate the perturbation probabilities with the spatial smoothing method to obtain reliable community-level vulnerability estimations that are robust to a small-sampling-size problem incurred by location perturbation. Considering the fast-spreading nature of coronavirus, we integrate the vulnerability estimates into the modified susceptible-infected-removed (SIR) model with vaccination for building a future trend map. It helps to provide a guideline for vaccine allocation when supply is limited. Extensive simulations based on real-world data demonstrate the proposed scheme superiority over the peer designs satisfying geo-indistinguishability in terms of estimation accuracy and reliability.

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Wi-Fi-Based Fall Detection Using Spectrogram Image of Channel State Information

Nakamura T., Bouazizi M., Yamamoto K., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 18 ） 17220 - 17234 2022年09月

　概要を見る

Wi-Fi channel state information (CSI)-based fall detection systems have a great potential compared with other alternatives since they are nonintrusive and nonspace limited. However, in the conventional work on Wi-Fi CSI-based fall detection, a phenomenon is commonly observed: the classification performance degrades when data in different environments are used for learning and testing. Nonetheless, when the signal-to-noise-power ratio (SNR) is small, the conventional methods cannot capture features of motion and cannot segment signals accurately. Therefore, there is a need to address these problems in order to build a robust fall detection system. In this article, we propose a spectrogram-image-based fall detection using Wi-Fi CSI. Unlike the conventional method, CSI is segmented with a certain sliding-time window, and then the classifier detects fall by using the spectrogram image generated from the segmented CSI. We use a pretrained convolutional neural network (CNN) optimized for binary classification of the spectrogram images of the fall and nonfall motions. We carried out experiments to evaluate the classification performance of our proposed method against the conventional one by using motion data in two different rooms for learning and testing. As a result, we confirmed that our proposed method outperforms the conventional one and reaches over 0.92 accuracy. In addition, compared with the conventional method, the fall detection performance of our method does not degrade even when using different environment data for learning and testing.

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Sub-GHz Two-Way Ranging Based on Phase Detection for Remote Keyless Entry Systems

Seto I., Otaka S., Yoshida H., Nonin K., Nishikawa M., Kato T., Nito Y., Ishiwata H., Otsuki T.

IEEE Transactions on Vehicular Technology （IEEE Transactions on Vehicular Technology）  71 （ 9 ） 9705 - 9720 2022年09月

ISSN  00189545

　概要を見る

Remote keyless entry systems have penetrated the vehicular market due to its convenience. However, its vulnerability against relay attacks exploiting the wireless channel is a serious threat. One of the prominent countermeasures is radio ranging between the key fob and the vehicle. Taking into account radio regulations, usability and power consumption among other things, a suitable approach is necessary to devise. This article proposes sub-GHz two-way ranging based on phase detection which offers high tolerance against reference clock offset between a pair of transceiver devices and a novel compensation scheme against multipath effects. We present the operation principle and theoretical performance under a two-path model of radio propagation and additive white Gaussian noise. In field experiments with prototype devices, the ranging accuracy is validated. We show that the proposed ranging produces sufficient performance for remote keyless entry systems, confirming if the key fob is within a few meters of the vehicle.

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Inter-User Distance Estimation Based on a New Type of Fingerprint in Massive MIMO System for COVID-19 Contact Detection

Yang S., Bouazizi M., Cao Y., Ohtsuki T.

Sensors （Sensors）  22 （ 16 ）  2022年08月

ISSN  14248220

　概要を見る

In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users’ location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m2. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users’ location information.

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2-D LIDAR-Based Approach for Activity Identification and Fall Detection

Bouazizi M., Ye C., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 13 ） 10872 - 10890 2022年07月

　概要を見る

Activity detection is a key task in the monitoring of elderly people living alone. This is because it helps locate them and identify any accident that might occur to them. In this article, we propose a novel approach that uses 2-D light detection and ranging (LIDAR) and deep learning to perform activity detection. In a first step, our approach processes and interpolates the data collected using the 2-D LIDAR following an algorithm we propose to locate the person and identify the useful data points. In the next steps, the data are transformed into two types of representations: 1) a time-series type and 2) an image type. The time-series data are used to train different long short-term memory (LSTM) networks to identify the person and to recognize his/her activity, while the image type is used to fine-tune a convolutional neural network (CNN) for fall detection. Throughout our experiments, we show that our approach allows for the identification of people from their gait, and the detection of unsteady gait or unstable walk (i.e., when the person is about to fall or feeling dizzy) as well as the detection of up to four activities: 1) walking; 2) standing; 3) sitting; and 4) falling. The results obtained from our experiment show that the proposed method reaches an accuracy equal to 94.1% for multiclass activity detection, 98.6% for fall detection, 93.2% for person identification (for three different people), and 92.5% for unsteady walk detection.

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A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things

Zhao R., Gui G., Xue Z., Yin J., Ohtsuki T., Adebisi B., Gacanin H.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  9 （ 12 ） 9960 - 9972 2022年06月

　概要を見る

The purpose of a network intrusion detection (NID) is to detect intrusions in the network, which plays a critical role in ensuring the security of the Internet of Things (IoT). Recently, deep learning (DL) has achieved a great success in the field of intrusion detection. However, the limited computing capabilities and storage of IoT devices hinder the actual deployment of DL-based high-complexity models. In this article, we propose a novel NID method for IoT based on the lightweight deep neural network (LNN). In the data preprocessing stage, to avoid high-dimensional raw traffic features leading to high model complexity, we use the principal component analysis (PCA) algorithm to achieve feature dimensionality reduction. Besides, our classifier uses the expansion and compression structure, the inverse residual structure, and the channel shuffle operation to achieve effective feature extraction with low computational cost. For the multiclassification task, we adopt the NID loss that acts as a better loss function to replace the standard cross-entropy loss for dealing with the problem of uneven distribution of samples. The results of experiments on two real-world NID data sets demonstrate that our method has excellent classification performance with low model complexity and small model size, and it is suitable for classifying the IoT traffic of normal and attack scenarios.

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Fully Convolutional Neural Network-Based CSI Limited Feedback for FDD Massive MIMO Systems

Fan G., Sun J., Gui G., Gacanin H., Adebisi B., Ohtsuki T.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  8 （ 2 ） 672 - 682 2022年06月

　概要を見る

Due to the lack of channel reciprocity in frequency division duplexity (FDD) massive multiple-input multiple-output (MIMO) systems, it is impossible to infer the downlink channel state information (CSI) directly from its reciprocal uplink CSI. Hence, the estimated downlink CSI needs to be continuously fed back to the base station (BS) from the user equipment (UE), consuming valuable bandwidth resources. This is exacerbated, in massive MIMO, with the increase of the antennas at the BS. This paper propose a fully convolutional neural network (FullyConv) to compress and decompress the downlink CSI. FullyConv will improve the reconstruction accuracy of downlink CSI and reduce the training parameters and computational resources. Besides, we add a quantization module in the encoder and a dequantization module in the decoder of the FullyConv to simulate a real feedback scenario. Experimental results demonstrate that the proposed FullyConv is better than the baseline on reconstruction performance and reduction of the storage and computational overhead. Furthermore, the FullyConv added quantization and dequantization modules is robust to quantization error in real feedback scenarios.

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An Infrared Array Sensor-Based Approach for Activity Detection, Combining Low-Cost Technology with Advanced Deep Learning Techniques

Arumugasamy M.K., Bouazizi M., Ohtsuki T.

Sensors （Sensors）  22 （ 10 ）  2022年05月

ISSN  14248220

　概要を見る

In this paper, we propose an activity detection system using a 24 × 32 resolution infrared array sensor placed on the ceiling. We first collect the data at different resolutions (i.e., 24 × 32, 12 × 16, and 6 × 8) and apply the advanced deep learning (DL) techniques of Super-Resolution (SR) and denoising to enhance the quality of the images. We then classify the images/sequences of images depending on the activities the subject is performing using a hybrid deep learning model combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM). We use data augmentation to improve the training of the neural networks by incorporating a wider variety of samples. The process of data augmentation is performed by a Conditional Generative Adversarial Network (CGAN). By enhancing the images using SR, removing the noise, and adding more training samples via data augmentation, our target is to improve the classification accuracy of the neural network. Through experiments, we show that employing these deep learning techniques to low-resolution noisy infrared images leads to a noticeable improvement in performance. The classification accuracy improved from 78.32% to 84.43% (for images with 6 × 8 resolution), and from 90.11% to 94.54% (for images with 12 × 16 resolution) when we used the CNN and CNN + LSTM networks, respectively.

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An Adaptive Vehicle Clustering Algorithm Based on Power Minimization in Vehicular Ad-Hoc Networks

Zhao H., Tang J., Adebisi B., Ohtsuki T., Gui G., Zhu H.

IEEE Transactions on Vehicular Technology （IEEE Transactions on Vehicular Technology）  71 （ 3 ） 2939 - 2948 2022年03月

ISSN  00189545

　概要を見る

In this paper, we propose an adaptive vehicle clustering algorithm based on fuzzy C-means algorithm, which aims at minimizing power consumption of the vehicles. Specifically, the proposed algorithm firstly dynamically allocates the computing resources of each virtual machine in the vehicle, according to the popularity of different virtualized network functions. The optimal clustering number to minimize the total energy consumption of vehicles is determined using the fuzzy C-means algorithm and the clustering head is selected based on vehicles moving direction, weighted mobility, and entropy. Simulation results are provided to confirm that the proposed algorithm can decrease the power consumption of vehicles while satisfying the vehicle delay requirement.

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Lightweight Automatic Modulation Classification Based on Decentralized Learning

Fu X., Gui G., Wang Y., Ohtsuki T., Adebisi B., Gacanin H., Adachi F.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  8 （ 1 ） 57 - 70 2022年03月

　概要を見る

Due to the implementation and performance limitations of centralized learning automatic modulation classification (CentAMC) method, this paper proposes a decentralized learning AMC (DecentAMC) method using model consolidation and lightweight design. Specifically, the model consolidation is realized by a central device (CD) for edge device (ED) model averaging (MA) and multiple EDs for ED model training. The lightweight is designed by separable convolutional neural network (S-CNN), in which the separable convolutional layer is utilized to replace the standard convolution layer and most of fully connected layers are cut off. Simulation results show that the proposed method substantially reduces the storage and computational capacity requirements of the EDs and communication overhead. The training efficiency also shows remarkable improvement. Compared with convolutional neural network (CNN), the space complexity (i.e., model parameters and output feature map) is decreased by about 94% and the time complexity (i.e., floating point operations) of S-CNN is decreased by about 96% while degrading the average correct classification probability by less than 1%. Compared with S-CNN-based CentAMC, without considering model weights uploading and downloading, the training efficiency of our proposed method is about N times of it, where N is the number of EDs. Considering the model weights uploading and downloading, the training efficiency of our proposed method can still be maintained at a high level (e.g., when the number of EDs is 12, the training efficency of the proposed AMC method is about 4 times that of S-CNN-based CentAMC in dataset D1 = 2 {FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, 16QAM} and about 5 times that of S-CNN-based CentAMC in dataset D2 = 2 {FSK, 4FSK, 8FSK, BPSK, QPSK, 8PSK, PAM2, PAM4, PAM8, 16QAM}), while the communication overhead is reduced more than 35%.

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Low-Resolution Infrared Array Sensor for Counting and Localizing People Indoors: When Low End Technology Meets Cutting Edge Deep Learning Techniques

Bouazizi M., Ye C., Ohtsuki T.

Information (Switzerland) （Information (Switzerland)）  13 （ 3 ）  2022年03月

　概要を見る

In this paper, we propose a method that uses low-resolution infrared (IR) array sensors to identify the presence and location of people indoors. In the first step, we introduce a method that uses 32 × 24 pixels IR array sensors and relies on deep learning to detect the presence and location of up to three people with an accuracy reaching 97.84%. The approach detects the presence of a single person with an accuracy equal to 100%. In the second step, we use lower end IR array sensors with even lower resolution (16 × 12 and 8 × 6) to perform the same tasks. We invoke super resolution and denoising techniques to faithfully upscale the low-resolution images into higher resolution ones. We then perform classification tasks and identify the number of people and their locations. Our experiments show that it is possible to detect up to three people and a single person with accuracy equal to 94.90 and 99.85%, respectively, when using frames of size 16 × 12. For frames of size 8 × 6, the accuracy reaches 86.79 and 97.59%, respectively. Compared to a much complex network (i.e., RetinaNet), our method presents an improvement of over 8% in detection.

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Blockchain-Based Task Offloading for Edge Computing on Low-Quality Data via Distributed Learning in the Internet of Energy

Liu Y., Guan X., Peng Y., Chen H., Ohtsuki T., Han Z.

IEEE Journal on Selected Areas in Communications （IEEE Journal on Selected Areas in Communications）  40 （ 2 ） 657 - 676 2022年02月

ISSN  07338716

　概要を見る

With the development of the Internet of energy, more and more participants share data by different types of edge devices. However, such multi-source heterogenous data typically contain low-quality data, e.g., missing values, which may result in potential risks. Besides, resource-constrained devices incur large latency in edge computing networks. To alleviate such latency, distributed task offloading schemes are designed to share the computation burden between edge nodes and nearby servers. However, there are three main drawbacks of such schemes. First, low-quality data are not carefully evaluated by constraints under scenarios, which may result in slow convergence in distributed computation. Second, multi-source data including sensitive information are computed and shared among edge nodes without privacy protection. Third, distributed tasks on low-quality data may result in low-quality results even with an optimal offloading scheme. To address the problems above, a task offloading framework for edge computing based on consortium blockchain and distributed reinforcement learning is proposed in this paper, which can provide high-quality task offloading policies with data privacy protected. This framework consists of three key components: data quality evaluation (DQ) with multiple data quality dimensions, data repairing (DR) with a repairing algorithm based on a novel repairing consensus mechanism and distributed reinforcement learning for task arrangement (DELTA) with a distributed reinforcement learning algorithm based on a novel low-quality data distributing strategy. Numeric results are presented to illustrate the effectiveness and efficiency of the proposed task offloading framework for edge computing on low-quality data in the IoE.

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Dementia Detection Using Language Models and Transfer Learning

Bouazizi M., Zheng C., Ohtsuki T.

ACM International Conference Proceeding Series （ACM International Conference Proceeding Series）     152 - 157 2022年01月

　概要を見る

Over the last years, more and more attention has been given by the researchers towards dementia diagnosis using computational approaches applied on speech samples given by dementia patients. With the progress in the field of Deep Learning (DL) and Natural Language Processing (NLP), techniques of text classification using these techniques that are derived from fields such as sentiment analysis have been applied for dementia detection. However, despite the relative success in these techniques, the two tasks (i.e., sentiment analysis and dementia detection) have major differences, leading us to believe that adjustments need to be made to make the detection more accurate. In the current paper, we use transfer learning applied on a common language model. Unlike conventional work, where the text is stripped from stop words, we address the idea of exploiting the stop words themselves, as they embed non-context related information that could help identify dementia. For this sake, we prepare 3 different models: a model processing only context words, a model stop words with patterns of part-of-speech sequences, and a model including both. Through experiments, we show that both grammar and vocabulary contribute equally to the classification: the first model reaches an accuracy equal to 70.00%, the second model reaches an accuracy equal to 76.15%, and the third model reaches an accuracy equal to 81.54%.

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Data Augmentation Aided Few-Shot Learning for Specific Emitter Identification

Zhang X., Wang Y., Zhang Y., Lin Y., Gui G., Tomoaki O., Sari H.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

Specific emitter identification (SEI) extracts the fingerprint characteristics of emitters according to the subtle differences of transmitted signals, to distinguish different emitter individuals and prevent unauthorized network access. Deep learning (DL) based SEI methods have been proposed to achieve a good identification performance in recent years. However, the existing methods need a massive specific emitter dataset to alleviate model overfitting during the training stage. In this paper, we propose data augmentation (DA) aided few-shot learning method and validate the proposed method using automatic dependent surveillance-broadcast (ADS-B) signals. Specifically, according to the characteristics of ADS-B signals, four DA methods, i.e., flip, rotation, shift, and noise are studied for the proposed method. Experimental results are provided to show that the proposed method improves the recognition accuracy and the model robustness.

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Cross-Person Activity Recognition Method Using Snapshot Ensemble Learning

Xu S., He Z., Shi W., Wang Y., Ohtsuki T., Guiy G.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

Human activity recognition (HAR) is one of the most promising technologies in the smart home, especially radio frequency (RF-based) method, which has the advantages of low cost, few privacy concerns and wide coverage. In recent years, deep learning (DL) has been introduced into HAR and these DL-based HAR methods usually have outstanding performance. However, as the recognition scenarios and target change, the model performance drops sharply. To solve this problem, we propose a generalized method for cross-person activity recognition (CPAR), which is called snapshot ensemble learning based an attention with bidirectional long short-term memory (SE-ABLSTM). Specifically, by defining the cosine annealing learning rate, the models with diversity are saved and integrated in the same training process. In addition, we provide a dataset for CPAR and simulation results show that our method improves generalization performance by 5% compared to the original method. The source code and dataset for all the experiments can be available at https://github.com/NJUPT-Sivan/Cross-person-HAR.

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Co-Evolutionary Dynamic Cell optimization Algorithm for HAPS Mobile Communications

Shibata Y., Takabatake W., Hoshino K., Nagate A., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-June 2022年

ISSN  15502252

　概要を見る

High-Altitude Platform Stations (HAPSs) are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Single-cell frequency reuse using multiple cells can increase capacity to cover a wide area. Thus far, we have proposed a cell configuration optimization method based on a genetic algorithm (GA). We clarified the optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution scenario. Whereas user distributions differ depending on location. Thus, cell configuration optimization is required for a non-uniform user distribution. Each cell needs different antenna parameters for non-uniform user distributions, resulting in an increase in the number of parameters compared with a uniform user distribution, making the optimization difficult even by GA in some cases. To address this problem, we propose a co-evolutionary dynamic cell optimization algorithm. Co-evolution is one of the divide-and-conquer methods. The proposed method divides multiple cells into several groups to decrease the number of parameters to optimize at a time, and each group is optimized in order. The simulation results show that the cell configuration with the proposed method can increase the sum of the square root of throughput for non-uniform user distributions compared to that for uniform user distributions. Furthermore, the proposed method with three sub-areas can improve the sum of the square root of throughput while reducing the number of combinations performed compared to the method without subarea division for a nine-cell scenario.

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Autoencoder-based Pilot Pattern Design for CDL Channels

Yamada Y., Ohtsuki T.

APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era （APCC 2022 - 27th Asia-Pacific Conference on Communications: Creating Innovative Communication Technologies for Post-Pandemic Era）     76 - 80 2022年

　概要を見る

In the pilot-based channel estimations, a large number of pilot signals enable an improvement in the channel estimation accuracy but force a decrease in the data transmission efficiency. Therefore, the pilot pattern design schemes on the resource grid have been researched to achieve high channel estimation accuracy with a small number of pilot signals. In the conventional scheme for pilot pattern design, the autoencoder which enables discrete feature selection is utilized to design pilot patterns for Vehicular-A channels. However, the pilot pattern for other channels is not reported, and it is not clear whether this scheme has good performance compared to uniform pilot patterns. In this paper, we derive the pilot patterns for Clustered Delay Line (CDL) channels specified in the 5th Generation Mobile Communication System (5G) standard using the autoencoder-based pilot pattern design scheme. We also derive the pilot patterns for CDL-A and CDL-D, which model respectively NLOS (Non-Line-of-Sight) and LOS (Line-of-Sight) environments, with different three speeds of User Equipment (UE). Through computer simulation, we show that the autoencoder-based pilot patterns improve the channel estimation accuracy compared to the uniform pilot pattern.

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Attention-CNN-Aided Specific Emitter Identification Method with Limited Radio Frequency Dataset

Wang S., Zhang Y., Sun J., Ohtsuki T., Gui G.

International Conference on Communication Technology Proceedings, ICCT （International Conference on Communication Technology Proceedings, ICCT）  2022-November-November   1536 - 1540 2022年

　概要を見る

Specific emitter identification (SEI) plays a more and more important role to recognize unknown radio frequency (RF) devices in the field of physical layer security. Existing deep learning (DL)-based SEI methods have been proposed for solving problems that enough RF dataset can be obtained at the model training stage. In many real application scenarios, however, only the limited dataset can be collected which causes the conventional DL methods unstable. To solve this problem, in this paper, we propose a robust SEI method using a self-attention mechanism and convolutional neural network (attention-CNN). To be specific, we collect a real RF dataset from the automatic dependent surveillance-broadcast (ADS-B) signal. Here, the ADS-B dataset is limited in some special cases and data augmentation is used to improve the features of the dataset. Simulation results show that the proposed method achieves better identification performance than the benchmark.

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Arterial Blood Pressure Estimation Method from Electrocardiogram Signals using U-Net

Yoshizawa R., Yamamoto K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2022-July   2689 - 2692 2022年

ISSN  1557170X

　概要を見る

Previous works proposed deep learning models to estimate blood pressure from electrocardiogram (ECG) signals. However, they can only estimate max, min, and mean arterial blood pressures and cannot estimate arterial blood pressure (ABP). This paper presents the ABP estimation method from ECG signals using the deep learning model of U-Net. Through the performance evaluation with signals of about 185 hours, we observed that the proposed method estimated ABP with high accuracy. Furthermore, the accuracies of the calculated max\min and mean ABPs were comparable to those in the previous works, even though our method also estimated ABP. In the end, we discussed the subject-overfitting problem and future work toward practical use of daily blood pressure monitoring.

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An Evaluation on Information Composition in Dementia Detection Based on Speech

Zheng C., Bouazizi M., Ohtsuki T.

IEEE Access （IEEE Access）  10   92294 - 92306 2022年

　概要を見る

In recent years, scientists are paying much attention to the research on automatic dementia detection that could be applied to the speech samples of dementia patients. In a related context, recent research has seen the fast development of Deep Learning (DL) and Natural Language Processing (NLP). The techniques developed for text classification or sentiment analysis have been applied to the field of early dementia detection by many researchers. However, text classification and sentiment analysis are different tasks from dementia detection, which makes us believe that for dementia detection, some adjustments would help improve the performance of the machine learning models. In this work, we implemented experiments with various language models including traditional n-gram language models, Average stochastic gradient descent Weight-Dropped Long Short-Term Memory (AWD-LSTM) models, and attention-based models to evaluate the speech data of dementia patients. Unlike traditional works where the text is stripped from stop words, we propose the idea of exploiting the stop words themselves, since they offer non-context information which helps to identify dementia. As a result, 3 different language models are prepared in this work: a model processing only context words, a model processing stop words and Part-of-Speech (PoS) tag sequences, and a model processing both of them. By performing the aforementioned experiments, we show that both grammar and vocabulary contribute equally to classification: The 3 models achieve an accuracy equal to 70.00%, 76.16%, and 81.54%, respectively.

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An Effective Radio Frequency Signal Classification Method Based on Multi-Task Learning Mechanism

Liu H., Hao C., Peng Y., Wang Y., Ohtsuki T., Gui G.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

With the increasing popularity of Internet of things (IoT), the emergence of many IoT devices has led to security vulnerabilities. The classification of wireless signals is very important for secure communications. Most of existing signal classification tasks only focus on single signal classification task, while ignoring the relationship between radio frequency fingerprinting identification (RFFI) and automatic modulation classification (AMC). To solve the multi-task classification problem, this paper designs a multi-task learning convolutional neural networks (MTL-CNN). Real-radio datasets are generated by Signal Hound VSG60A and collected by Signal Hound BB60C to solve the lack of RFF samples with numerous modulation types. Experimental results confirm that the MTL-CNN method can work well by using the generated dataset. The MTL network designed in this paper improves the accuracy of RFFI by 1xs% relative to the single-task learning (STL) network. The keras code is released at https://github.comLiuK1288/1hw-000.

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Adaptive DNN-based CSI Feedback with Quantization for FDD Massive MIMO Systems

Gao J., Bouazizi M., Ohtsuki T., Gui G.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-September 2022年

ISSN  15502252

　概要を見る

Accessing the accurate downlink channel state information (CSI) is essential to take full advantage of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its weak channel reciprocity. Meanwhile, great computational burdens will happen, which is accompanied by continuous CSI feedback. The existing compressive sensing (CS)-based and deep learning (DL)-based methods try to solve such problems, but do not achieve desired effect to get ideal CSI feedback or decrease the overhead. An adaptive deep neural network (DNN)-based CSI feedback method is proposed in this paper to address this. A classification block of the compression ratio is adopted and modified to apply to a more complex channel model named Clustered-Delay-Line (CDL), which helps decrease the computational overhead of the network. Besides, the reconstruction accuracy of the CSI feedback is further improved by proposing a new structure of the encoder. Quantization and dequantization modules are also applied to make the whole network more robust and effectively minimize the quantization distortion in the real communication scenario, respectively. The simulation results show that the proposed method performs better than the conventional ones on the CSI reconstruction accuracy in terms of normalized mean square error (NMSE), even though the quantization module is added.

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A Semi-Supervised Federated Learning Scheme via Knowledge Distillation for Intrusion Detection

Zhao R., Yang L., Wang Y., Xue Z., Gui G., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2022-May   2688 - 2693 2022年

ISSN  15503607

　概要を見る

Federated learning (FL) has become an increasingly popular solution for intrusion detection to avoid data privacy leakage in Internet of Things (IoT) edge devices. However, most of the current FL-based intrusion detection methods still suffer from three limitations: (1) model parameters transmitted in each round may be used to recover private data which leads to security risks, (2) not independent and identically distributed (non-IID) private data seriously adversely affects the training of FL (especially distillation-based FL), and (3) high communication overhead caused by the large model size greatly hinders the actual deployment of the solution. To address these problems, this paper develops an intrusion detection method based on semi-supervised FL scheme via knowledge distillation. First, our proposed method leverages unlabeled data via distillation method to enhance the classifier performance. Second, we build a CNN-based model for extracting deep features of the traffic packets, and take this model as both the classifier network and discriminator network. Third, discriminator is designed to improve the quality of each client's predicted labels, to avoid the failure of distillation training caused by a large number of incorrect predictions under private non-IID data. Moreover, the combination of hard-label strategy and voting mechanism further reduces communication overhead. Experimental results on the real-world traffic dataset show that our proposed method can achieve better classification performance as well as lower communication overhead than state-of-the-art methods.

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A Multi-Layer Hybrid Network With Its Application in Fetal Heart Rate Monitoring

Wang L., Ohtsuki T., Owada K., Honma N., Hayashi H.

IEEE Signal Processing Letters （IEEE Signal Processing Letters）  29   1207 - 1211 2022年

ISSN  10709908

　概要を見る

Fetal heart rate monitoring is an enormous challenge since the observed fetal electrocardiography (ECG) signal is typically characterized by a very low signal-to-noise ratio (SNR). In this letter, we aim to improve the accuracy of heartbeat detection by proposing an adaptive template for removing the maternal cycle. The template is formed by a matrix, each row of which consists of an abdominal recorded signal (ADS). It can be updated by integrating the incoming cycle while removing the contribution of the previous recording. This process is conducted by considering a discriminator to adapt the non-stationarity of each incoming cycle. Furthermore, to suppress the morphological change caused by noise, we propose a novel multi-layer hybrid network to reconstruct the chest maternal ECG (chest mECG) morphology from a set of templates. The approach has a deep structure of each layer consisting of a reservoir layer and an encoder layer. The reservoir layer explores multi-scale dynamics by transforming the input series into a high-dimensional space. The encoder layer achieves the collection of the encoder features from the output of the reservoir layer. Once the model is built, the output weight of a direct connection is trained by solving a regression problem. Experimental results show that the proposed method has a better performance compared with some classical approaches.

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Dynamic Antenna Control for HAPS Using Fuzzy Q-Learning in Multi-Cell Configuration

Wada K., Yang S., Bouazizi M., Ohtsuki T., Shibata Y., Takabatake W., Hoshino K., Nagate A.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2022-May   1 - 6 2022年

ISSN  15503607

　概要を見る

In the 5th generation mobile communications (5G) and 5G and beyond (B5G), a high altitude platform station (HAPS) is expected to serve as a flying base station (BS) to provide communications over wide areas. In the HAPS system, a multi-cell configuration with multiple beams is considered to increase system throughput. When the HAPS is subjected to wind pressure, the cell range moves accordingly, causing degradation of received signal power and handover to the user equipment (UE). To suppress such degradation and handover, beam control of HAPS is necessary. However, it is not easy to control the beam because multiple antenna parameters affect each other and determine the cell range. In this paper, we propose a beam control method for HAPS using fuzzy Q-learning in multi-cell configuration. In this type of learning, the variable states are controlled by the use of fuzzy sets, which allows multiple searches to be performed in one setup, thus reducing the cost of search, compared with conventional Q-learning. In the proposed beam control method, antenna parameters are controlled by fuzzy Q-learning so that the number of users having a received signal power larger than a predetermined threshold becomes larger in each cell. We evaluate the proposed method by computer simulation and show that the proposed method can improve the number of users having a received signal power larger than a predefined threshold and thus reduce the number of users with low throughput compared to before learning.

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Unsupervised Representation Learning-based Doppler Ultrasound Signal Quality Assessment

Shi X., Yamamoto K., Ohtsuki T., Matsui Y., Owada K.

2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings （2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings）     2254 - 2259 2022年

ISSN  23340983

　概要を見る

The Doppler ultrasound (DUS) transducer has been widely used for fetal heart rate (FHR) monitoring. However, the fetal DUS signals from the transducers can be corrupted by several interference sources such as maternal and fetal movements, which makes FHR estimation using fetal DUS signals challenging. Fetal DUS signal quality assessment (SQA) can help to remove or interpolate unreliable FHRs estimated from noisy signals to improve the accuracy of FHR estimation. There are some existing approaches for fetal DUS SQA, and most of these approaches with high accuracy are based on supervised learning-based algorithms and human-defined properties. Nonetheless, the fetal DUS datasets with quality-level annotations are limited, and human-defined properties place a limitation on mining more deep information related to signal quality in fetal DUS signals. In this paper, we propose an unsupervised representation learning-based fetal DUS SQA for the improvement of FHR estimation performance. We firstly learn representations of pre-processed fetal DUS data from variational autoencoder (VAE) and then combine these representations as one signal quality index (SQI) using a self-organizing map (SOM). Finally, we apply the combined SQI and a Kalman filter (KF) to estimate fetal RR intervals (FRRI) for reducing the errors of FHR estimation. The experimental results showed that our proposed method could reduce the averaged root mean squared error (RMSE) of FRRI and averaged absolute error (AAE) of FHR.

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Two-Step Dynamic Cell Optimization Algorithm for HAPS Mobile Communications

Shibata Y., Takabatake W., Hoshino K., Nagate A., Ohtsuki T.

IEEE Access （IEEE Access）  10   68085 - 68098 2022年

　概要を見る

High altitude platform stations are attracting much attention as novel mobile communication platforms for ultra-wide coverage areas and disaster-resilient networks. Multi-cell configurations can increase communication capacity in a wide area. Conventional work optimizes the cell configuration for a multi-cell configuration based on a genetic algorithm (GA). This method identifies an optimal cell configuration in terms of spectral efficiency depending on the number of cells under a uniform user distribution. However, user distributions differ depending on location; thus, cell configuration optimization is also required for non-uniform user distributions. The problem is an increased number of parameters because each cell requires different antenna parameters for non-uniform user distribution compared to a uniform user distribution. This increases the difficulty of optimization, even when using a GA in some cases. Therefore, we propose a GA-based two-step cell optimization algorithm that comprises both search space reduction and antenna parameter optimization steps to address this problem. The proposed method employs the concept of co-evolution, i.e., a divide-and-conquer method. The proposed method divides multiple cells into several groups to reduce the number of optimized parameters and optimizes each group in order. In addition, the search space is reduced based on a marginal histogram obtained via optimization with a large step size. Simulation results demonstrate that this technique can reduce the number of combinations compared to the case without sub-area division. In addition, there are cases where the search space reduction further reduces the number of combinations without degrading the sum of the square root of throughput.

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Social Robot Detection Using RoBERTa Classifier and Random Forest Regressor with Similarity Analysis

Chen Y., Bouazizi M., Ohtsuki T.

2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings （2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings）     6433 - 6438 2022年

ISSN  23340983

　概要を見る

Twitter has skyrocketed over the past few years and has become a major social media platform. At the same time, the number of social robots on Twitter has also increased significantly. These bot accounts imitate the speeches of normal users to manipulate public opinions, affect the normal communication of users. Therefore, bot account detection came into being. Despite extensive research efforts, bots on Twitter are still evolving to evade detection. Most of the current bot detection methods have a single structure and cannot detect and identify different types of bot accounts well. In this paper, we propose a new system for social robot detection that uses a RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach) classifier and a random forest regressor with similarity analysis. In particular, the system considers the similarity of tweets and uses a voting system in addition to a set of features extracted from the user profile information and the tweets themselves. We conduct experiments using the largest dataset of bots available and show that the accuracy of our system is up to 0.8588, which is higher than that of all the other baseline methods.

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Smartphone-Aided Human Activity Recognition Method using Residual Multi-Layer Perceptron

Shi S., Wang Y., Dong H., Gui G., Ohtsuki T.

INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops （INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops）  2022年

　概要を見る

Human activity recognition (HAR) has received intensely attention in many applications, such as healthcare, human-computer interaction, and smart home. Existing HAR methods based on deep learning (DL) have been proposed in the last several years. However, these DL-based HAR methods are hard to balance between performance and cost, which truly limited the applications in practical scenarios. To solve this problem, this paper proposes a smartphone-aided HAR method using the residual multi-layer perceptron (Res-MLP). It composes of two linear layers and Gaussian error linear unit (GELU) activation function, and obtains Res-MLP network through residual. Experimental results show that the proposed HAR method can achieve a high classification accuracy of 96.72% based on the public UCI HAR dataset.

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Sarcasm Over Time and Across Platforms: Does the Way We Express Sarcasm Change?

Bouazizi M., Ohtsuki T.

IEEE Access （IEEE Access）  10   55958 - 55987 2022年

　概要を見る

Sarcasm is a sophisticated form of speech used to convey a message other than the apparent one. To date, there are numerous papers that have discussed the idea of automatic sarcasm detection and how it could be used for sentiment analysis improvement. The objective of this paper is to provide non-experts with a comprehensive overview of the state of research in this field and the main findings regarding sarcasm detection. Therefore, in this paper, we survey the state-of-the-art work done in this field, we recapitulate the research effort done, with focus on the more recent works, and we present the expected performance out of the proposed works. Nevertheless, we study in detail how this form of speech is used in different platforms, and how the way we express it evolves over time. We also discuss the proposition that suggests that sarcasm is a polarity switcher for sentiment analysis. To achieve these goals, we run some experiments on 3 different data sets, collected from 3 different platforms, and compare how sarcasm is employed in each. These platforms are Twitter, Reddit, and some news websites. Our experiments show that the way sarcasm is expressed is highly dependent on language mastery and the platform used. For instance, in the Twitter data set, whose users vary widely in age, language mastery, and understanding what sarcasm means, the overall precision of detection of sarcastic statement reaches 89.31%. In the reddit data set, the precision of detection of such statements is about 55.33%, and in the news data set, the precision reaches over 96.67%. Our experiments also show that, to a great extent, it is safe to affirm that sarcasm, when employed, switches the polarity of a given piece of text: for the 3 platforms presented above, sarcasm has been a polarity switcher for 89.3%, 89.1%, and 92.0% of their respective instances.

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Non-invasive Fetal ECG Signal Quality Assessment based on Unsupervised Learning Approach

Shi X., Yamamoto K., Ohtsuki T., Matsui Y., Owada K.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2022-July   1296 - 1299 2022年

ISSN  1557170X

　概要を見る

The non-invasive fetal electrocardiogram (FECG) derived from abdominal surface electrodes has been widely used for fetal heart rate (FHR) monitoring to assess fetal well-being. However, the accuracy of FECG-based FHR estimation heavily depends on the quality of FECG signal itself, which can generally be affected by several interference sources such as maternal heart activities and fetal movements. Hence, FECG signal quality assessment (SQA) is an essential task to improve the accuracy of FHR estimation by removing or interpolating low-quality FECG signals. In recent research, various SQA methods based on supervised learning have been proposed. Although these methods could perform accurate SQA, they require large labeled datasets. Nevertheless, the labeled datasets for the FECG SQA are very limited. In this paper, to address this limitation, we propose an unsupervised learning-based SQA method for identifying high and low-quality FECG signal segments. Specifically, a fully convolutional network (FCN)-based autoencoder (AE) is trained for reconstructing a spectrogram derived from FECG. An AE-based feature related to reconstruction error is then calculated to identify high and low-quality FECG segments. In addition, entropy-based features, statistical features, and ECG signal quality indices (SQIs) are also extracted. The high and low-quality segments are identified by feeding the extracted features into self-organizing map (SOM). The experimental results showed that our proposal achieved an accuracy of 98% in high and low-quality signal classification.

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Non-contact Blood Pressure Estimation Method Based on Blood Pressure Category Classification

Ishizaka S., Yamamoto K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2022-July   2676 - 2679 2022年

ISSN  1557170X

　概要を見る

In recent years, non-contact Blood Pressure (BP) measurement has been attracting attention for measuring our health status in daily life. A Doppler radar can observe pulse waves caused by chest wall displacement due to heartbeat. BP can be estimated by constructing a BP estimation model using BP-related features obtained from the pulse wave. However, compared to when modeling for each subject, the BP esti-mation accuracy deteriorates significantly when modeling with multiple subjects including the testing subject. To deal with this limitation, BP category classification has been introduced into PhotoPlethysmoGraphy (PPG)-based BP estimation. In this paper, we develop a Doppler radar-based BP estimation method based on BP category classification. In the proposed method, the pulse waves extracted from a Doppler radar are classified into three categories, 'Low BP', 'Normal BP', and 'High BP' by k-Nearest Neighbor (kNN) based on the features that correlate with BP. The SBP estimation model is trained for each BP category. After the BP category classification, SBP is then estimated by using the model corresponding to the classified BP category. The experimental results showed that the proposed method with BP category classification estimated SBP accurately, compared to without BP category classification.

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Multibeam Doppler Sensor-Based Non-Contact Heartbeat Detection Using Beam Diversity

Kitagawa T., Yamamoto K., Endo K., Ohtsuki T.

IEEE Access （IEEE Access）  10   16242 - 16253 2022年

　概要を見る

Heartbeat detection could enable various applications in the medical and health care fields. In particular, non-contact heartbeat detection can be acceptable for those who have difficulty wearing devices, such as burn patients and infants. A Doppler sensor could be a key device to realize heartbeat detection without any wearable devices. Many researches in recent years have focused on heartbeat detection using a Doppler sensor with a single beam Doppler sensor. However, when the SNR (Signal-to-Noise Ratio) of heartbeat components is low for the beam direction, the heartbeat detection accuracy is likely to degrade. In this paper, for more accurate heartbeat detection, we propose a heartbeat detection method based on beam diversity using a multibeam Doppler sensor. Through the preliminary experiments, we clarified that the SNRs of heartbeat components differ from one beam to another. This means that when the SNR is low for one beam, the SNR could be high for the other beams. Inspired by this fact, the proposed method extracts heartbeat components from all peaks detected by the multi-beam signals. To verify the heartbeat detection accuracy of our method, we conducted the experiments for different detection ranges. The obtained results show that compared to the conventional methods using a single beam, our proposed method using multibeam detected heartbeat more accurately. This indicates the benefit of exploiting beam diversity, i.e., the heartbeat detection accuracy can be improved by utilizing beam diversity.

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Max-Min Fair 3D Trajectory Planning for Solar-Powered UAV-Assisted Data Collection

Sun C., Xiong X., Ni W., Ohtsuki T., Wang X.

2022 IEEE/CIC International Conference on Communications in China, ICCC 2022 （2022 IEEE/CIC International Conference on Communications in China, ICCC 2022）     610 - 615 2022年

　概要を見る

This paper presents a new solar-powered fixed-wing unmanned aerial vehicle (UAV)-assisted data collection technique, where a fixed-wing UAV harvests solar energy to fly and collect data from smart devices (SDs). The minimum of the data upload-ed from any of the SDs is maximized by optimizing the UAV's three-dimensional (3D) trajectory. The key idea is that we develop variable substitution and successive convex approximation (SCA) techniques to convexify the nonconvex data transmission, UAV energy consumption and mobility constraints, as well as the nonconvex energy harvesting constraint. The proposed algorithm guarantees a locally optimal solution satisfying the Karush-Kuhn-Tucker (KKT) conditions. Another important aspect is that an effective set of approximation parameters are identified for accurate approximation of the solar energy harvesting at relatively low altitudes, contributing to the design of efficient 3D trajectories balancing energy harvesting and data collection. Numerical results show that the 3D flight increases uploaded data by 59% as compared to the two-dimensional (2D) flight.

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Joint Weighted and Truncated Nuclear Norm Minimization for Matrix Completion-Assisted mmWave MIMO Channel Estimation

Li Y., Liu J., Chen C., Gui G., Ohtsuki T., Sari H.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-June 2022年

ISSN  15502252

　概要を見る

Matrix completion-assisted channel estimation is considered one of promising techniques in millimeter wave (mmWave) massive multiple input multiple output (MIMO) system by exploiting the low-rank property of channel matrix in the angle domain. However, existing channel estimation approaches are hard to achieve high accuracy due to the inevitable bias solution caused by nuclear norm based minimization (NNM). To address this problem, this paper proposes a novel matrix completion-assisted mmWave massive MIMO channel estimation method. We employ an effective and flexible rank function named joint weighted and truncated nuclear norm as relaxation of nuclear norm, and then construct an novel matrix completion model for channel estimation problem. Moreover, a popular framework of alternating direction method of multipliers (ADMM) is derived for minimization of the resulting optimization problem. Simulation results are provided to verify the proposed method that can flexibly and effectively improve the channel estimation accuracy with reliable convergence.

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Heartbeat Detection Using 3D Lidar and MIMO Doppler Radar

Bouazizi M., Yamamoto K., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2022-May   3040 - 3045 2022年

ISSN  15503607

　概要を見る

Non-contact measurement of the heart rate (HR) refers to the usage of wireless sensors (e.g., Doppler sensors) to identify the heart-related faint movements and to reconstruct the R-peaks or estimate the HR. Conventional work in the field requires the chest of the subject being monitored to be in a specific position in front of the sensor. In our previous work, we have proposed to use a Multiple-Input Multiple-Output (MIMO) Doppler Radar to perform the detection of heartbeats even when the Signal-to-Noise Ratio (SNR) is not very high. By exploiting the fact that different beams have different SNR of heartbeat components, our previous method detects heartbeats by aggregating several received signals. In general, it is essential to use beam directions toward the a subject's chest. However, the chest location estimation is challenging, when there exist several subjects or other moving objects. In the current paper, to deal with this issue, we narrow down the direction of beams to use by using a system composed of a MIMO Doppler sensor and a Light Detection and Ranging (LiDAR) device. By means of transfer learning and automatic annotation of data, the LiDAR is trained to identify the relative chest angle and distance to the subject from the devices, while the MIMO Doppler Radar can adjust the beam towards the identified direction. Throughout experiments, we show the effectiveness of the combination of both the Lidar and radar in detecting the direction and distance to the subject as well as the heartbeat.

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Fetal Arrhythmia Detection based on Deep Learning using Fetal ECG Signals

Nakatani S., Yamamoto K., Ohtsuki T.

2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings （2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings）     2266 - 2271 2022年

ISSN  23340983

　概要を見る

Arrhythmia is one of the causes of sudden infant death, and it is very important to detect fetal arrhythmia for fetal well-being. Fetal electrocardiogram (FECG) is one of the methods to detect a heartbeat. Fetal arrhythmia can be detected based on the heartbeat detection results from FECG signals such as heartbeat intervals. However, the accuracy of arrhythmia detection easily degrades depending on the accuracy of heartbeat detection. In this paper, we propose a deep learning-based fetal arrhythmia detection method using FECG signals. Recently, arrhythmia detection methods using adult ECG signals have achieved a high arrhythmia detection accuracy based on deep learning. Motivated by this fact, in the proposed method, the acquired FECG signals are segmented, and the segments are input into a deep learning model that classifies them into normal or arrhythmia ones. Based on the classification results of multiple segments, a subject is judged as a healthy or arrhythmia subject. Each segment of the training data is divided into three categories based on the estimated heartbeat interval: (i) normal, (ii) arrhythmia, and (iii) a segment that could be both normal and arrhythmia. Only segments labeled as normal or arrhythmia are used for training a deep learning model to achieve a higher classification accuracy of the model. Through these procedures, the proposed method detects fetal arrhythmia with fewer effects of heartbeat detection results. The experimental results showed that the proposed method could outperform the conventional methods with heartbeat detection and feature detection in terms of accuracy, specificity, and recall.

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Dynamic Antenna Control for HAPS Using Geometry-based Method in Multi-Cell Configuration

Yang S., Bouazizi M., Ohtsuki T., Shibata Y., Takabatake W., Hoshino K., Nagate A.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2022-June 2022年

ISSN  15502252

　概要を見る

In this research, we propose a novel antenna control method for reducing the number of low throughput User Equipments (UEs) caused by the movement and rotation of High-altitude platform station (HAPS). We assume that each HAPS has three antenna arrays for serving one region that consists of three cells and that we know the UE locations in each cell. In the proposed method we first redesign the cell configuration for each HAPS and then divide all UEs into three cells based on the UE locations. Based on the radius and center location information of three new cells, we can mathematically calculate the antenna parameters by the desired coverage model. Thus, each antenna array will be controlled to serve a new cell, respectively. We evaluate the proposed method under 5 different UE distribution scenarios. The simulation results show that, in 5 different UE distribution scenarios, the proposed method can reduce the number of UEs with low throughput. Compared with the conventional method, the proposed method can achieve good throughput performance in all the scenarios.

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Downlink CSI Feedback Algorithm with Deep Transfer Learning for FDD Massive MIMO Systems

Zeng J., Sun J., Gui G., Adebisi B., Ohtsuki T., Gacanin H., Sari H.

IEEE Transactions on Cognitive Communications and Networking （IEEE Transactions on Cognitive Communications and Networking）  7 （ 4 ） 1253 - 1265 2021年12月

　概要を見る

In this paper, a channel state information (CSI) feedback method is proposed based on deep transfer learning (DTL). The proposed method addresses the problem of high training cost of downlink CSI feedback network in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. In particular, we obtain the models of different wireless channel environments at low training cost by fine-tuning the pre-trained model with a relatively small number of samples. In addition, the effects of different layers on training cost and model performance are discussed. Furthermore, a model-agnostic meta-learning (MAML)-based method is proposed to solve the problem associated with large number of samples of a wireless channel environment required to train a deep neural network (DNN) as a pre-trained model. Our results show that the performance of the DTL-based method is comparable with that of the DNN trained with a large number of samples, which demonstrates the effectiveness and superiority of the proposed method. At the same time, although there is a certain performance loss compared with the DTL-based method, the MAML-based method shows good performance in terms of the normalized mean square error (NMSE).

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Joint UL/DL Resource Allocation for UAV-Aided Full-Duplex NOMA Communications

Shi W., Sun Y., Liu M., Xu H., Gui G., Ohtsuki T., Adebisi B., Gacanin H., Adachi F.

IEEE Transactions on Communications （IEEE Transactions on Communications）  69 （ 12 ） 8474 - 8487 2021年12月

ISSN  00906778

　概要を見る

This paper proposes an unmanned aerial vehicle (UAV)-aided full-duplex non-orthogonal multiple access (FD-NOMA) method to improve spectrum efficiency. Here, UAV is utilized to partially relay uplink data and achieve channel differentiation. Successive interference cancellation algorithm is used to eliminate the interference from different directions in FD-NOMA systems. Firstly, a joint optimization problem is formulated for the uplink and downlink resource allocation of transceivers and UAV relay. The receiver determination is performed using an access-priority method. Based on the results of the receiver determination, the initial power of ground users (GUs), UAV, and base station is calculated. According to the minimum sum of the uplink transmission power, the Hungarian algorithm is utilized to pair the users. Secondly, the subchannels are assigned to the paired GUs and the UAV by a message-passing algorithm. Finally, the transmission power of the GUs and the UAV is jointly fine-tuned using the proposed access control methods. Simulation results confirm that the proposed method achieves higher performance than state-of-the-art orthogonal frequency division multiple-access method in terms of spectrum efficiency, energy efficiency, and access ratio of the ground users.

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Compressive Sampled CSI Feedback Method Based on Deep Learning for FDD Massive MIMO Systems

Wang J., Gui G., Ohtsuki T., Adebisi B., Gacanin H., Sari H.

IEEE Transactions on Communications （IEEE Transactions on Communications）  69 （ 9 ） 5873 - 5885 2021年09月

ISSN  00906778

　概要を見る

Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality.

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Robust resource allocation for two-tier HetNets: An interference-efficiency perspective

Xu Y., Gui G., Ohtsuki T., Gacanin H., Adebisi B., Sari H., Adachi F.

IEEE Transactions on Green Communications and Networking （IEEE Transactions on Green Communications and Networking）  5 （ 3 ） 1514 - 1528 2021年09月

　概要を見る

The heterogeneous network (HetNet) is a promising network for beyond 5G to improve network capacity and spectral efficiency by developing low-power small cells within macro networks. Therefore, appropriate resource allocation schemes should be designed to balance between data rates and the cross-tier interference among different cells. To improve system robustness and also reduce the harmful interference to macrocell users (MUs), in this paper, we maximize the total interference efficiency (IE) (i.e., the ratio of the sum data rates of femtocell users (FUs) to the total cross-tier interference from femtocell base stations (FBSs) to MUs) with imperfect channel state information in a two-tier orthogonal frequency division multiple access based HetNet by jointly optimizing the transmit power of the FBS and the subcarrier allocation factor. Meanwhile, the outage rate constraint of each FU, the outage interference constraint of each MU, the maximum transmit power constraint of the FBS, and the subcarrier assignment constraint are considered simultaneously. The considered problem belongs to a mixed-integer non-linear programming problem and thus is NP-hard. The original problem with uncertain parameters is transformed into a deterministic and convex one solved by using the quadratic transformation approach, the variable relaxation approach, and Lagrange dual theory. The feasible region analysis and robust sensitivity are provided. Simulation results demonstrate that the proposed scheme has a lower interference to the MU and higher IE by comparing it with the traditional schemes.

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SALDR: Joint Self-Attention Learning and Dense Refine for Massive MIMO CSI Feedback with Multiple Compression Ratio

Song X., Wang J., Wang J., Gui G., Ohtsuki T., Gacanin H., Sari H.

IEEE Wireless Communications Letters （IEEE Wireless Communications Letters）  10 （ 9 ） 1899 - 1903 2021年09月

ISSN  21622337

　概要を見る

The advantages of massive multiple-input multiple-output (MIMO) techniques depend heavily on the accuracy of channel state information (CSI). In frequency division duplexing (FDD) massive MIMO systems, the user equipment (UE) needs to feed downlink CSI back to the base station (BS) through the feedback link. The excessive feedback overheads and low reconstruction accuracy are the main obstacles for actual deployment of FDD massive MIMO systems. In recent years, deep learning (DL) has been widely used to address the above problems. In this letter, we propose a neural network by utilizing the self-attention learning and dense refine (SALDR), which improves the accuracy of CSI feedback. Furthermore, a unified decoder named SALDR-U is designed to realize different compression ratios for CSI feedback without changing any parameter. Simulation results show that the proposed SALDR and SALDR-U outperform the state-of-the-art network in terms of accuracy and overhead of CSI feedback. The source code for all the experiments is available at GitHub.The code of this letter can be downloaded from GitHub link: https://github.com/XS96/SALDR.

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An Efficient Intrusion Detection Method Based on Dynamic Autoencoder

Zhao R., Yin J., Xue Z., Gui G., Adebisi B., Ohtsuki T., Gacanin H., Sari H.

IEEE Wireless Communications Letters （IEEE Wireless Communications Letters）  10 （ 8 ） 1707 - 1711 2021年08月

ISSN  21622337

　概要を見る

The proliferation of wireless sensor networks (WSNs) and their applications has attracted remarkable growth in unsolicited intrusions and security threats, which disrupt the normal operations of the WSNs. Deep learning (DL)-based network intrusion detection (NID) methods have been widely investigated and developed. However, the high computational complexity of DL seriously hinders the actual deployment of the DL-based model, particularly in the devices of WSNs that do not have powerful processing performance due to power limitation. In this letter, we propose a lightweight dynamic autoencoder network (LDAN) method for NID, which realizes efficient feature extraction through lightweight structure design. Experimental results show that our proposed model achieves high accuracy and robustness while greatly reducing computational cost and model size.

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An Efficient Specific Emitter Identification Method Based on Complex-Valued Neural Networks and Network Compression

Wang Y., Gui G., Gacanin H., Ohtsuki T., Dobre O.A., Poor H.V.

IEEE Journal on Selected Areas in Communications （IEEE Journal on Selected Areas in Communications）  39 （ 8 ） 2305 - 2317 2021年08月

ISSN  07338716

　概要を見る

Specific emitter identification (SEI) is a promising technology to discriminate the individual emitter and enhance the security of various wireless communication systems. SEI is generally based on radio frequency fingerprinting (RFF) originated from the imperfection of emitter's hardware, which is difficult to forge. SEI is generally modeled as a classification task and deep learning (DL), which exhibits powerful classification capability, has been introduced into SEI for better identification performance. In the recent years, a novel DL model, named as complex-valued neural network (CVNN), has been applied into SEI methods for directly processing complex baseband signal and improving identification performance, but it also brings high model complexity and large model size, which is not conducive to the deployment of SEI, especially in Internet-of-things (IoT) scenarios. Thus, we propose an efficient SEI method based on CVNN and network compression, and the former is for performance improvement, while the latter is to reduce model complexity and size with ensuring satisfactory identification performance. Simulation results demonstrated that our proposed CVNN-based SEI method is superior to the existing DL-based methods in both identification performance and convergence speed, and the identification accuracy of CVNN can reach up to nearly 100% at high signal-to-noise ratios (SNRs). In addition, SlimCVNN just has 10% sim 30 % model sizes of the basic CVNN, and its computing complexity has different degrees of decline at different SNRs; there is almost no performance gap between SlimCVNN and CVNN. These results demonstrated the feasibility and potential of CVNN and model compression.

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Federated learning for DL-CSI prediction in FDD massive MIMO systems

Hou W., Sun J., Gui G., Ohtsuki T., Elbir A.M., Gacanin H., Sari H.

IEEE Wireless Communications Letters （IEEE Wireless Communications Letters）  10 （ 8 ） 1810 - 1814 2021年08月

ISSN  21622337

　概要を見る

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, deep learning for predicting the downlink channel state information (DL-CSI) has been extensively studied. However, in some small cellular base stations (SBSs), a small amount of training data is insufficient to produce an excellent model for CSI prediction. Traditional centralized learning (CL) based method brings all the data together for training, which can lead to overwhelming communication overheads. In this work, we introduce a federated learning (FL) based framework for DL-CSI prediction, where the global model is trained at the macro base station (MBS) by collecting the local models from the edge SBSs. We propose a novel model aggregation algorithm, which updates the global model twice by considering the local model weights and the local gradients, respectively. Numerical simulations show that the proposed aggregation algorithm can make the global model converge faster and perform better than the compared schemes. The performance of the FL architecture is close to that of the CL-based method, and the transmission overheads are much fewer.

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From Multiset Events to Signal Restoration via Tensor Decomposition Based Separation Learning

Wang L., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2021年06月

ISSN  15503607

　概要を見る

The vision and development of blind source separation (BSS) considering the multi-dimensional components attract growing interests in the research community. The single-set BSS methods cannot address the challenge of aligning the extracted components across different datasets, because they neglect the dependence information across datasets. In this paper, we propose to apply the tensor theory to develop a novel structure for the BSS technique that allows us to discover the multi-set components and meaningful spatio-temporal structures of data. The structure is stacked of covariance matrices with temporal information, in terms of limited storage, using the tensor theory that can efficiently encapsulate and compress large-scale data into a compact format. In addition, we discuss how our results provide unique decomposition under such conditions from the theoretical perspective. The experiments designed on the signal with time variation (multiset), which is not identifiable when each mixture is considered individually. It can be solved to restate as a tensor structure of their spatial and temporal correlation matrices. Compared with the other three classical algorithms, the proposed algorithm consistently shows high accuracy.

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Non-contact Blood Pressure Measurement using Doppler Radar based on Waveform Analysis by LSTM

Ishizaka S., Yamamoto K., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2021年06月

ISSN  15503607

　概要を見る

Recently, non-contact blood pressure measurement is attracting increasing interests because it is suitable for the blood pressure measurement on a daily basis. A Doppler radar is a key device to enable the non-contact blood pressure measurement, and it can observe pulse waves due to aortic vasomotion. Specifically, PTT (Pulse Transit Time) is known to be correlated with the blood pressure, and thus the blood pressure can be measured by calculating the PTT from the pulse waves. However, to avoid the effects of slight body movements and breathing, the conventional method requires the subject to lie down on a bed and to stop breathing. In this paper, we propose a Doppler radar-based blood pressure measurement method that works against a subject with slight body movements and breathing. Due to respiration and slight body movements, the pulse wave could be distorted, which makes it difficult to calculate PTT. To solve this issue, in the proposed method, by relating features of the distorted pulse wave to those of the clean pulse wave using a deep learning model with LSTM (Long Short-Term Memory), the distorted pulse wave is transformed to a clean pulse wave that provides PTT correlated with the blood pressure strongly. The experimental results showed that PTT calculated using the proposed method has a strong correlation with actual blood pressure and the accuracy of blood pressure estimation is improved, compared with the conventional method.

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Multi-Task Learning for Generalized Automatic Modulation Classification under Non-Gaussian Noise with Varying SNR Conditions

Wang Y., Gui G., Ohtsuki T., Adachi F.

IEEE Transactions on Wireless Communications （IEEE Transactions on Wireless Communications）  20 （ 6 ） 3587 - 3596 2021年06月

ISSN  15361276

　概要を見る

Automatic modulation classification (AMC) is a critical algorithm for the identification of modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Deep learning (DL)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work under the corresponding condition. In this paper, a novel multi-task learning (MTL)-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from knowledge-sharing-based MTL in varying noise scenarios. In detail, multiple CNN models with the same structure are trained for multiple SNR conditions, but they share their knowledge (e.g. model weight) with each other. Thus, MTL can extract the general features from datasets in different noise scenarios. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

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Mobility-Aware Routing and Caching: A Federated Learning Assisted Approach

Cao Y., Maghsudi S., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2021年06月

ISSN  15503607

　概要を見る

We develop mobility-aware routing and caching strategies to solve the network cost minimization problem for dense small-cell networks. The challenge mainly stems from the insufficient backhaul capacity of small-cell networks and the limited storing capacity of small-cell base stations (SBSs). The optimization problem is NP-hard since both the mobility patterns of the mobilized users (MUs), as well as the MUs' preference for contents, are unknown. To tackle this problem, we start by dividing the entire geographical area into small sections, each of which containing one SBS and several MUs. Based on the concept of one-stop-shop (OSS), we propose a federated routing and popularity learning (FRPL) approach in which the SBSs cooperatively learn the routing and preference of their respective MUs, and make caching decision. Notably, FRPL enables the completion of the multi-tasks in one shot, thereby reducing the average processing time per global aggregation.1 Theoretical and numerical analyses show the effectiveness of our proposed approach.

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Distributed Deep Reinforcement Learning for Renewable Energy Accommodation Assessment with Communication Uncertainty in Internet of Energy

Fang D., Guan X., Peng Y., Chen H., Ohtsuki T., Han Z.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  8 （ 10 ） 8557 - 8569 2021年05月

　概要を見る

Nowadays, microgrids (MG) have attracted much attention, as a key technology of the Internet of Energy (IoE). A great deal of research have shown that the hierarchical microgrid is a more novel structure of IoE. Although the hierarchical microgrid model solves the problem of weak power scheduling capability across microgrids, it suffers from severe communications uncertainty, which can lead to communication delay and fluctuation. To obtain the accurate result of the renewable energy accommodation assessment capacity, a hierarchical microgrid model considering communication uncertainty is proposed in this article. The solution to solve the problem of the assessment renewable energy accommodation capacity for hierarchical MG is a hybrid control based on distribution deep reinforcement learning. The temporal difference (TD) generation adversarial network (TD-GAN) is proposed as a value-based method. Compared with the policy-based method, it can better solve the distributed problem in hybrid control with a generation adversarial network (GAN). Moreover, the challenge that the method cannot handle a continuous action space is solved by using a normalized advantage function (NAF). The method similar with the TD error method is employed to train the GAN network. Simulation results using real power grid data demonstrate the effectiveness and accuracy of the proposed method.

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Spectral Viterbi Algorithm for Contactless Wide-Range Heart Rate Estimation with Deep Clustering

Ye C., Ohtsuki T.

IEEE Transactions on Microwave Theory and Techniques （IEEE Transactions on Microwave Theory and Techniques）  69 （ 5 ） 2629 - 2641 2021年05月

ISSN  00189480

　概要を見る

Objective: The main challenge in contactless heartbeat detection comes from breathing and/or body motion, which typically deteriorate heart rate (HR) measurements, due to incorrect selection of spectral peaks associated with HR. To acquire the reliable peak selection on spectrum within a relatively broad range, this article first proposes a spectral Viterbi algorithm. Second, a nonlinear source separation approach is further proposed to eliminate the noises generated by respiration and movements, suppressing the undesired spectral energy. Proposal: Inspired by the fact that the period of peak-to-peak intervals of heartbeat (RRIs) rarely vary within a short duration, a novel spectral Viterbi algorithm is proposed to estimate HR change, by the path metric (PM) of candidate paths of HR change. Moreover, based on a deep recurrent neural network (RNN), deep clustering (DC) is applied to separate out the targeted heartbeat source from Doppler signal, by dividing its spectrogram. Results: On the premise of wide-range HR measurement, the usage of spectral Viterbi algorithm substantially improved the precision compared with typical methods of HR estimation, both in the statuses of human subjects' sitting still and typewriting. In addition, the combination of DC obtains the smallest average errors. Significance: The proposed spectral Viterbi algorithm with DC is provided with three main strengths: 1) good adaptability to wide-range HR change; 2) robustness to nonlinearly mixed signal and noises; and 3) requirement of only a single-channel sensor.

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Spectrum-aware energy efficiency analysis in K-tier 5G hetnets

Panahi F.H., Panahi F.H., Ohtsuki T.

Electronics (Switzerland) （Electronics (Switzerland)）  10 （ 7 ）  2021年04月

　概要を見る

In future multi-tier cellular networks, cognitive radio (CR) compatible with device-to-de-vice (D2D) communication can be an aid to enhance system spectral efficiency (SE) and energy efficiency (EE). Users in proximity can establish a direct connection with D2D communication and by-pass the base stations (BSs), thereby offloading the network infrastructure and providing EE im-provement. We use stochastic geometry to model and analyze cognitive D2D communication underlying a multi-tier/multi-channel cellular network where the D2D transmitters are capable of harvesting RF energy from ambient interference resulting from simultaneous cellular downlink trans-missions. For further improvement in EE, small cells (SCs) can be put into a power-saving mode by specifying a load-dependent transmission power coefficient (TPC) for SC BSs. In addition, to con-sider practical D2D communication scenarios, we propose a wireless video sharing framework where cache-enabled users can store and exchange popular video files through D2D communica-tion. We investigate the potential effects of the TPC and the introduced D2D layer on the network EE and SE. We will also observe that the energy-harvesting CR-based D2D communication network design will not only ease the spectrum shortage problem but will also result in a greener network thanks to its reliance on ambient energies.

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All-in-One Hate Speech Detectors May not be what You Want

Bouazizi M., Niida N., Ohtsuki T.

ACM International Conference Proceeding Series （ACM International Conference Proceeding Series）     165 - 170 2021年01月

　概要を見る

The detection of Hate speech has been an increasingly active research topic. The results reported by the state-of-the-art systems to automatically detect hateful contents achieved almost perfect performance on common data sets. However, "hate speech"is a very subjective term, and people with different backgrounds have different levels of tolerance to what constitutes hate. In this paper, we show the limitations of having a single classifier handling the problem of hate speech detection. We then propose to build classifiers customized for different people, instead of a single classifier. The main obstacle towards achieving such a goal is the scarcity of data. Therefore, we use transfer learning to overcome this issue and use very limited amount of annotated data to build these customized classifiers. In a first stage, we build a classifier on a large data set which classifies tweets into 3 classes: hate, offensive, clean, and which we refer to as the general classifier. In the second stage, we asked 3 annotators with different backgrounds to re-annotate a small sub-set of tweets (600 tweets) from the original one. We refer to this newly created data set as "the customized data set."We then fine-tune the general classifier on the customized data set and build the customized classifier for each annotator. The accuracy of classification of corresponding customized data set got 0.08, 0.06 and 0.11 higher than the general classifier. The result shows that it is possible to start with a general classifier, and adjusted it to each individual despite the very limited amount of the training data for him/her.

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A Deep Learning-Based Low Overhead Beam Selection in mmWave Communications

Echigo H., Cao Y., Bouazizi M., Ohtsuki T.

IEEE Transactions on Vehicular Technology （IEEE Transactions on Vehicular Technology）  70 （ 1 ） 682 - 691 2021年01月

ISSN  00189545

　概要を見る

Due to large amounts of available spectrum at high frequencies, millimeter-wave (mmWave) technology has gained extensive research attention in 5G communications, whereas mmWave links suffer from severe free space attenuation. Codebook-based beamforming techniques with multiple antennas can effectively alleviate this challenge with low computational complexity and low hardware cost. However, small delay and high-speed communications with beamforming techniques require beam alignment with small overhead so as to establish the wireless link quickly. In this context, this paper proposes a deep learning-based low overhead analog beam selection scheme by virtue of the super-resolution technology. To be concrete, deep neural networks are employed to conduct beam quality estimation based on partial beam measurements. Our proposed scheme can cover all the directions of arriving signals with low overhead by utilizing codebooks with different beam widths. Furthermore, for the purpose of further reducing the overhead, we formulate the beam quality prediction model based on the past beam sweepings. With these beam quality estimation and prediction model, the beam that achieves large signal-to-noise-power-ratio (SNR) can be selected based on partial beam measurements. Simulation results show that the proposed scheme can accurately estimate beam qualities and give high probability of optimal beam selections with low overhead.

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Discrete BP Polar Decoder Using Information Bottleneck Method

Yamada A., Ohtsuki T.

IEEE Access （IEEE Access）  9   10645 - 10656 2021年

　概要を見る

Polar code is one of the channel codes and is used in the 5th generation of mobile communication system (5G). This encoding scheme is based on the operation of channel polarization, and it is possible to achieve the capacity of arbitrary binary-input symmetric discrete memoryless channels. Compared with Turbo codes and LDPC (Low-Density Parity-Check) codes, the implementation of the encoder and decoder is easier. BP (Belief Propagation) decoding, one of the decoding methods of the polar codes, can be performed at high speed because it can decode in parallel. However, the disadvantages of the BP decoding are the hardware and computational complexities. The technique of quantization can be used to reduce complexities of hardware and calculation. One of the quantization methods is the information bottleneck method, which allows an observation variable to compressed one while trying to preserve the mutual information shared with a relevant variable. As a novel approach, the information bottleneck method is used in the design of quantizers for the BP decoding of LDPC codes. In this paper, we propose a discrete BP polar decoder that can use only unsigned integers in the decoding process by using the information bottleneck method. Thus, we can replace complex calculations of BP decoding with simple lookup tables. We also investigate the minimum bit width for quantization with negligible degradation and the suboptimal Eb/N0 for designing lookup tables, where Eb and N0 denote energy per bit and noise power density, respectively. The simulation results show that the proposed method can achieve almost the same error correcting capability compared with the BP decoding without compression in the range of low Eb/N0. Besides, we show that the proposed decoder can compress both channel outputs and BP messages with small loss compared with the uniform quantization decoder.

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Differentiable architecture search-based automatic modulation classification

Wei X., Luo W., Zhang X., Yang J., Gui G., Ohtsuki T.

IEEE Wireless Communications and Networking Conference, WCNC （IEEE Wireless Communications and Networking Conference, WCNC）  2021-March 2021年

ISSN  15253511

　概要を見る

Automatic modulation classification (AMC) is an essential and meaningful technology in the development of cognitive radio. It can judge the modulation mode according to the signal acquired by the receiver. In recent years, the deep learning (DL) method has been used to take the place of modulation signal recognition based on decision theory and pattern recognition, which has achieved very effective results. The development of the neural network classification model focuses on architectural engineering. Discovering state-of-the-art neural network architectures requires substantial prior knowledge and effort of human experts. Neural architecture search (NAS) can be viewed as a subdomain of automatic machine learning (AutoML), which uses a neural network to automatically adjust the structures and parameters to obtain a network that researchers need by following search strategies that maximize performance. In this paper, we propose a differentiable architecture search (DARTS) based AMC method. In addition, we also consider six other methods, including convolutional neural network (CNN), simple recurrent unit (SRU), a convolutional-recurrent neural network (CRFN-CSS), Residual Networks (ResNet), Inception Modules (Inception) and MobileNet. Simulation results show that the proposed method can achieve the optimal classification accuracy at low parameters and floating-point operations (FLOPs) without manual architecture engineering.

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Deep Learning-Based Channel Estimation for Massive MIMO Systems with Pilot Contamination

Hirose H., Ohtsuki T., Gui G.

IEEE Open Journal of Vehicular Technology （IEEE Open Journal of Vehicular Technology）  2   67 - 77 2021年

　概要を見る

In a time division duplex (TDD) based massive multiple-input multiple-output (MIMO) system, a base station (BS) is required to obtain accurate estimation of channel state information (CSI) for a user terminal (UT). Because of the time-varying nature of the channel, the length of pilot signals is limited and the number of orthogonal pilot signals is finite. Hence, the same pilot signals are required to be reused in neighboring cells and thus its channel estimation performance is deteriorated by pilot contamination from the neighboring cells. The minimum mean square error (MMSE) channel estimation can be used to reduce the influence of pilot contamination. However, it needs to know the covariance matrix of channels for all the UTs, which is unknown to the BS in practice. In this paper, we propose two methods of deep learning aided channel estimation to reduce the influence of pilot contamination. One method uses a neural network consisting of fully connected layers, while the other method uses a convolutional neural network (CNN). The neural network, particularly the CNN, plays a role in extracting features of the spatial information from the contaminated signals. The former method is better in terms of the training speed, however, the latter one can estimate the channel more accurately. We evaluate the proposed methods under two scenarios, i.e., perfect timing synchronization and imperfect one. Simulation results confirm that the proposed methods are better than the LS and covariance estimation methods via normalized mean square error (NMSE). In addition, we also investigate the impact of channel aging, and show that including some expected data into training datasets can avoid the great degradation of estimation quality.

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Decentralized Learning-based Scenario Identification Method for Intelligent Vehicular Communications

Zhou Y., Wangt Y., Liu P., Yang J., Ohtsuki T., Sari H.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2021-September 2021年

ISSN  15502252

　概要を見る

Scenario identification (SCI) is one of key techniques for intelligent vehicular communications (IVC) to maintain an effective and reliable operating state. Based on the deep learning (DL), it is a hotspot to identify scenarios of wireless communication using the characteristic quantity inherent in wireless channels. This paper proposes a decentralized learning-based SCI (DecentSCI) for IVC, relying on the algorithm of lightweight and model aggregation. By improving training efficiency and meanwhile reducing model complexity, the proposed method achieves low computing and communication, which is applicable for vehicular devices. Simulation results show that the training efficiency is upgraded by 97.15% and the model complexity is decreased by 90.25% at the cost of slight performance loss, i.e., 0.15%.

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Data Augmentation for Sentiment Analysis Using Sentence Compression-Based SeqGAN with Data Screening

Luo J., Bouazizi M., Ohtsuki T.

IEEE Access （IEEE Access）  9   99922 - 99931 2021年

　概要を見る

Sentiment analysis refers to the process of automatically identifying the emotions expressed by people. Its accuracy is highly dependent on the amount of training data. However, it takes time and cost for humans to collect a large number of data. Many research works used generative models to generate a large amount of data based on a small amount of data for sentiment analysis. However, training on long texts and inaccurate sentiment information that might be generated are two severe challenges. It is difficult to improve the sentiment analysis accuracy effectively. In this paper, we propose a novel data augmentation framework based on Sequence generative adversarial networks (SeqGAN) to improve the sentiment analysis accuracy when the dataset already has a certain amount of data and contains long texts. Penalty-based SeqGAN is used to generate high-quality and diversified text data. Long short-term memory (LSTM) networks with attention mechanisms are used to conduct sentence compression for the training data of SeqGAN. A sentiment dictionary is used to retain the sentiment words for compressed data. We also propose a data screening method to obtain more accurate data from the generated data. The results of the usability, novelty, and diversity of the generated data show that the proposed sentence compression method can help SeqGAN learn more information from the long text data. The data generated by the proposed framework improve the classification accuracy of four classifiers applied on two distinct text datasets.

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An Effective Radar Signal Recognition Method Using Neural Architecture Search

Zhang M., Luo W., Wang Y., Sun J., Yang J., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2021-September 2021年

ISSN  15502252

　概要を見る

Deep learning-based radar signal recognition is considered one of the important technologies in the field of electronic countermeasure (ECM). However, existing deep learning-based methods require much time to design a specific neural network by experts for recognizing radar signals. It is difficult to employ these methods in real application scenarios. To solve this problem, we proposed an effective radar signal recognition method using neural architecture search (NAS) to automatically design convolutional neural networks (CNN). Experiments are given to validate the proposed method via comparing with both machine learning and deep learning-based methods. Experimental results show that the proposed method can achieve the optimal accuracy with low parameters and floating-point operations.

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An Autoencoder-Based Fetal Heart Rate Detector for Noninvasive Recordings

Qureshi A.A., Wang L., Ohtsuki T., Owada K., Honma N., Hayashi H.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2021-January   60 - 63 2021年

ISSN  1557170X

　概要を見る

Antenatal fetal health monitoring primarily depends on the signal analysis of abdominal or transabdominal electrocardiogram (ECG) recordings. The noninvasive approach for obtaining fetal heart rate (HR) reduces risks of potential infections and is convenient for the expectant mother. However, in addition to strong maternal ECG presence, undesirable signals due to body motion activity, muscle contractions, and certain bio-electric potentials degrade the diagnostic quality of obtained fetal ECG from abdominal ECG recordings. In this paper, we address this problem by proposing an improved framework for estimating fetal HR from non-invasively acquired abdominal ECG recordings. Since the most significant contamination is due to maternal ECG, in the proposed framework, we rely on neural network autoencoder for reconstructing maternal ECG. The autoencoder endeavors to establish the nonlinear mapping between abdominal ECG and maternal ECG thus preserving inherent fetal ECG artifacts. The framework is supplemented with an existing blind-source separation (BSS) algorithm for post-treatment of residual signals obtained after subtracting reconstructed maternal ECG from abdominal ECG. Furthermore, experimental assessments on clinically-acquired subjects' recordings advocate the effectiveness of the proposed framework in comparison with conventional techniques for maternal ECG removal.

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Activity Detection using 2D LIDAR for Healthcare and Monitoring

Bouazizi M., Ye C., Ohtsuki T.

2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings （2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings）  2021年

ISSN  23340983

　概要を見る

Monitoring elderly people living alone is of the utmost importance given the amount of risk they are exposed to. Being aware of the activities of the elderly person in real time could help prevent/detect dangerous event that might occur such as falling. In this paper, we propose a method for activity detection using a 2D LIght Detection and Ranging (LIDAR) and deep learning. Unlike conventional work, where an activity refers to moving from one position to another, we use the term 'activity' to refer to a set of movements including walking, standing, falling and sitting. Not only does our approach detect these activities, but it also identifies a given person from his gait, and identifies unsteady gait (i.e., when he is about to fall or feeling dizzy). Throughout our experiments, we show that the proposed approach could reach an accuracy equal to 92.3% and 91.3% in activity and unsteady gait detection, respectively. It is also capable of identifying up to 3 people's gait with an accuracy equal to 92.4% using 10 seconds of walking data.

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A Novel Hybrid Deep Learning Model for Activity Detection Using Wide-Angle Low-Resolution Infrared Array Sensor

Muthukumar K.A., Bouazizi M., Ohtsuki T.

IEEE Access （IEEE Access）  9   82563 - 82576 2021年

　概要を見る

In this paper, we propose a deep learning-based technique for activity detection that uses wide-angle low-resolution infrared (IR) array sensors. Alongside with the main challenge which is how to further improve the performance of IR array sensor-based methods for activity detection, throughout this work, we address the following challenges: we employ a wide-angle infrared array sensor with peripheral vision in comparison to a standard IR array sensor. This makes activities at different positions have different patterns of temperature distribution, making it challenging to learn these different patterns. In addition, unlike previous works, our goal is to perform the activity detection using the least possible amount of information. While the conventional works use a time window equal to 10 seconds where a single event occurs, we aim to identify the activity using a time window of less than 1 second. Nevertheless, we aim to improve over the accuracy obtained in previous work by employing deep learning, while keeping the approach light for it to run on devices with low computational power. Therefore, we use a hybrid deep learning model well suited for the classification of distorted images because the neural network learns the features automatically. In our work, we use two IR sensors ( 32\times24 pixels), one placed on the wall and one on the ceiling. We collect data simultaneously from both the IR sensors and apply hybrid deep learning classification techniques to classify various activities including 'walking', 'standing', 'sitting', 'lying', 'falling', and the transition between the activities which is referred to as 'action change'. This is done in two steps. In the first step, we classify ceiling data and wall data separately as well as the combination of both (ceiling and wall) using a Convolutional Neural Network (CNN). In the second step, the output of the CNN is fed to a Long Short Term Memory (LSTM) with a window size equal to 5 frames to classify the sequence of activities. Through experiments, we show that the classification accuracy of the ceiling data, wall data, and combined data with the LSTM reach 0.96, 0.95, and 0.97, respectively.

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A novel compression CSI feedback based on deep learning for FDD massive MIMO systems

Wang Y., Zhang Y., Sun J., Gui G., Ohtsuki T., Adachi F.

IEEE Wireless Communications and Networking Conference, WCNC （IEEE Wireless Communications and Networking Conference, WCNC）  2021-March 2021年

ISSN  15253511

　概要を見る

Accurate channel state information (CSI) is necessary for frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. Existing deep learning-based CSI feedback methods, e.g., CSI sensing and recovery neural network (CsiNet), designed based on an autoencoder architecture, achieves higher feedback accuracy and reconstruction speed. However, this network needs to be retrained due to different communication scenarios and channel conditions, which is costly in practical deployment. To solve this problem, this paper proposes a deep learning-based modular adaptive multiple-rate (MAMR) compression CSI feedback framework. Extra padding modules are added at the base station to pad compressed CSI into different compression rates into the same dimensions, thereby realizing a general autoencoder performing variable-rate compression. Simulation results are given to confirm the effectiveness of the proposed method in terms of normalized mean square error.

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A Novel Approach for Inter-User Distance Estimation in 5G mmWave Networks Using Deep Learning

Bouazizi M., Yang S., Cao Y., Ohtsuki T.

Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021 （Proceeding - 2021 26th IEEE Asia-Pacific Conference on Communications, APCC 2021）     223 - 228 2021年

　概要を見る

Accurate localization of devices in 5G cellular networks is of that utmost importance. This is because location information is a key component of a variety of new emerging applications. In particular, collocation (or co-location) refers to the idea of identifying devices that are located within a certain range from one another. In this paper, we propose a novel technique for inter-user distance estimation that uses low-resolution and high-resolution beam energy-based images as location fingerprints. Our approach uses the beam energy-based images generated by different users to estimate the distance between each pair of them. Nevertheless, we explore the idea of using a deep learning technique referred to as super resolution applied on low-resolution beam energy-based images to enhance their resolution, thus identify collocated users with an accuracy comparable to that of higher resolution ones. More specifically, throughout our experiments, we generate images of resolution 4times 4 and 8times 8 and use these for distance estimation between users. Afterwards, we apply super resolution on images with size 4times 4 to improve their resolution, and compare their results to the ones obtained with the original 8times 8 images. For an area roughly equal to 60times 30 mathrm{m}, our proposed approach reaches an average mean squared error equal to 0.13 m. We also demonstrate how our proposed approach outperforms the conventional ones that rely on user location detection to measure the inter-user distance.

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A novel approach based on lightweight deep neural network for network intrusion detection

Zhao R., Li Z., Xue Z., Ohtsuki T., Gui G.

IEEE Wireless Communications and Networking Conference, WCNC （IEEE Wireless Communications and Networking Conference, WCNC）  2021-March 2021年

ISSN  15253511

　概要を見る

With the ubiquitous network applications and the continuous development of network attack technology, all social circles have paid close attention to the cyberspace security. Intrusion detection systems (IDS) plays a very important role in ensuring computer and communication systems security. Recently, deep learning has achieved a great success in the field of intrusion detection. However, the high computational complexity poses a major hurdle for the practical deployment of DL-based models. In this paper, we propose a novel approach based on a lightweight deep neural network (LNN) for IDS. We design a lightweight unit that can fully extract data features while reducing the computational burden by expanding and compressing feature maps. In addition, we use inverse residual structure and channel shuffle operation to achieve more effective training. Experiment results show that our proposed model for intrusion detection not only reduces the computational cost by 61.99% and the model size by 58.84%, but also achieves satisfactory accuracy and detection rate.

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Fast Beamforming Design Method for IRS-Aided mmWave MISO Systems

He Z., Huang H., Yang J., Gui G., Ohtsuki T., Adebisi B., Gacanin H.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2021-September 2021年

ISSN  15502252

　概要を見る

Intelligent reflecting surface (IRS)-aided millimeter-wave (mmWave) multiple-input single-output (MISO) is considered one of the promising techniques in next-generation wireless communication. However, existing beamforming methods for IRS-aided mm Wave MISO systems require high computational power, so it cannot be widely used. In this paper, we combine an unsupervised learning-based fast beamforming method with IRS-aided MISO systems, to significantly reduce the computational complexity of this system. Specifically, a new beamforming design method is proposed by adopting the feature fusion means in unsupervised learning. By designing a specific loss function, the beamforming can be obtained to make the spectrum more efficient, and the complexity is lower than that of the existing algorithms. Simulation results show that the proposed beamforming method can effectively reduce the computational complexity while obtaining relatively good performance results.

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Weighted-Beam Superposition for mmWave Massive MIMO-NOMA Systems

Dai H., Yin Y., Huang H., Yang J., Ohtsuki T., Sari H., Adachi F.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2021-September 2021年

ISSN  15502252

　概要を見る

Millimeter wave (mmWave) and massive multiple input multiple output (MIMO) are recognized as key technologies in the forthcoming beyond the fifth-generation (B5G) and the sixth-generation (6G) wireless networks. In this paper, a multibeam MIMO non-orthogonal multiple access (NOMA) scheme with weighted beam superposition for mmWave is proposed to enhance the system sum rate while ensuring fairness of users as much as possible. Specifically, a method of power allocation is adopted to guarantee the minimum demanded for the quality of service (QoS) of the weak users (lower channel gain)in each group. Furthermore, to improve further the sum rate after the QoS of the weak users is satisfied, the coefficient of strong users' beam gain are set to the largest value. In system simulations, we compare the performance of three multi-beam schemes, i.e, beam splitting, beam superposition and the proposed scheme, together with a single beam scheme, and a TDMA scheme at different levels of the SNR. The simulation results demonstrate that the system sum rate of the proposed method is much higher than TDMA scheme and competitive compared to the best scheme.

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Spatial degrees of freedom exploration and analog beamforming designs for signature spatial modulation

Cao Y., Ohtsuki T.

IEICE Transactions on Communications （IEICE Transactions on Communications）  E104B （ 8 ） 934 - 941 2021年

ISSN  09168516

　概要を見る

In this paper, we focus on developing efficient multi-configuration selection mechanisms by exploiting the spatial degrees of freedom (DoF), and leveraging the simple design benefits of spatial modulation (SM). Notably, the SM technique, as well as its variants, faces the following critical challenges: (i) the performance degradation and difficulty in improving the system performance for higher-level QAM constellations, and (ii) the vast complexity cost in precoder designs particularly for the increasing system dimension and amplitude-phase modulation (APM) constellation dimension. Given this situation, we first investigate two independent modulation domains, i.e., the original signal- and spatial-constellations. By exploiting the analog shift weighting and the virtual spatial signature technologies, we introduce the signature spatial modulation (SSM) concept, which is capable of guaranteing superior trade-offs among spectral- and cost-efficiencies, and system bit error rate (BER) performance. Besides, we develop an analog beamforming for SSM by solving the introduced unconstrained Lagrange dual function minimization problem. Numerical results manifest the performance gain brought by our developed analog beamforming for SSM.

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Remote Sensing of Heartbeat based on Space Diversity Using MIMO FMCW Radar

Yamamoto K., Endo K., Ohtsuki T.

2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings （2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings）  2021年

ISSN  23340983

　概要を見る

Remote sensing of heartbeat offers various applications in the medical and health care fields. To realize non-contact heartbeat detection, an FMCW (Frequency Modulated Continuous Wave) radar-based heartbeat detection method has been investigated. The conventional FMCW radar-based heartbeat detection method estimates a range from an FMCW radar to a subject and extracts heartbeat components from phase changes for the range. However, the range suitable for extracting heartbeat components can change over time due to respiration and body fluctuation. Thus, when the SNR (Signal-to-Noise Ratio) of heartbeat components over phase changes is low at the estimated range, the accuracy of heartbeat detection tends to degrade. In this paper, we propose a MIMO (Multiple-Input Multiple-Output) FMCW radar-based heartbeat detection method based on space diversity. A MIMO FMCW radar can estimate the range for multiple beam directions and obtain phase changes for a space specified with the range and the beam direction. The SNR of heartbeat components over phase changes differs from one space to another. Taking it into account, the proposed method detects heartbeat by exploiting the space diversity of phase changes. The experimental results showed that compared to the detection method using only one phase change, the proposed method using phase changes for multiple spaces detected heartbeat accurately, which is brought by the diversity effect of phase changes for multiple spaces.

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Overloadedwireless mimo switching for information exchanging through untrusted relay in secure wireless communication

TAKAHASHI A., TAKYU O., FUJIWARA H., FUJII T., OHTSUKI T.

IEICE Transactions on Communications （IEICE Transactions on Communications）  E104B （ 10 ） 1249 - 1259 2021年

ISSN  09168516

　概要を見る

Information exchange through a relay node is attracting attention for applying machine-to-machine communications. If the node demodulates the received signal in relay processing confidentially, the information leakage through the relay station is a problem. In wireless MIMO switching, the frequency spectrum usage efficiency can be improved owing to the completion of information exchange within a short time. This study proposes a novel wireless MIMO switching method for secure information exchange. An overloaded situation, in which the access nodes are one larger than the number of antennas in the relay node, makes the demodulation of the relay node difficult. The access schedule of nodes is required for maintaining the overload situation and the high information exchange efficiency. This study derives the equation model of the access schedule and constructs an access schedule with fewer time periods in the integer programming problem. From the computer simulation, we confirm that the secure capacity of the proposed MIMO switching is larger than that of the original one, and the constructed access schedule is as large as the ideal and minimum time period for information exchange completion.

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Noncontact heartbeat detection by Viterbi algorithm with fusion of beat-beat interval and deep learning-driven branch metrics

Yamamoto K., Ohtsuki T.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings （ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings）  2021-June   8308 - 8312 2021年

ISSN  15206149

　概要を見る

Heartbeat is one of essential vital signs to assess our health condition. Noncontact heartbeat detection is thus receiving a lot of attention in recent years, which motivates many researchers to investigate heartbeat detection via a Doppler radar. In this paper, to detect heartbeat with a high accuracy, we propose a Doppler radar-based heartbeat detection method by the Viterbi algorithm with a fusion of Beat-Beat Interval (BBI) and deep learning-driven Branch Metrics (BM). The Viterbi algorithm is a technique to estimate a sequence with maximum likelihood by using a pre-defined metric, namely, a BM. In the proposed method, we combine two BMs defined based on (i) a difference between two adjacent BBIs and (ii) an output probability of a deep learning model that judges whether a peak is caused by heartbeat or not. We apply the VIterbi algorithm with the fusion of the two BMs to the signal obtained by some signal processing. We experimentally confirmed that our method performed heartbeat detection with small Root Mean Squared Error (RMSE) between the estimated and actual BBIs.

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Non-contact Heartbeat Detection Based on Beam Diversity Using Multibeam Doppler Sensor

Kitagawa T., Yamamoto K., Ohtsuki T.

2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings （2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings）  2021年

ISSN  23340983

　概要を見る

Heartbeat detection is a promising technology for health care and is in great demand in many applications. For non-contact and non-invasive heartbeat detection, Doppler sensor-based heartbeat detection methods have been investigated extensively. However, since the conventional methods use a single beam Doppler sensor, the accuracy of heartbeat detection tends to degrade, when the SNR (Signal-to-Noise Ratio) of heartbeat components for the beam direction is low. In this paper, we propose a non-contact heartbeat detection method based on beam diversity using a multibeam Doppler sensor. The effects of noises and the corresponding SNRs of heartbeat components over the received signal differ from one beam to another. Based on this fact, the proposed method detects heartbeat by exploiting beam diversity of the received signals. The experimental results showed that compared to the detection method using a single beam, our proposed method using multiple beams detected heartbeat accurately, which indicates that exploiting the beam diversity can lead to the improvement of heartbeat detection accuracy.

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Multi-Rate Compression for Downlink CSI Based on Transfer Learning in FDD Massive MIMO Systems

Wang Y., Sun J., Wang J., Yang J., Ohtsuki T., Adebisi B., Gacanin H.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2021-September 2021年

ISSN  15502252

　概要を見る

Accurate downlink channel state information (CSI) is one of the essential requirements for harnessing the potential advantages of frequency-division duplexing (FDD) massive multi-input multi-output (MIMO) systems. The current state-of-art in this vibrant research area include the use of deep learning to compress and feedback downlink CSI at the user equipments (UEs). These approaches focus mainly on achieving CSI feedback with high reconstruction performance and low complexity, but at the expense of inflexible compression rate (CR). High training overheads and limited storage capacity requirements are some of the challenges associated with the design of dynamic CR, which instantaneously adapt to propagation environment. This paper applies transfer learning (TL) to develop a multi-rate CSI compression and recovery neural network (TL-MRNet) with reduced training overheads. Simulation results are presented to validate the superiority of the proposed TL-MRNet over traditional methods in terms of normalized mean square error and cosine similarity.

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Lightweight Network Design Based on ResNet Structure for Modulation Recognition

Lu X., Tao M., Fu X., Gui G., Ohtsuki T., Sari H.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2021-September 2021年

ISSN  15502252

　概要を見る

The problem of unknown modulation signal recognition has been received intensely attentions in next-generational intelligent wireless communications. The deep learning (DL) has been widely used in unknown modulation signal recognition due to its excellent performance in solving classification problems and the DL-based automatic modulation classification (AMC) had been proposed. However, DL-based AMC method usually has high space complexity and computational complexity, which limits DL-based AMC to miniaturized devices with limited storage and computing capability. Therefore, a lightweight residual neural network (LResNet) for AMC is proposed in this paper. The simulation results show that the model parameters of LResNet is about 4.8% of the traditional CNN network, and about 14.9% of the ResNet and the classification performance of LResNet improves more than 3% compared with the traditional CNN network and decreases less than 1.5% compared to the ResNet.

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Lightweight network and model aggregation for automatic modulation classification in wireless communications

Fu X., Gui G., Wang Y., Ohtsuki T., Adebisi B., Gacanin H., Adachi F.

IEEE Wireless Communications and Networking Conference, WCNC （IEEE Wireless Communications and Networking Conference, WCNC）  2021-March 2021年

ISSN  15253511

　概要を見る

This paper proposes a decentralized automatic modulation classification (DecentAMC) method using light network and model aggregation. Specifically, the lightweight network is designed by separable convolution neural network (S-CNN), in which the separable convolution layer is utilized to replace the standard convolution layer and most of the fully connected layers are cut off, the model aggregation is realized by a central device (CD) for edge device (ED) model weights aggregation and multiple EDs for ED model training. Simulation results show that the model complexity of S-CNN is decreased by about 94% while the average CCP is degraded by less than 1% when compared with CNN and that the proposed AMC method improves the training efficiency when compared with the centralized AMC (CentAMC) using S-CNN.

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HSRRS Classification Method Based on Deep Transfer Learning and Multi-Feature Fusion

Wang Z., Li Z., Wang Y., Li W., Yang J., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2021-September 2021年

ISSN  15502252

　概要を見る

Convolutional neural network (CNN) is one of the most important tools to accomplish high-spatial-resolution remote sensing (HSRRS) image classification tasks with their unique feature extraction and feature expression capabilities. However, the CNN-based classification method is very limited due to the acquisition of HSRRS images is difficult and the sample size is limited. In addition, the extraction of features by a single model is very limited, which limits the further improvement of classification performance. To solve the above problems, we propose ResNet50-InceptionV3 based on deep transfer learning and multi-feature fusion (TLMFFRI) model to apply for high-spatial-resolution remote sensing image classification. First, both ResNet50 and InceptionV3 are trained on the ImageNet dataset. Then, transfer the trained convolutional layers weights to the TLMFFRI model to fuse the features and realize the HSRRS image classification. Finally, we evaluate the method on the HSRRS dataset. Compared with ResNet50 based on transfer learning (TL-ResNet50) and InceptionV3 based on transfer learning (TL-InceptionV3), the proposed method achieved better classification performance.

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Fetal Heart Rate Detection Using First Derivative of ECG Waveform and Multiple Weighting Functions

Niida N., Wang L., Ohtsuki T., Owada K., Honma N., Hayashi H.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2021-January   434 - 438 2021年

ISSN  1557170X

　概要を見る

Fetal heart rate monitoring using the abdominal electrocardiograph (ECG) is an important topic for the diagnosis of heart defects. Many studies on fetal heart rate detection have been presented, however, their accuracy is still unsatisfactory. That is because the fetal ECG waveform is contaminated by maternal ECG interference, muscle contractions, and motion artifacts. One of the conventional methods is to detect the R-peaks from the integrated power of the frequency corresponding to the fetal heartbeats. However, the detection accuracy of the R-peaks is not enough. In this paper, we propose a method to generate the candidates of R-peaks using the first derivative of the signal and to pick up the estimated heartbeats by a multiple weighting function. The proposed multiple weighting function is designed by the Gaussian distribution, of which parameters are set from a grid search with the goal of minimizing the standard deviation of RR intervals (neighboring R-peaks intervals). The validation for the proposed framework has been evaluated on real-world data, which got the better accuracy than the conventional method that detects R-peaks from the integrated power and uses the weighting function produced by a fixed parameter of Gaussian distribution [12]. The averaged absolute error (AAE) which compares the estimated fetal heart rate and the reference fetal heart rate has been decreased by 17.528 bpm.

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Surveillance Plane Aided Air-Ground Integrated Vehicular Networks: Architectures, Applications, and Potential

Sun J., Liu F., Zhou Y., Gui G., Ohtsuki T., Guo S., Adachi F.

IEEE Wireless Communications （IEEE Wireless Communications）  27 （ 6 ） 122 - 128 2020年12月

ISSN  15361284

　概要を見る

© 2002-2012 IEEE. Future air-ground integrated vehicular network (AGVN) is a vital part of the expected ubiquitous communication system. The resources in AGVN are geographically separated and are often heterogeneous. Therefore, distributed management and cooperative management are key issues in a multi-tier based topology of AGVN. Recently, artificial intelligence (AI) shows advantages in facilitating the management of AGVN; however, adequate, real-time, and secure information for driving AI is still a big concern. In this article, we propose a conceptual enhanced AGVN by introducing a so-called surveillance plane to promote the management in the control plane. Thus, components such as virtual network functions (VNFs) and service function chaining (SFC) can get profits. Concepts and trials of utilizing the surveillance plane and side information are presented from three different aspects, namely networking, security, and application. We also summarize research directions in designing a more flexible management scheme by coupling the service, surveillance, and control planes of the enhanced AGVN.

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Dual-Ascent Inspired Transmit Precoding for Evolving Multiple-Access Spatial Modulation

Cao Y., Ohtsuki T., Quek T.Q.S.

IEEE Transactions on Communications （IEEE Transactions on Communications）  68 （ 11 ） 6945 - 6961 2020年11月

ISSN  00906778

　概要を見る

© 1972-2012 IEEE. In this article, we investigate the dual-ascent inspired transmit precoding (TPC) for multiple-access spatial modulation (MASM) in multiple-input multiple-output (MIMO) systems. Note that several novel TPC techniques have been developed in earlier works, to provide a solution for either the maximal-minimum Euclidean distance, or the quadratically constrained quadratic program problems. However, numerical results expose that the system performances degrade distinctly when applying these TPC techniques into MASM-MIMO. The main reason behind is that these TPC techniques are sensitive to the system dimensions and the quadratic constraints. In this context, we first recast the above challenging problems as an unconstrained problem by imposing a penalty over the quadratic constraints. Based on the primal-dual optimality theory, we next propose a Broyden-Fletcher-Goldfarb-Shanno (BFGS) aided dual-ascent approach for finding a global optimum solution to the unconstrained problem. Further, we introduce non-stationary time-varying TPC parameters to characterize an evolving MASM-MIMO in which the signals are multiplexed over a small coherence time, and thereby resulting in dual-ascent aided non-stationary TPC approach. Numerical results manifest that the proposed algorithms possess an inherent robustness to the increasing system dimension and quadratic constraint. Besides, simulation results show the benefits of our algorithms under different kinds of performance metrics.

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Evaluating smart grid renewable energy accommodation capability with uncertain generation using deep reinforcement learning

Liu Y., Guan X., Li J., Sun D., Ohtsuki T., Hassan M.M., Alelaiwi A.

Future Generation Computer Systems （Future Generation Computer Systems）  110   647 - 657 2020年09月

ISSN  0167739X

　概要を見る

© 2019 Elsevier B.V. Due to environment-friendliness, renewable energy like solar power and wind power is more and more introduced to energy systems all over the world. Simultaneously, high penetrations of wind and solar generation also have brought severe curtailment of wind and solar. How to alleviate curtailment of wind and solar is a crucial problem in evaluating accommodation capability of renewable energy, which reflects the extent of utilization of renewable energy and economic benefits. The uncertainty of renewable energy brings challenges to precisely describe renewable generation, which leads to difficulty in designing effective mechanisms for accommodation capability of renewable energy. Existing work suffers from high computation overhead from frequently updated data, and low precision of describing renewable energy, which leads to less effective policies for renewable energy accommodation and underestimated accommodation capability. To make the most of renewable energy, an algorithm AccCap-DRL based on deep reinforcement learning is proposed. AccCap-DRL partitions a distribution into segments by time intervals, employs WGAN to describe distributions of renewable energy data, and employs DDPG to obtain approximate policies for renewable energy accommodation in different scenarios. Simulation results from real power generation and users’ demand data show high effectiveness of the proposed algorithm, and high efficiency of evaluating accommodation capability.

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Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

Lu Y., Wang J., Liu M., Zhang K., Gui G., Ohtsuki T., Adachi F.

IEEE Transactions on Vehicular Technology （IEEE Transactions on Vehicular Technology）  69 （ 8 ） 8459 - 8467 2020年08月

ISSN  00189545

　概要を見る

© 1967-2012 IEEE. The ever-increasing amount of data in cellular networks poses challenges for network operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs)-based hard decision methods are difficult to undertake the task of QoE anomaly detection in the case of big data. To solve this problem, in this paper, we propose a KQIs-based QoE anomaly detection framework using semi-supervised machine learning algorithm, i.e., iterative positive sample aided one-class support vector machine (IPS-OCSVM). There are four steps for realizing the proposed method while the key step is combining machine learning with the network operator's expert knowledge using OCSVM. Our proposed IPS-OCSVM framework realizes QoE anomaly detection through soft decision and can easily fine-Tune the anomaly detection ability on demand. Moreover, we prove that the fluctuation of KQIs thresholds based on expert knowledge has a limited impact on the result of anomaly detection. Finally, experiment results are given to confirm the proposed IPS-OCSVM framework for QoE anomaly detection in cellular networks.

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An Infrared Array Sensor-Based Method for Localizing and Counting People for Health Care and Monitoring

Bouazizi M., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2020-July   4151 - 4155 2020年07月

ISSN  9781728119908

　概要を見る

© 2020 IEEE. To build a system for monitoring elderly people living alone, an important step needs to be done: identifying the presence/absence of the person being monitored and his location. Such task has several applications that we discuss in this paper, and remains very important. Several techniques were proposed in the literature. However, most of them suffer from issues related to privacy, coverage or convenience. In the current paper, we propose an infrared array sensor-based approach to detect the presence/absence of a person in a room. We used a wide angle low resolution sensor (i.e., 32×24 pixels) to collect heat-related information from the area monitored, and used Deep Learning (DL) to identify the presence of up to 3 people with an accuracy reaching 97%. Our approach also detects of the presence or absence of a person with a 100% accuracy. Nevertheless, it allows identifying the location of the detected people within a room of dimensions 4×7.4 m with a margin of 0.3 m.

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Online Recursive ICA Algorithm Used for Motor Imagery EEG Signal

Lin X., Wang L., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2020-July   502 - 505 2020年07月

ISSN  9781728119908

　概要を見る

© 2020 IEEE. Electroencephalogram (EEG) signals are important to study the activities of human brains. The independent component analysis (ICA) algorithm is a practical blind source separation (BSS) technique that can separate EEG sources from artifacts effectively. However, most traditional ICA algorithms assume that the mixing process is instantaneous and off-line. In this paper, a novel framework based on the extension of the online recursive ICA algorithm (ORICA) is proposed to apply for motor imagery (MI) EEG recording. The contributions are as follows. Firstly, we show ORICA's adaptability to accurate and effective source separation used for artifact-contaminated MI EEG recording. Secondly, to identify EOG signals on the output of source separation, the topographic map is presented to distinguish the target signals. The experimental results show that the proposed framework is able to be applied to process MI EEG recording in real-time situations.

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Joint Multiple Subspace-based BSS Method for Fetal Heart Rate Extraction from Non-invasive Recordings

Wang L., Ohtsuki T., Owada K., Honma N., Hayashi H.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2020-July   616 - 620 2020年07月

ISSN  9781728119908

　概要を見る

© 2020 IEEE. Despite the enormous potential applications, non-invasive recordings have not yet made enough satisfaction for fetal disease detection. This is mainly due to the fetal ECG signal is contaminated by the maternal electrocardiograph (ECG) interference, muscle contractions, and motion artifacts. In this paper, we propose a joint multiple subspace-based blind source separation (BSS) approach to extract the fetal heart rate (HR), so that it could greatly reduce the effect of maternal ECG and motion artifacts. The approach relies on the estimation of the coefficient matrix formulated as the tensor decomposition in terms of multiple datasets. Since the objective function takes the coupling information from the stacking of the covariance matrix for multiple datasets into account, estimating the coefficient matrices is fulfilled not only on dependence across multiple datasets, but also can combine the extracted components across four different datasets. Numerical results demonstrate that the proposed method can achieve a high extracted HR accuracy for each dataset, when compared to some conventional methods.

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Deep Reinforcement Learning for Economic Dispatch of Virtual Power Plant in Internet of Energy

Lin L., Guan X., Peng Y., Wang N., Maharjan S., Ohtsuki T.

IEEE Internet of Things Journal （IEEE Internet of Things Journal）  7 （ 7 ） 6288 - 6301 2020年07月

　概要を見る

© 2014 IEEE. With the high penetration of large-scale distributed renewable energy generation, the power system is facing enormous challenges in terms of the inherent uncertainty of power generation of renewable energy resources. In this regard, virtual power plants (VPPs) can play a crucial role in integrating a large number of distributed generation units (DGs) more effectively to improve the stability of the power systems. Due to the uncertainty and nonlinear characteristics of DGs, reliable economic dispatch in VPPs requires timely and reliable communication between DGs, and between the generation side and the load side. The online economic dispatch optimizes the cost of VPPs. In this article, we propose a deep reinforcement learning (DRL) algorithm for the optimal online economic dispatch strategy in VPPs. By utilizing DRL, our proposed algorithm reduced the computational complexity while also incorporating large and continuous state space due to the stochastic characteristics of distributed power generation. We further design an edge computing framework to handle the stochastic and large-state space characteristics of VPPs. The DRL-based real-time economic dispatch algorithm is executed online. We utilize real meteorological and load data to analyze and validate the performance of our proposed algorithm. The experimental results show that our proposed DRL-based algorithm can successfully learn the characteristics of DGs and industrial user demands. It can learn to choose actions to minimize the cost of VPPs. Compared with the deterministic policy gradient algorithm and DDPG, our proposed method has lower time complexity.

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Deep Clustering with LSTM for Vital Signs Separation in Contact-free Heart Rate Estimation

Ye C., Gui G., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2020-June 2020年06月

ISSN  9781728150895

　概要を見る

© 2020 IEEE. So far, most separation approaches of vital signs such as heartbeat and respiration, are implemented based on linear mixtures. However, some literatures have reported that non-linear mixtures actually occur in the associated applications, e.g., heart rate (HR) estimation with Doppler radar, where the simple linear demixing architecture may limit the effect of source separation. In addition, the human motions during HR measurement further complicate the mixing processes. The issue motivates us to exploit a more suitable separation approach to deal with contact-free HR estimation, considering non-linear mixtures including motions. A semi-supervised deep clustering (DC) is proposed to separate the three mixed sources of heartbeat, respiration, and motions, by segmenting the spectrogram of Doppler signal. First, through training a deep recurrent neural network (RNN) with long short-term memory (LSTM) via heartbeat/respiration-only data, the embeddings to each frame-sample from spectrogram can be acquired, which enables feature optimization in a lower dimensional space. Then, in the test phase, K-means clusters the embeddings associated with each source, to infer the masks used for spectrogram segmentation. The proposed deep clustering has three main strengths: It (i) gets rid of the restriction of mixture class, relying on data mining; (ii) can handle three-source mixtures by training two sorts of source-independent samples; (iii) only requires the mixtures from single-channel. The HR measurement experiments on subjects' sitting still and typing, validate the improvements of accuracy and robustness by our proposal, over some prevailing approaches in signal decomposition or separation.

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Transfer Learning for Semi-Supervised Automatic Modulation Classification in ZF-MIMO Systems

Wang Y., Gui G., Gacanin H., Ohtsuki T., Sari H., Adachi F.

IEEE Journal on Emerging and Selected Topics in Circuits and Systems （IEEE Journal on Emerging and Selected Topics in Circuits and Systems）  10 （ 2 ） 231 - 239 2020年06月

ISSN  21563357

　概要を見る

© 2011 IEEE. Automatic modulation classification (AMC) is an essential technology for the non-cooperative communication systems, and it is widely applied into various communications scenarios. In the recent years, deep learning (DL) has been introduced into AMC due to its outstanding identification performance. However, it is almost impossible to implement previously proposed DL-based AMC algorithms without large number of labeled samples, while there are generally few labeled sample and large unlabel samples in the realistic communication scenarios. In this paper, we propose a transfer learning (TL)-based semi-supervised AMC (TL-AMC) in a zero-forcing aided multiple-input and multiple-output (ZF-MIMO) system. TL-AMC has a novel deep reconstruction and classification network (DRCN) structure that consists of convolutional auto-encoder (CAE) and convolutional neural network (CNN). Unlabeled samples flow from CAE for modulation signal reconstruction, while labeled samples are fed into CNN for AMC. Knowledge is transferred from the encoder layer of CAE to the feature layer of CNN by sharing their weights, in order to avoid the ineffective feature extraction of CNN under the limited labeled samples. Simulation results demonstrated the effectiveness of TL-AMC. In detail, TL-AMC performs better than CNN-based AMC under the limited samples. What's more, when compared with CNN-based AMC trained on massive labeled samples, TL-AMC also achieved the similar classification accuracy at the relative high SNR regime.

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Risk Assessment of Private Information Inference for Motion Sensor Embedded IoT Devices

Huang Y., Guan X., Chen H., Liang Y., Yuan S., Ohtsuki T.

IEEE Transactions on Emerging Topics in Computational Intelligence （IEEE Transactions on Emerging Topics in Computational Intelligence）  4 （ 3 ） 265 - 275 2020年06月

　概要を見る

© 2017 IEEE. In the era of advanced computer intelligence, Internet of Things (IoT) provides fantastic services to users. However, users are suffering a severe risk of private information inference, which is caused by the leakage of motion sensory data from IoT devices. Existing works of risk assessment of motion sensor based private information inference underestimates the risk because they ignore the possibility of using advanced Computational Intelligence techniques and the variety of languages with different input methods. In this paper, we assess the risk of motion sensor based private information inference by considering the variety of languages with different input methods, advanced Computational Intelligence techniques, and reinforcement learning of personal usage habits. We collect data from real users and run simulations to provide an authentic and up-to-date risk assessment. Based on the simulation result, we discuss the risky usage actions and possible defense strategies for the Internet of Things users.

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Multi-Configuration Selection Mechanisms and Analog Precoding for Signature Spatial Modulation

Cao Y., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2020-June 2020年06月

ISSN  9781728150895

　概要を見る

© 2020 IEEE. Spatia1 modulation (SM) has attracted enormous research attention due to its notable merits in terms of enabling higher spectral- and energy-efficiencies relative to the conventional MIMO technique. To provide higher system performance for SM, linear precoder is designed to shape the APM constellations observed at the receiver. However, numerical results reveal that: (1) The performance of precoding based approaches degrades dramatically for higher-level QAM constellations, which implicitly affect the spectral-efficiency improvement of SM. (2) The complexity of precoder optimization increases greatly with the system dimensions and the APM constellation dimensions, which indeed is cost inefficient. In this context, this paper introduces the signature spatial modulation (SSM) technique that inherits the properties of both the SM and the analog shift weighting techniques. To guarantee superior trade-offs among spectral-and cost-efficiencies, and bit error rate (BER) performance for SSM, we first develop efficient multi-configuration selection mechanisms by exploiting the spatial degree of freedoms (DoFs), as well as by leveraging the benefits of SM. To provide a global optimum solution against the constant-modulus constrained analog precoding problem, we next relax the precoding problem into unconstrained alternating minimization subproblems, followed by proposing Broyden-Fletcher-Goldfarb-Shanno (BFGS) aided alternating minimization algorithm to solve those subproblems. It should be noted that our proposed mechanism can realize an L-fold reduction in terms of the number of transmit antennas. In particular, theoretical results reveal that our proposal can reduce the complexities brought by the configuration selection operation up to 50% when considering L-QAM with L=4.

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Discrete Polar Decoder using Information Bottleneck Method

Yamada A., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2020-June 2020年06月

ISSN  9781728150895

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© 2020 IEEE. Polar codes are attracting much attention and being used for control channels of the 5th generation of mobile communication system (5G). As a feature, it is easier to implement encoder and decoder than Turbo codes and LDPC (Low Density Parity Check) codes. One of the decoding methods of polar codes is BP (Belief Propagation) decoding, which can decode in parallel, so that decoding can be performed at high speed. However, due to hardware limitation, calculations on the decoder get very complicated. This issue can be solved by using the information bottleneck method. This method compresses an observation variable to a quantized one while attempting to preserve the mutual information shared with a relevant random variable. In the conventional research, the information bottleneck method is applied to the BP decoding of the LDPC codes. In this paper, the information bottleneck method is used for the BP decoding of polar codes. The BP decoding of polar codes is distinct from that of LDPC codes. It has several types of the messages, and each time a message is updated, the decoding becomes more complex. By using the information bottleneck method, the decoder can compress the channel outputs and the messages of BP into unsigned integers while preventing degradation of the error correcting performance. Thus, we can reduce the complexity of calculation in the decoding process and easily implement the decoder. This paper also investigates the minimum bit width for quantization with negligible degradation and the suboptimal E-{b}/N-{0} for designing the lookup tables. These lookup tables are used for updating the messages. The simulation results show that the error correcting capability of the discrete polar decoders of the proposed method is negligibly degraded compared to the BP decoding without compression.

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Damping Factor Learning of BP Detection with Node Selection in Massive MIMO using Neural Network

Tachibana J., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2020-May 2020年05月

ISSN  9781728152073

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© 2020 IEEE. In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection performance deteriorates when there is a difference in the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can reduce the detection performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method can improve the detection performance compared to the conventional DNN-dBP with the same computational complexity. By computer simulation, it is shown that the proposed method significantly reduces the bit error rate (BER) performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method has the better BER performance than the conventional DNN-dBP with the same computational complexity.

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On-Road Object Identification with Time Series Automotive Millimeter-wave Radar Information

Nakamura T., Toyoda K., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2020-May 2020年05月

ISSN  9781728152073

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© 2020 IEEE. Identifying objects with radar is in great demand to avoid road accidents. Recent research tried to identify moving objects on a road by inputting radar information to a machine learning classifier. In the conventional method, the features used in the machine learning are extracted from the observed radar information with a short time interval. Since the movement of the objects is different depending on the objects, time series information is effective for classification, which has not been exploited before. In this paper, we propose an on-road object identification considering time series of radar information. We measured objects with 79.5 GHz millimeter-wave radar and extract features from a series of time windows by calculating the mean and variance of object information, i.e., velocity, distance, and signal power. The classification performance was evaluated with a dataset obtained by on-road experiments. It is shown that our method outperforms the conventional one and the proposed features significantly contribute to the accurate identification.

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Interference Control of LTE-LAA using Q-learning with HARQ

Wada K., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2020-May 2020年05月

ISSN  9781728152073

　概要を見る

© 2020 IEEE. LTE-LAA (LTE-Licensed Assisted Access) is the LTE (Long Term Evolution) to use an unlicensed band. However, in addition to the currently used license band, to use the unlicensed band, interference control with Wi-Fi that also uses the unlicensed bandwidth is required. A technique called LBT (Listen Before Talk) is considered as a technique of LTE-LAA interference control. However, since the channel occupation time (TXOP) of LTE-LAA is larger than that of Wi-Fi, there is a problem that Wi-Fi channel access is hindered. As a solution to this problem, one way is to increase the chances of channel access to Wi-Fi by establishing a waiting time of Tmute in LTE-LAA to reduce TXOP of LTE-LAA and postpone transmission. In this paper we propose an interference control method for LTE-LAA using Q-learning, which is one of the reinforcement learning techniques. In the proposed method, we select TXOP and Tmute using Q-learning. In the proposed method, the number of ACKs and that of NACKs in HARQ (Hybrid Automatic Repeat Request) are used as reward. In LTE-A the user terminal generates ACK / NACK according to the received radio frame and transmits it to the BS (base station). BS retransmits according to the number of ACKs / NACKs. Learning values of TXOP, Tmute of the LTE-LAA BS can be obtained so as to increase the number of ACKs or NACKs. When the LTE-LAA BS learns to increase the number of ACKs, the throughput of LTE-LAA is controlled to be high and that of Wi-Fi is controlled to be low. On the other hand, when the LTE-LAA BS learns to increase the number of NACKs, the throughput of LTE-LAA is controlled to be low and that of Wi-Fi is controlled to be high. Simulation result shows the proposed method using LTE-LAA UE (User Equipment) and Wi-Fi UE.

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User Association to Overcome Human Blockage at mmWave Cellular Networks

Yuva Kumar S., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2020-May 2020年05月

ISSN  9781728152073

　概要を見る

© 2020 IEEE. The large spectral bandwidth at millimeter-wave (mmWave) frequencies provides a mean to achieve very high data rates in wireless communication systems. A unique characteristic of mmWave is that mmWave links are very sensitive to blockage and have large propagation path loss, which exhibits low line-of-sight (LoS) probability, unstable connectivity and unreliable communication. To overcome such challenges, one of the existing solution is to associate the user equipment (UE) with other available Base Stations (BSs) by handover (HO) if the serving BS is blocked. In this paper, for a pedestrian scenario, we propose two reinforcement learning (RL) based user association algorithms, which accounts for the past experience of the blockage on the position of the UE. One focuses on the reward to increase the sum LoS probability and is named as Blockage-Aware User Association (BAUA). The other focuses on the reward to balance the tradeoff between the throughput and the LoS probability and is named as modified BAUA. Simulation results show that the BAUA algorithm increased sum LoS probability and the modified BAUA algorithm show better trade-off between the throughput and the LoS probability than the maximum Signal-to-Interference-plus-Noise Ratio (SINR) based and maximum-throughput based user association algorithms.

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Heartbeat Detection Technology for Monitoring Driver's Physical Condition

Tsuchiya K., Mochizuki K., Ohtsuki T., Yamamoto K.

SAE Technical Papers （SAE Technical Papers）  2020-April （ April ）  2020年04月

　概要を見る

© 2020 SAE International. All Rights Reserved. In recent years, the number of reported traffic accidents due to sudden deterioration in driver's physical condition has been increasing, it is expected to develop a system that prevents accidents even if physical condition suddenly changes while driving, or reduces damage through vehicle body control. For this purpose, it is necessary to detect sudden changes of the driver's physical condition, and research is being conducted widely. Among them, it is reported that some of such changes may appear in the heartbeat interval. In other words, by acquiring the driver's heartbeat interval in real time, it may be possible to detect the sudden changes, and reduce traffic accident. Even if a traffic accident occurs, the damage can be reduced by emergency evacuation immediately after detecting sudden changes. Therefore, we focused on the technology to detect the heartbeat interval with 24GHz microwave Doppler radar, which can detect heartbeat non-contactly while maintaining the interior design and passenger's privacy. Doppler radar with microwave is sensitive enough to detect heartbeat, however vibration noise is also superimposed on the sensor signal easily. Thus, in general algorithm studied widely, the problem is that the accuracy of detecting the heartbeat interval deteriorates particularly while driving, because the car vibration is superimposed on the frequency band used for heartbeat analysis, and the SNR (Signal to Noise power Ratio) decreases. To solve this problem, we developed an algorithm that extracts power spectrum over wide band including harmonic components of heartbeat and detects the peaks. This time, our new peak-searching method, in which multiple peak intervals are selected simultaneously based on the previous estimation results, suppresses false detection of peaks caused by vibration noise. As a result, the error rate of the heartbeat interval detection was reduced by half compared to the conventional algorithm in which a peak is detected based on previous heartbeat intervals.

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Blind Source Separation on Non-Contact Heartbeat Detection by Non-Negative Matrix Factorization Algorithms

Ye C., Toyoda K., Ohtsuki T.

IEEE Transactions on Biomedical Engineering （IEEE Transactions on Biomedical Engineering）  67 （ 2 ） 482 - 494 2020年02月

ISSN  00189294

　概要を見る

© 1964-2012 IEEE. In non-contact heart rate (HR) monitoring via Doppler radar, the disturbance from respiration and/or body motion is treated as a key problem on the estimation of HR. This paper proposes a blind source separation (BSS) approach to mitigate the noise effect in the received radar signal, and incorporates the sparse spectrum reconstruction to achieve a high-resolution of heartbeat spectrum. The proposed BSS decomposes the spectrogram of mixture signal into original sources, including heartbeat, using non-negative matrix factorization (NMF) algorithms, through learning the complete basis spectra (BS) by a hierarchical clustering. In particular, to exploit the temporal sparsity of heartbeat component, two variants of NMF algorithms with sparseness constraints are applied as well, namely sparse NMF and weighted sparse NMF. Compared with usual BSS, our proposed BSS has three advantages: 1) clustering-induced unsupervised manner; 2) compact demixing architecture; and 3) merely requiring single-channel input data. In addition, the HR estimation method using our proposal delivers more satisfactory precision and robustness over other existing methods, which is demonstrated through the measurements of distinguishing people's activities, gaining both smallest absolute errors of HR estimation for sitting still and typewriting.

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Non-Contact Heartbeat Detection by Heartbeat Signal Reconstruction Based on Spectrogram Analysis with Convolutional LSTM

Yamamoto K., Ohtsuki T.

IEEE Access （IEEE Access）  8   123603 - 123613 2020年

　概要を見る

© 2013 IEEE. Heartbeat detection is one of key techniques to monitor our health condition in daily life, and demands for this technique have increased year and year. Thanks to the non-contact and non-invasive features, various Doppler sensor-based detection methods have been investigated so far. However, the heartbeat detection accuracy of the conventional methods could get degraded due to the low SNR (Signal-to-Noise Ratio) of heartbeat components. Thus, even after some signal processing, non-heartbeat components still remain over such processed signal, which could degrade the heartbeat detection accuracy. In particular for the subjects with low HR (Heart Rate), the estimated HR tends to be higher than the ground truth HR due to such non-heartbeat components, though the conventional methods have mainly focused on the heartbeat detection against the subjects with the normal HR higher than 50 bpm (Beats Per Minute). In this paper, to accurately detect heartbeat even with low HR via a Doppler sensor, we propose a heartbeat detection method based on heartbeat signal reconstruction with convolutional LSTM (Bidirectional-Long Short-Term Memory). In the proposed method, to reconstruct a heartbeat signal based on the periodicity of heartbeat and the spectrum distribution peculiar to heartbeat, successive spectrograms that might be due to heartbeat is used as an input to convolutional LSTM. In addition, for better reconstruction of a heartbeat signal, the previously estimated RRI (R-R Interval) is also used as a feature in the proposed deep learning model with convolutional LSTM. Through the experiments, we confirmed that our proposed method accurately detected heartbeat against 17 subjects including the ones with the HR lower than 50 bpm.

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Low-Complexity Subspace MMSE Channel Estimation in Massive MU-MIMO System

Deng Y., Ohtsuki T.

IEEE Access （IEEE Access）  8   124371 - 124381 2020年

　概要を見る

© 2013 IEEE. Massive multi-user multiple-input multiple-output (massive MU-MIMO) technology is considered as a promising enabler to fulfill the rapid growth of traffic requirement for wireless mobile communications. The massive MU-MIMO system can achieve unlimited capacity when the base station (BS) has accurate channel state information (CSI). In time-division-duplex (TDD) mode, the BS estimates CSI by receiving pilot signals sent from user terminals (UEs). However, because of using non-orthogonal pilots, pilot contamination happens to degrade the quality of the CSI estimation. To deal with pilot contamination problem, a low-complexity subspace minimum mean square error (MMSE) estimation method is proposed in this paper. Specifically, our approach operates the MMSE estimation in a low-dimensional subspace to avoid large matrix manipulation. Meanwhile, subspace projection helps to discriminate the desired signal and interfering signals in the power domain. Interference analysis shows the MMSE estimation can achieve interference-free estimation even in a low-dimensional subspace with a large number of BS antennas, and non-overlapping angles of arrival (AoAs) between desired and interfering UEs. Furthermore, thanks to the low-rank property of the channel covariance matrix in massive MU-MIMO systems, a two-step covariance matrix subspace projection method is proposed for further computational complexity reduction. The complexity analysis and simulation results indicate that our proposed approach has better channel estimation accuracy with lower complexity than the conventional MMSE estimation when the number of BS antennas is large.

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Learning and Analysis of Damping Factor in Massive MIMO Detection Using BP Algorithm with Node Selection

Tachibana J., Ohtsuki T.

IEEE Access （IEEE Access）  8   96859 - 96866 2020年

　概要を見る

© 2013 IEEE. In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection performance deteriorates when there is a difference of the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can improve the detection performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method has the same detection performance with low computational complexity as the conventional DNN-dBP. By computer simulation, it is shown that the proposed method significantly improves the bit error rate (BER) performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method can show the same BER performance with low computational complexity as the conventional DNN-dBP. We also investigate the distribution of the trained damping factors and evaluate the tendency of that.

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Energy efficiency analysis in cache-enabled D2D-Aided heterogeneous cellular networks

Panahi F.H., Panahi F.H., Ohtsuki T.

IEEE Access （IEEE Access）  8   19540 - 19554 2020年

　概要を見る

© 2013 IEEE. Small cells (SCs) offer a promising approach to meeting the exponentially growing data rate demands. However, dense deployment of SCs can degrade the energy-efficiency (EE) of the network due to the additional deployed base stations (BSs). Under heterogeneous network (HetNet) deployments, SCs can be dynamically switched off for energy saving when traffic load decreases. We do this by defining a load-dependent transmission power coefficient (TPC) for SC BSs. In addition, with Device-to-Device (D2D) communication, mobile users in proximity can establish a direct link and bypass the BSs, thereby offloading the network infrastructure and providing further improvement of EE. In this paper, the objective is to rely on both D2D communications and sleeping SC BSs to offload traffic from the mains powered macrocell BSs leading to energy saving in the network. Furthermore, in order to consider D2D requirements under the practical application scenarios, we assume a framework for wireless video sharing, where users can store popular video files and share files via D2D communication. To begin with, we derive the EE expression of the cache-enabled D2D-aided HetNet. Then, to maximize the EE of the cache-enabled D2D-aided HetNet, the optimal load-dependent TPC for SC BSs is obtained under constraints on both network's coverage and rate. Using simulations, we will investigate the potential effects of the TPC defined for SC BSs as well as the introduced D2D layer on the network EE.

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ECG Signal Reconstruction via Doppler Sensor by Hybrid Deep Learning Model with CNN and LSTM

Yamamoto K., Hiromatsu R., Ohtsuki T.

IEEE Access （IEEE Access）  8   130551 - 130560 2020年

　概要を見る

© 2013 IEEE. An Electrocardiogram (ECG) is a typical method used to detect heartbeat, and an ECG signal analysis enables the detection of some heart diseases. However, the ECG-based heartbeat detection requires device attachment, which is not preferred for daily use. A Doppler sensor could be a device used to enable the non-contact heartbeat detection. In this paper, we propose a Doppler sensor-based ECG signal reconstruction method by a hybrid deep learning model with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). An ECG signal can be reconstructed by relating features of a heartbeat signal obtained by a Doppler sensor to those of the ECG signal. Thus, we construct the deep learning model that extracts the spatial and temporal features from the heartbeat signal by CNN and LSTM. Based on the extracted features, the ECG signal is reconstructed. We conducted experiments to observe heartbeat against 9 healthy subjects without heart disease. The experimental results showed that our method performed ECG signal reconstruction with a correlation coefficient of 0.86 between the reconstructed and actual ECG signals, even without attaching devices. The results indicate that it is possible to remotely reconstruct an ECG signal from a heartbeat signal via a Doppler sensor.

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CNN-based respiration rate estimation in indoor environments via MIMO FMCW radar

Yamamoto K., Toyoda K., Ohtsuki T.

2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings （2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings）  2019年12月

ISSN  9781728109626

　概要を見る

© 2019 IEEE. Respiration is known to reflect our health condition, which motivates researchers to develop various radar-based respiration rate estimation methods. However, these conventional methods do not work, when a subject is not right in front of the radar. In this paper, we propose a novel CNN (Convolutional Neural Network)-based respiration rate estimation method in indoor environments via a MIMO (Multiple-Input Multiple-Output) FMCW (Frequency Modulated Continuous Wave) radar. A MIMO FMCW radar can estimate the DoA (Direction of Arrival) and the distance between a MIMO FMCW radar and an object. Thus, the respiration can be captured based on the phase variation at a subject's location. However, even when the advanced signal processing, e.g., MUSIC (MUltiple SIgnal Classification) algorithm, is used, it is difficult to estimate the DoA and the distance in indoor environments due to the large effect of multipath. To deal with this problem, in the proposed method, phase variations against various locations are calculated from the received signals of a MIMO FMCW radar, and then spectrograms are calculated from the phase variations. Each spectrogram is subsequently fed into the CNN that outputs the respiration rates, e.g., 0.1 Hz, 0.2 Hz, and non-respiration, i.e., a spectrogram without the effect of respiration, where is one of the deep learning techniques that have been successfully applied to the image recognition. Through the experiments we confirmed that except for when microwaves were not transmitted directly toward a subject's chest due to furniture, the proposed method accurately estimated the respiration rate, regardless of the situation.

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FMCW radar-based anomaly detection in toilet by supervised machine learning classifier

Takabatake W., Yamamoto K., Toyoda K., Ohtsuki T., Shibata Y., Nagate A.

2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings （2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings）  2019年12月

ISSN  9781728109626

　概要を見る

© 2019 IEEE. With the increase in the population of elderly, a technique for monitoring them is receiving more and more attention all over the world. In particular, it is highly demanded to develop the system that detects a fainting elderly in a toilet, since such an accident is likely to get worse due to the late detection. In this paper, we propose an anomaly detection method in toilet using an FMCW(Frequency Modulated Continuous Wave) radar, which can measure the distance between an object and an FMCW radar. As anomalies, we determine as the situations where a subject faints while stooping on the toilet seat, leaning on the backrest, leaning on the side wall, and lying on the floor after the fall. With an FMCW radar, a fainting subject could be detected based on whether the estimated distance between a subject and an FMCW radar is almost constant. However, a subject might be sitting still, and also the distance to not a subject but a floor could be estimated depending on a subject's posture. To address this problem, in the proposed method, the time when the estimated distance is almost constant is detected, and then the detected time is classified into the time due to a faint or a non-faint by a supervised machine learning classifier. The used features are extracted based on the data, e.g., the estimated distance, just before the detected time. To evaluate the anomaly detection accuracy of the proposed method, we conducted the experiments to observe not only the aforementioned abnormal behavior but also the normal behavior, e.g., the button operation and sitting down. As a result, in terms of the classification of abnormal and normal behavior, our proposed method achieved the F- measure of 0.91, where F-measure is the overall score of recall and precision rates.

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Dual-ascent inspired iterative transmit precoding approaches for multiple access spatial modulation

Cao Y., Ohtsuki T.

2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings （2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings）  2019年12月

ISSN  9781728109626

　概要を見る

© 2019 IEEE. In this paper, we investigate the multiple access spatial modulation (MASM) in multiple-input multiple-output (MIMO) systems with the aid of dual-ascent inspired iterative transmit precoding (TPC) methods. To find a global optimum against the maximum minimum Euclidean distance (MMD) problem, as well as to reach a fast convergence rate with low-complexity, we first study the peculiarities of the convex optimization methods that take the dual- ascent method into account. This beneficially provides us an insight into developing a new dual- ascent method which is capable of bringing robustness to the existing dual-ascent method. Then, we introduce an evolving MASM-MIMO system by imposing non-stationary time-varying TPC parameters, and thereby resulting in a guaranteed Euclidean distance (GED) aided TPC method. Numerical results reveal that our proposals are capable of promoting a significant bit error rate (BER) performance improvement comparing with the existing methods for the MASM-MIMO systems, as well as facilitating a fast convergence with low- complexity.

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Orthogonality Structure Designs for Generalized Precoding Aided Spatial Modulation

Cao Y., Ohtsuki T.

IEEE Wireless Communications Letters （IEEE Wireless Communications Letters）  8 （ 5 ） 1406 - 1409 2019年10月

ISSN  21622337

　概要を見る

© 2012 IEEE. In this letter, we propose an orthogonality structure design (OSD) for the generalized precoding aided spatial modulation (GPSM) to overcome the performance degradation caused by correlated fading channels. To improve the bit error rate (BER) performance of GPSM system, we devise an efficient receive antenna subset selection (RAS) technique relied on the mechanisms of maximizing the equivalent channel gain. To facilitate a better trade-off between performance and computational complexity, we study the peculiarities of the Hermitian matrix which provides an important insight for conceiving orthogonality conditions to the channel matrix of GPSM system. We prove that the performance gain in terms of BER can be attained with the aids of the proposed efficient RAS, and OSD-aided schemes with low-complexity. In particular, our results demonstrate that the OSD-aided scheme is capable of providing significant BER performance improvement for GPSM systems, compared to that of current strategies.

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Low complexity and high accuracy channel interpolation with dividing URA into Small URAs for 3D Massive MIMO

Kuriyama M., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2019-September 2019年09月

ISSN  9781728112206

　概要を見る

© 2019 IEEE. Massive multiple-input multiple-output (MIMO) is a technology that uses many antennas at base station (BS), and can realize advanced beamforming and spatial multiplexing. However, since Massive MIMO has many antennas, there exist problems such as the increase of overhead including pilot signals used for channel estimation and feedback of channel state information (CSI). To reduce the overhead, there is a technique of transmitting pilot signals only from some antennas and obtaining channels of the remaining antennas by the minimum mean square error (MMSE) interpolation based on spatial correlation. In addition, to perform the MMSE interpolation with low computational complexity, there is a method of interpolating channels by dividing the uniform rectangular antenna array (URA) into the vertical and horizontal linear directions. However, there is a problem that channel interpolation accuracy becomes low with dividing into the linear arrays. In this paper, we propose a method of channel interpolation with dividing the entire URA into a set of smaller URAs where interpolation is performed in each URA. Through computer simulation we show that the proposed method reduces the computational complexity compared with that without dividing, and improves the channel interpolation accuracy compared with that with dividing into linear arrays.

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Multi-class sentiment analysis on twitter: Classification performance and challenges

Bouazizi M., Ohtsuki T.

Big Data Mining and Analytics （Big Data Mining and Analytics）  2 （ 3 ） 181 - 194 2019年09月

ISSN  20960654

　概要を見る

© 2020 The author(s). Sentiment analysis refers to the automatic collection, aggregation, and classification of data collected online into different emotion classes. While most of the work related to sentiment analysis of texts focuses on the binary and ternary classification of these data, the task of multi-class classification has received less attention. Multi-class classification has always been a challenging task given the complexity of natural languages and the difficulty of understanding and mathematically "quantifying"how humans express their feelings. In this paper, we study the task of multi-class classification of online posts of Twitter users, and show how far it is possible to go with the classification, and the limitations and difficulties of this task. The proposed approach of multi-class classification achieves an accuracy of 60.2% for 7 different sentiment classes which, compared to an accuracy of 81.3% for binary classification, emphasizes the effect of having multiple classes on the classification performance. Nonetheless, we propose a novel model to represent the different sentiments and show how this model helps to understand how sentiments are related. The model is then used to analyze the challenges that multi-class classification presents and to highlight possible future enhancements to multi-class classification accuracy.

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MUSIC-based Non-contact Heart Rate Estimation with Adaptive Window Size Setting

Yamamoto K., Toyoda K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）     6073 - 6076 2019年07月

ISSN  9781538613115

　概要を見る

© 2019 IEEE. Continuous HR (Heart Rate) monitoring enables the stress estimation in daily life. A Doppler sensor could be a key device to facilitate the non-contact HR estimation. As one of the Doppler sensor-based HR estimation methods, we have previously proposed a MUSIC (MUltiple SIgnal Classification)-based HR estimation method. MUSIC is the algorithm widely used as a tool to estimate DOA (Direction of Arrival). In our previous method, MUSIC spectrum is calculated in each sliding window, and then HR is estimated by the maximum peak detection over the MUSIC spectrum. However, when HR changes largely within the window, several peaks due to heartbeats appear over the MUSIC spectrum, which might cause the incorrect peak detection. Hence, an adaptive window is required so that only one peak appears. In this paper, we propose a MUSIC-based HR estimation method with an adaptive window size setting. When several peaks due to heartbeats appear over the MUSIC spectrum, our proposed method shortens the time window and re-calculates the MUSIC spectrum, which is repeated until only one peak appears. The experimental results showed that our method outperformed not only our previous one but also the other existing MUSIC-based HR estimation one in terms of the estimation accuracy of the HR, the stress indexes CVI (Cardiac Vagal Index) and CSI (Cardiac Sympathetic Index).

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Overloaded Wireless MIMO Switching for Secure Wireless Communication Exchanging to Untrusted Relay

Takahashi A., Sasamori F., Handa S., Takyu O., Fujii T., Ohtsuki T.

International Conference on Ubiquitous and Future Networks, ICUFN （International Conference on Ubiquitous and Future Networks, ICUFN）  2019-July   121 - 125 2019年07月

ISSN  9781728113395

　概要を見る

© 2019 IEEE. Wireless MIMO Switch makes the information exchange among terminals by two time steps. As a result, the highly efficiency of information exchanging is achieved. If the relay is impersonated by eavesdropper, it can exploit the information during the relay process, where the relay exploiting the information is the untrusted relay. In the proposed wireless MIMO switch, the stations whose number is the number of antennas plus one access the relay. As a result, the relay station hardly decodes each information owing to the overloaded access. In each station, firstly, one information signal is decoded by the interference cancellation of its own signal and the zero forcing of relay filter. Since one information signal is shared to all the stations, each station can demodulate the information because the overloaded condition is mitigated by the shared information signal. We confirm the effect of proposed scheme by computer simulation.

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Novel CA-CFAR Approach for Improvement of Doppler Sensor-based Heart Rate Variability Estimation

Hiromatsu R., Yamamoto K., Toyoda K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）     796 - 799 2019年07月

ISSN  9781538613115

　概要を見る

© 2019 IEEE. Heart rate variability is one of major physiological parameters to reflect our stress, which has motivated researchers to investigate a Doppler sensor-based non-contact heartbeat interval estimation algorithm. As one of such methods, we have previously proposed a spectrogram-based method. In this method, the spectrum that might be due to heartbeats is integrated over a spectrogram, and then heartbeat interval is estimated by detecting peaks over the integrated spectrum. However, when a subject moves, undesired peaks with large amplitude appear, which causes the incorrect peak detection. As one of the technique to eliminate the undesired peaks with large amplitude, there is CA-CFAR (Cell Average-Constant False Alarm Rate). CA-CFAR is the technique to detect a signal, when the amplitude of a signal exceeds a threshold calculated with average amplitude of signals before and after the investigated one. However, depending on the duration of body movements, the influence of body movements might be included within the signals used for the threshold calculation, which might results in the detection failure of undesired peaks. This is because the length of GT (Guard Time) is fixed, where GT is the time to prevent the signal used for the threshold calculation from including the investigated signal components. To solve this problem, we propose a novel CA-CFAR in which the length of GT is set as the latest peak interval and only the signal before the investigated one is used so that the influence of body movements does not affect the threshold calculation. Through the experiments where a subject moves, i.e., typing, we confirmed that our spectrogram-based heart rate variability estimation method with the proposed CA-CFAR outperformed the one with CA-CFAR based on fixed GT by the RMSE (Root Mean Squared Error) between the estimated heartbeat interval and the ground truth value of the one.

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Performance Evaluation of Information-Sharing Scheme among Multiple Destinations with Mirroring Null Steering in Single Antenna Networks

Noguchi T., Takyu O., Fujii T., Ohtsuki T.

International Conference on Information Networking （International Conference on Information Networking）  2019-January   417 - 420 2019年05月

ISSN  9781538683507

　概要を見る

© 2019 IEEE. This paper clarifies the secure capacity in a secure information sharing among multiple destinations by using artificial noise and mirroring null steering (MNS) constructed by cooperative relays in single antenna networks. The MNS can selectively pass the desired signal to all destinations and remove artificial noise. Because the destinations archive high-quality received signals and the other systems cannot, secure information sharing can be achieved from a physical layer security perspective.

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Identification of Darknet Markets' Bitcoin Addresses by Voting Per-Address Classification Results

Kanemura K., Toyoda K., Ohtsuki T.

ICBC 2019 - IEEE International Conference on Blockchain and Cryptocurrency （ICBC 2019 - IEEE International Conference on Blockchain and Cryptocurrency）     154 - 158 2019年05月

ISSN  9781728113289

　概要を見る

© 2019 IEEE. Bitcoin is a decentralized digital currency whose transactions are recorded in a common ledger, so called blockchain. Due to the anonymity and lack of law enforcement, Bitcoin has been misused in darknet markets which deal with illegal products, such as drugs and weapons. Therefore from the security forensics aspect, it is demanded to establish an approach to identify newly emerged darknet markets' transactions and addresses. In this paper, we thoroughly analyze Bitcoin transactions and addresses related to darknet markets and propose a novel identification method of darknet markets' addresses. To improve the identification performance, we propose a voting based method which decides the labels of multiple addresses controlled by the same user based on the number of the majority label. Through the computer simulation with more than 200K Bitcoin addresses, it was shown that our voting based method outperforms the nonvoting based one in terms of precision, recal, and F1 score. We also found that DNM's addresses pay higher fees than others, which significantly improves the classification.

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Underdetermined Blind Separation using Multi-Subspace Representation in Time-Frequency Domain

Wang L., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2019-May 2019年05月

ISSN  9781538680889

　概要を見る

© 2019 IEEE. Blind source separation (BSS) is a technique to recognize the multiple talkers from the multiple observations received by some sensors without any prior knowledge information. The problem is that the mixing is always complex, i.e., nonlinear, underdetermined mixture, such as the case where sources are mixed with some direction angles, or where the number of sensors is less than that of sources. In this paper, we propose a multi-subspace representation based BSS approach that allows the mixing process to be nonlinear and underdetermined. The approach relies on a multi-layer representation and sparse representation in time-frequency (TF) domain. By parameterizing such subspaces, we can map the observed signals in the feature space with the coefficient matrix from the parameter space. We then exploit the linear mixture in the feature space that corresponds to the nonlinear mixture in the input space. Once such subspaces are built, the coefficient matrix can be constructed by solving an l1-regularization on the coding coefficient vector. Relying on the TF representation, the target matrix can be constructed in a sparse mixture TF vectors with a fewer computational cost. The experiments are run on the observations that are generated from nonlinear functions, and that are collected with some direction angles in a virtual room environment. The proposed approach exhibits a higher separation accuracy than that of the conventional algorithms.

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Spectrogram-Based Simultaneous Heartbeat and Blink Detection using Doppler Sensor

Yamamoto K., Toyoda K., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2019-May 2019年05月

ISSN  9781538680889

　概要を見る

© 2019 IEEE. Heartbeats and a blink are two major physiological signals that provide crucial information on our stress and drowsiness. Hence, it is highly demanded to detect simultaneously heartbeats and blinks in many applications. A Doppler sensor could be a key device to facilitate the non-contact heartbeat and blink detection in daily life. Although many Doppler sensor-based heartbeat and blink detection methods have been independently proposed, when heartbeats and blinks are detected simultaneously with one Doppler sensor, the detection accuracies of such heartbeat and blink detection methods get degraded because of at least two issues: (i) the low SNR (Signal-to-Noise Ratio) of each signal reflected from a subject's chest and face, and (ii) the similarity of the spectrum distribution of heartbeats and a blink. In this paper, we propose a spectrogram-based simultaneous heartbeat and blink detection using one Doppler sensor. In the proposed method, to extract the spectra that might be due to heartbeats and a blink, the spectra on a spectrogram are integrated. Blink detection is then performed by classifying the peaks of the integrated spectrum into a peak due to a blink or a non-blink based on a supervised machine learning classifier trained with a set of the time domain and the time-frequency domain features. Based on the non-blink peaks, heartbeats are detected considering the RRI (R-R Interval) estimated before the investigated peak to prevent the incorrect heartbeat detection. Through the experiments in the case where microwaves are transmitted from one Doppler sensor toward a body including a chest and a face, our proposal has been shown to be able to simultaneously detect heartbeats and blinks with high accuracy.

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Secrecy Performance Analysis of Wireless Sensor Networks

Ai Y., Cheffena M., Ohtsuki T., Zhuang H.

IEEE Sensors Letters （IEEE Sensors Letters）  3 （ 5 ）  2019年05月

　概要を見る

© 2017 IEEE. The wireless sensor network (WSN) plays a fundamentally important role in the realization of Internet of Things and Industry 4.0. In this article, we study the physical layer security performance of a large-scale ad hoc and homogeneous WSN. To accurately evaluate the secrecy performance of the large-scale WSN, we take into account the randomness of the interfering nodes (both in terms of the number of nodes and their respective position) based on Poisson point process in our analysis. We derive novel expressions for the average secrecy capacity and secrecy outage probability, including the effects of fading, path loss, and the network's spatial randomness. The results demonstrate the impacts of several factors such as node intensity and transmission power in large-scale WSN on secrecy performance, which should be seriously taken into consideration while designing WSNs.

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Precoding Aided Generalized Spatial Modulation with K Transmit Antenna Groups

Cao Y., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2019-May 2019年05月

ISSN  9781538680889

　概要を見る

© 2019 IEEE. In this paper, we divide antennas at the transmitter into K transmit antenna groups (TAGs) and extend the generalized spatial modulation (GSM) idea to a new precoding aided multiple-input multiple-output (MIMO) system, which is referred to as precoding aided GSM (PGSM) system. To select the receive antenna subset with low complexity, a class of efficient receive antenna selection techniques with low computational complexity are devised. To reduce the complexity of maximum likelihood (ML) detector, a low complexity iterative greedy (IG) algorithm relied ML (IG-ML) detector is also derived. Simulation results show that the bit error rate (BER) performance gain up to 3.5 dB is obtained by the proposed PGSM with IG-ML detector over the PGSM with ordered block minimum mean-squared error (OBMMSE) detector at a given BER of 10-4. It is also shown that the proposed PGSM with IG-ML detector achieves a similar BER performance in high signal-to-noise power ratio (SNR) region compared to that of an ideal precoding for the GSM with optimal ML detector.

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Polynomial Networks Representation of Nonlinear Mixtures with Application in Underdetermined Blind Source Separation

Wang L., Ohtsuki T.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings （ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings）  2019-May   3687 - 3691 2019年05月

ISSN  9781479981311

　概要を見る

© 2019 IEEE. Similar to the deep architectures, a novel multi-layer architecture is used to extend the linear blind source separation (BSS) method to the nonlinear case in this paper. The approach approximates the nonlinearities based on a polynomial network, where the layer of our network begins with the polynomial of degree 1, up to build an output layer that can represent data with a small bias by a good approximate basis. Relying on several transformations of the input data, with higher-level representation from lower-level ones, the networks are to fulfill a mapping implicitly to the high-dimensional space. Once the polynomial networks are built, the coefficient matrix can be estimated by solving an l1-regularization on the coding coefficient vector. The experiment shows that the proposed approach exhibits a higher separation accuracy than the comparison algorithms.

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Non-Negative Matrix Factorization-Based Blind Source Separation for Non-Contact Heartbeat Detection

Ye C., Toyoda K., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2019-May 2019年05月

ISSN  9781538680889

　概要を見る

© 2019 IEEE. Recently, through exploiting the spectral sparsity of heartbeat component, a heartbeat detection method using a stochastic gradient approach has enabled a high-resolution of heartbeat spectrum reconstruction by Doppler radar signal, which also suppresses the residual noises after signal decomposition. However, the interference from respiration and/or body motion often corrupts the decomposition of signal by singular spectrum analysis (SSA), resulting in an inaccurate extraction of heartbeat component. In this paper, a non-negative matrix factorization (NMF)-based blind source separation (BSS) is first applied to non-contact heartbeat detection for better heartbeat extraction, incorporating the stochastic gradient approach. Specifically, motion noise is taken into account as one of sources, achieving relatively stable separation in various scenarios. In our proposed BSS approach, the spectrogram originated from radar signal is decomposed twice by NMF, which is used to learn the basis spectra (BS) relying on spectral correlation. Experimental results showed the improved accuracy and robustness of our method over conventional methods, on the heart rate (HR) measurement against subjects' sitting still or typewriting.

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Optimal Sleep Wakeup Mechanism for Green Internet of Things

Panahi F.H., Panahi F.H., Heshmati S., Ohtsuki T.

ICEE 2019 - 27th Iranian Conference on Electrical Engineering （ICEE 2019 - 27th Iranian Conference on Electrical Engineering）     1659 - 1663 2019年04月

ISSN  9781728115085

　概要を見る

© 2019 IEEE. The presence of many battery-powered sensors in the Internet of Things (IoT) paradigm calls for the design of green and energy-aware protocols. One of the major challenges in the wireless networks based on IoT is to reduce energy consumption and increase network lifetime. This paper addresses the problem of designing a smart sleep/wake-up mechanism using the genetic algorithm (GA). GA, as a nature-inspired optimization approach, is used to manage the operation modes of the base stations (BSs) according to the time-variant profile of the daily traffic load. Accordingly, when a number of stations are sending data to nodes, i.e., active or awake nodes, other nodes are in inactive (sleep) mode. In this case, the energy efficiency (EE) of the network increases to an acceptable level via controlling the operation modes of the densely deployed BSs over wireless sensor networks (WSNs).

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A Convergence and Asymptotic Analysis of Nonlinear Separation Model

Wang L., Ohtsuki T.

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings （2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2018 - Proceedings）     963 - 971 2019年03月

ISSN  9789881476852

　概要を見る

© 2018 APSIPA organization. Nonlinear blind source separation is the process of estimating either the original signals or mixture functions from the degraded signals, without any prior information about original sources. The key idea is to recover the sources by estimating an approximation function so as to approximate the inverse of mixing function. However, in practice, the approximation function is derived from some estimation algorithm with finite sample size, which leads to the performance loss. In this paper, we work on the convergence and asymptotic analysis of the separation approach, which uses the flexible approximation to extract the nonlinearity of mixture function so that to make the problem linearly separable. The analysis stems from the performance of a mismatched estimator that accesses the finite sample size. By providing a closed-form expression of normalized mean squared error (NMSE), we can present a novel algebraic formalization that leads to the upper bound on the estimation error. The simulation results show that if the flexible approximation can extract the nonlinearity of mixing functions, the minimized NMSE can be achieved as the sample size tends to be infinity. This implies that the algorithm is feasible to separate the distortion of the nonlinear mixture.

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Time series analysis for bitcoin transactions: The case of Pirate@40's HYIP scheme

Toyoda K., Ohtsuki T., Mathiopoulos P.

IEEE International Conference on Data Mining Workshops, ICDMW （IEEE International Conference on Data Mining Workshops, ICDMW）  2018-November   151 - 155 2019年02月

ISSN  9781538692882

　概要を見る

© 2018 IEEE. Due to the increased popularity of Bitcoin, many researchers have analyzed how Bitcoin is being used based on the transaction history. However, the existing works analyze the transaction history in a 'static' manner and none of them analyzes transaction history 'dynamically', i.e. without taking into account the 'time variation of how Bitcoin is transferred'. The time analysis is in great demand for many practical cases, such as digital forensics tool that infers what was going on behind the scene of a fraudulent scam, and real-time inference of marketplace sales. In this paper, we propose a novel time series analysis for analyzing the history of Bitcoin transactions. In fact the main goal of our research is to detect changing points, namely anomaly detection, against a given (Bitcoin) address's transaction history. To show the effectiveness of the proposed approach, it is tested against the transaction history of Pirate@40's HYIP (High Yielding Investment Program) scheme, which raised 700,000 BTC from his investors and was charged by the Security and Exchange Commission (SEC) in 2013. It is shown that the proposed approach can successfully detect several remarkable points of Pirate@40's HYIP scheme, such as when its program's name was changed to Bitcoin Saving & Trust and when its investment rule was changed.

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A Novel Methodology for HYIP Operators' Bitcoin Addresses Identification

Toyoda K., Takis Mathiopoulos P., Ohtsuki T.

IEEE Access （IEEE Access）  7   74835 - 74848 2019年

　概要を見る

© 2013 IEEE. Bitcoin is one of the most popular decentralized cryptocurrencies to date. However, it has been widely reported that it can be used for investment scams, which are referred to as high yield investment programs (HYIP). Although from the security forensic point of view it is very important to identify the HYIP operators' Bitcoin addresses, so far in the open technical literature no systematic method which reliably collects and identifies such Bitcoin addresses has been proposed. In this paper, a novel methodology is introduced, which efficiently collects a large number of the HYIP operators' Bitcoin addresses and identifies them based upon a novel analysis of their transactions history. In particular, a scraping-based method is first proposed which is able to collect more than 2,000 HYIP operators' Bitcoin addresses from the Internet thus providing a large number of the HYIPs' samples. Second, a supervised machine learning technique, which classifies, whether or not, specific Bitcoin addresses belong to the HYIP operators, is introduced and its performance is evaluated. The proposed classification method is based upon two novel approaches, namely the rate conversion technique that mitigates the effect of Bitcoin price volatility and the sampling technique that reduces the computational amount without sacrificing the classification performance. By employing close to 30,000 real Bitcoin addresses, extensive performance evaluation results obtained by means of computer simulation experiments have shown that the proposed methodology achieves excellent performance, i.e., 95% of the HYIP addresses can be correctly classified, while maintaining a false positive rate less than 4.9%. In order to further validate the proposed classifier's ability to detect the HYIP operators' Bitcoin addresses, our designed classifier has been tested against a recently published list of the HYIP addresses maintaining its excellent detection accuracy by achieving a 93.75% success rate.

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Underdetermined Blind Source Separation with Multi-Subspace for Nonlinear Representation

Wang L., Ohtsuki T.

IEEE Access （IEEE Access）  7   84545 - 84557 2019年

　概要を見る

© 2013 IEEE. Blind source separation (BSS) is a technique to recognize the multiple talkers from the multiple observations received by some sensors without any prior knowledge information. The problem is that the mixing is always complex, such as the case where sources are mixed with some direction angles, or where the number of sensors is less than that of sources. In this paper, we propose a multi-subspace representation based BSS approach that allows the mixing process to be nonlinear and underdetermined. The approach relies on a multi-subspace structure and sparse representation in the time-frequency (TF) domain. By parameterizing such subspaces, we can map the observed signals in the feature space with the coefficient matrix from the parameter space. We then exploit the linear mixture in the feature space that corresponds to the nonlinear mixture in the input space. Once such subspaces are built, the coefficient matrix can be constructed by solving an optimization problem on the coding coefficient vector. Relying on the TF representation, the target matrix can be constructed in a sparse mixture of TF vectors with the fewer computational cost. The experiments are designed on the observations that are generated from an underdetermined mixture, and that is collected with some direction angles in a virtual room environment. The proposed approach exhibits higher separation accuracy.

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Multiple Radios for Fast Rendezvous in Heterogeneous Cognitive Radio Networks

Li A., Han G., Ohtsuki T.

IEEE Access （IEEE Access）  7   37342 - 37359 2019年

　概要を見る

© 2013 IEEE. In cognitive radio networks (CRNs), if two unlicensed secondary users (SUs) want to communicate with each other, they need to rendezvous with each other on the same channel at the same time. Rendezvous is the first key step for SUs to be able to communicate with each other. Channel hopping (CH) is a representative technique to solve the rendezvous problem in CRNs. SUs equipped with multiple radios can significantly reduce the time-to-rendezvous (TTR) for several existing CH algorithms while the additional cost is low. However, several drawbacks exist in the existing CH algorithms based on multiple radios. One of the main drawbacks is that they cannot be well applied in heterogeneous CRNs. The reason is that the number of radios for different SUs is implicitly assumed same or must be more than one in the existing CH algorithms based on multiple radios, which is unrealistic for heterogeneous CRNs. In heterogeneous CRNs, SUs may be equipped with different numbers of radios including one radio. To mainly address the above issue, hybrid radios rendezvous (HRR) algorithm is proposed in this paper. Moreover, the upper bounds of maximum TTR (MTTR) for the HRR algorithm are derived by a theoretical analysis. Furthermore, extensive simulations are performed to evaluate the expected TTR (ETTR), the MTTR, and the channel qualities of the rendezvous channels for the HRR algorithm. Simulation results show that rendezvous can be guaranteed by the HRR algorithm in heterogenous CRNs. Besides, the qualities of the rendezvous channels can be improved by the HRR algorithm. In addition, our algorithms can achieve rendezvous faster than several existing algorithms.

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Estimation of stress condition of children by nasal skin temperature

Suzuki M., Ohtsuki T.

Advances in Intelligent Systems and Computing （Advances in Intelligent Systems and Computing）  857   1351 - 1358 2019年

ISSN  9783030011765

　概要を見る

© Springer Nature Switzerland AG 2019. In Japan a lot of children stay home alone after school. Safety of children is a big issue. In addition, a stability of their emotion is a big issue. For the stability of their emotion, it is important to know the psychological state of children, particularly stress so that we can try to relieve their stress. As for the detection of the stress, it is preferred to detect in a less constrained or non-contact way. The nasal skin temperature is known to be affected by emotion, which can be detected in a non-contact way with an infrared thermography camera. In this paper, we report our experimental results about the detection of stress condition of children by the nasal skin temperature with the infrared thermography camera. In estimating the stress condition, we use a heart rate variability as a physiological index. With our experimental results, we show that the stress condition of children can be estimated by the nasal skin temperature. We also show that when the stress index rises, the change of the nasal skin temperature differs depending on their ages.

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Doppler Sensor-Based Blink Duration Estimation by Analysis of Eyelids Closing and Opening Behavior on Spectrogram

Yamamoto K., Toyoda K., Ohtsuki T.

IEEE Access （IEEE Access）  7   42726 - 42734 2019年

　概要を見る

© 2013 IEEE. Blink duration is one of the useful indicators to estimate drowsiness and fatigue. A Doppler sensor could be a key device to realize the non-contact blink duration estimation, which is very useful for the drowsiness and fatigue monitoring in real life. However, none of the blink duration estimation methods has been proposed so far. In this paper, we propose a novel Doppler sensor-based blink duration estimation method based on the analysis of eyelids closing and opening behavior on a spectrogram. When one blinks, the energies caused by eyelids closing and opening behavior appears on a spectrogram. Hence, the blink duration can be estimated by integrating such energies and then detecting the timings when the integrated energy caused by the eyelids closing behavior appears and when the energy caused by the eyelids opening behavior disappears. To precisely detect such timings, a spectrogram with a short time window is better. However, thorough the preliminary experiments, we confirmed that when a shorter time window is used, more noise appears around such timings, which makes it difficult to detect such timings. To deal with this issue, the proposed method multiplies several integrated energies for spectrograms with different time windows. By multiplying the integrated energies with such noise and the ones without such noise, the energy level of such noise gets significantly declined over the product. Furthermore, the levels of the redundant blink energies also decline to the noise level, where the redundant blink energies denote the ones that appear before and after the eyelids closing and opening timings because of a long time window. The results of the experiments on 8 subjects watching a movie showed that our method achieved an average root mean squared error (RMSE) of 49.3 ms.

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Precoding aided generalized spatial modulation with an iterative greedy algorithm

Yuwen Cao, Tomoaki Ohtsuki

IEEE Access （IEEE Access）  6   72449 - 72457 2018年11月

　概要を見る

© 2018 IEEE. This paper divides antennas at the transmitter into several transmit antenna groups (TAGs) and extends the generalized spatial modulation (GSM) idea to a new precoding-aided multiple-input multiple-output system, which is referred to as precoding-aided GSM (PGSM) system. To mitigate the influence caused by correlated channels, we consider the interleaved grouping scheme which leads to a minimum antenna correlation within the same TAG. To select the receive antenna subset with low complexity, we propose an iterative greedy (IG) algorithm that contributes to a maximum equivalent channel gain with low-computational complexity. To reduce the complexity of maximum likelihood (ML) detector, a low complexity IG algorithm-based ML (IG-ML) detector is also derived. Simulation results show that the bit error rate (BER) performance gain up to 3.5 dB is obtained by the proposed PGSM with IG-ML detector over the PGSM with an ordered block minimum mean-squared error detector at a given BER of 10-4. It is also shown that the proposed PGSM with the IG-ML detector achieves a similar BER performance in a high signal-to-noise power ratio region compared with that of an ideal precoding for the GSM with an optimal ML detector.

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Smart Image-Processing based Energy Harvesting for Green Internet of Things

Panahi F.H., Hajimirzaee P., Erfanpoor S., Panahi F.H., Ohtsuki T.

Proceedings - 2018 Smart Grid Conference, SGC 2018 （Proceedings - 2018 Smart Grid Conference, SGC 2018）  2018年11月

ISSN  9781728111384

　概要を見る

© 2018 IEEE. Internet of Things (IoT), as a widespread growing technology which connects various heterogeneous devices of wireless sensor networks (WSNs), plays a great role in sensing, monitoring, controlling of the covering environment. However, maximizing the lifetime of WSNs is still a major challenge. Although some approaches have been introduced to overcome the vital problem so far, research on this problem experiences a slow progress. Inspired by the promising performance of fuzzy-based Q-learning (FQL) algorithm to design practical smart sensors, this paper proposes a FQL-based approach that can maximize the lifetime of sensors and accelerate the process of wireless energy harvesting (EH) for mobile sensors which coexist with macro and small base stations (SBSs) deployed over a time-variant heterogeneous network (HetNet). The methodology is based on a centralized image-processing (IP) approach to scan and find the instant coverage map of the HetNet and then to localize red regions, i.e., regions with high levels of energy. Furthermore, mobile sensors attempt to access these regions during frequent movements. This will help to maximize the lifetime of the WSN. Simulation results confirm the effectiveness of the wireless EH process and smart aggregation of mobile sensors around the dense-coverage areas.

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Smart Energy Harvesting for Internet of Things

Panahi F.H., Moshirvaziri S., Mihemmedi Y., Panahi F.H., Ohtsuki T.

Proceedings - 2018 Smart Grid Conference, SGC 2018 （Proceedings - 2018 Smart Grid Conference, SGC 2018）  2018年11月

ISSN  9781728111384

　概要を見る

© 2018 IEEE. In this paper, we study the performance of smart networks in terms of energy efficiency (EE) based on the internet of things (IoT) technology. Energy Harvesting (EH) from surroundings is a promising solution for compensating the limitation of batteries lifetime. Fuzzy Q-Learning algorithm has been proposed to determine the sensor movement strategy toward the dedicated power stations (PSs) as energy charger nodes for mobile sensors in order to improve network lifetime via acceleration of the battery charging process of mobile sensors. Simulation results for evaluation of the proposed strategy in term of normalized network lifetime compared to the random sensor motions, confirm the effectiveness of this approach as a practical smart EH for IoT networks.

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D2D-aided Optimal Utilization of Renewable Energies for Green Powered Base Stations

Panahi F.H., Moshirvaziri S., Momene A., Panahi F.H., Ohtsuki T.

Proceedings - 2018 Smart Grid Conference, SGC 2018 （Proceedings - 2018 Smart Grid Conference, SGC 2018）  2018年11月

ISSN  9781728111384

　概要を見る

© 2018 IEEE. In this paper, due to the unlimited growth of service demands and high load traffic, a green wireless communication system has been considered with the aim of decreasing energy consumption of heterogeneous networks (HetNets). This system provides a proportion of required energy of Base Stations (BSs) by employing some renewable power suppliers (RPSs), while user equipments (UEs) especially benefit from short device-to-device (D2D) links as a promising technique to design energy-efficient HetNets. Indeed, we attempt to focus on efficient interaction between the RPS's random green power generations and the BS's dynamic of energy consumption via minimization of an energy-based cost function, considering the D2D impacts on efficient utilization of energy and bandwidth. Simulation results validate that the proposed framework provides significant improvements in electrical grid power-consumption and total energy efficiency (EE) of the network.

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Improved Sparse Adaptive Algorithms for Accurate Non-contact Heartbeat Detection Using Time-Window-Variation Technique

Ye C., Toyoda K., Ohtsuki T.

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS （Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS）  2018-July   1 - 6 2018年10月

ISSN  9781538636466

　概要を見る

© 2018 IEEE. Recently, a sparse adaptive algorithm termed zero-attracting sign least-mean-square (ZA-SLMS), has been clarified to be able to reconstruct robustly heartbeat spectrum by Doppler radar signal. However, since the strengths of noise evidently differ under different body motions, the sparse heartbeat spectra cannot be always acquired accurately by the constant regularization parameter (REPA) that balances the gradient correction and the sparse penalty, applying in the ZA-SLMS algorithm. In this paper, an improved ZA-SLMS algorithm is proposed by introducing adaptive REPA (AREPA), where the proportion of sparse penalty is adjusted based on the standard deviation of radar data. Moreover, to enhance the stability of heartbeat detection, a time-window-variation (TWV) technique is further introduced in the improved ZA-SLMS algorithm, considering the fact that the position of spectral peak associated with the heart rate (HR) is stable when the length of time window changes within a short period. Experimental results measured against five subjects validated that our proposal reliably improves the error of HR estimation than the standard ZA-SLMS algorithm.

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A Stochastic Gradient Approach for Robust Heartbeat Detection With Doppler Radar Using Time-Window-Variation Technique

Ye C., Toyoda K., Ohtsuki T.

IEEE Transactions on Biomedical Engineering （IEEE Transactions on Biomedical Engineering）  66 （ 6 ） 1730 - 1741 2018年10月

ISSN  00189294

　概要を見る

© 2019 IEEE. Heart rate (HR) variability indicates health condition and mental stress. The development of non-contact HR monitoring techniques with Doppler radar is attracting great attention. However, the performance of heartbeat detection via radar signals easily degrades due to respiration and body motion. In this paper, first, a stochastic gradient approach is applied to reconstruct the high-resolution spectrum of heartbeat by proposing the zero-attracting sign least-mean-square (ZA-SLMS) algorithm. To correct the quantized gradient of cost function and penalize the sparse constraint on updating the spectrum, a more accurate heartbeat spectrum is reconstructed. Then, to better adapt to the noises of different strengths caused by subjects' movements, an adaptive regularization parameter is introduced in the ZA-SLMS algorithm as an improved variant, which can adaptively regulate the proportion between gradient correction and sparse penalty. Moreover, in view of the stability of the location of the spectral peak associated with the HR when the size of time window slightly changes, a time-window-variation (TWV) technique is further incorporated in the improved ZA-SLMS (IZA-SLMS) algorithm for more stable HR estimation. Through the experiments on five subjects, our proposal is demonstrated to bring a significant improvement in accuracy compared with existing detection methods. Specifically, the IZA-SLMS algorithm with TWV achieves the smallest average error of 3.79 beats per minute when subjects type on a laptop.

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Multi-Class Sentiment Analysis in Twitter: What if Classification is Not the Answer

Bouazizi M., Ohtsuki T.

IEEE Access （IEEE Access）  6   64486 - 64502 2018年10月

　概要を見る

© 2018 IEEE. With the rapid growth of online social media content, and the impact these have made on people's behavior, many researchers have been interested in studying these media platforms. A major part of their work focused on sentiment analysis and opinion mining. These refer to the automatic identification of opinions of people toward specific topics by analyzing their posts and publications. Multi-class sentiment analysis, in particular, addresses the identification of the exact sentiment conveyed by the user rather than the overall sentiment polarity of his text message or post. That being the case, we introduce a task different from the conventional multi-class classification, which we run on a data set collected from Twitter. We refer to this task as 'quantification.' By the term 'quantification,' we mean the identification of all the existing sentiments within an online post (i.e., tweet) instead of attributing a single sentiment label to it. For this sake, we propose an approach that automatically attributes different scores to each sentiment in a tweet, and selects the sentiments with the highest scores which we judge as conveyed in the text. To reach this target, we added to our previously introduced tool SENTA the necessary components to run and perform such a task. Throughout this work, we present the added components; we study the feasibility of quantification, and propose an approach to perform it on a data set made of tweets for 11 different sentiment classes. The data set was manually labeled and the results of the automatic analysis were checked against the human annotation. Our experiments show the feasibility of this task and reach an F1 score equal to 45.9%.

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Spectrogram-Based Non-Contact RRI Estimation by Accurate Peak Detection Algorithm

Kohei Yamamoto, Kentaro Toyoda, Tomoaki Ohtsuki

IEEE Access （IEEE Access）  6 （ 1 ） 60369 - 60379 2018年10月

　概要を見る

© 2013 IEEE. Demands for vital sign monitoring are increasing in the field of health care. In particular, the R-R Interval (RRI) estimation has been studied extensively, since the RRI variation is highly related with the stress of a subject. Various Doppler sensor-based-heartbeat detection methods have been proposed so far, thanks to non-contact and non-invasive features of a Doppler sensor. In our previous research, we have proposed a Doppler sensor-based RRI estimation method by a spectrogram. In this method, the spectra due to heartbeats are integrated on a spectrogram, and then the RRI is estimated by detecting the peaks of the integrated spectrum. However, the undesired peaks sometimes appear even in the situation where a subject sits still. In this paper, as the extended version of our previous method, we propose a Doppler sensor-based RRI estimation method leveraging the accurate peak detection. In the proposed method, to prevent the incorrect peak detection, the peaks due to heartbeats are detected using some peaks before and after the investigated peak. Through the experiments on 10 subjects in the cases where a subject was sitting still, typing, and speaking, we confirmed that the proposed method improved our previous and state-of-the-art ones by the root-mean-squared error of the RRI. Furthermore, based on the estimated RRI, we calculated the stress index low-frequency/high-frequency (LF/HF), which is one of the useful indices to evaluate the stress of a subject. As a result, our proposed method outperformed the other ones by the relative error of the LF/HF.

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On Physical Layer Security of α-η-κ-μ Fading Channels

Mathur A., Ai Y., Bhatnagar M., Cheffena M., Ohtsuki T.

IEEE Communications Letters （IEEE Communications Letters）  22 （ 10 ） 2168 - 2171 2018年10月

ISSN  10897798

　概要を見る

© 1997-2012 IEEE. In this letter, we study the secrecy performance of the classic Wyner's wiretap model, where the main and eavesdropper channels are modeled by a general and versatile α-η-κ-μ fading model. Novel and exact expressions of the average secrecy capacity and secrecy outage probability have been derived. Previous results on physical layer security can be obtained through our newly derived expressions by specializing the model parameters. More importantly, the derived results are also applicable for the secrecy performance analysis of some field measurements (e.g., in millimeter wave communications), which cannot be analyzed by previous results.

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Recovery of block-structured sparse signal using block-sparse adaptive algorithms via dynamic grouping

Ye C., Gui G., Xu L., Ohtsuki T.

IEEE Access （IEEE Access）  6   56069 - 56083 2018年09月

　概要を見る

© 2013 IEEE. A key point for the recovery of a block-sparse signal is how to treat the different sparsity distributed on the different parts of the considered signal. It has been shown recently that grouping the signal, i.e., partitioning the original signal into different groups or segments, and conducting the recovery for these groups separately provides an effective method to deal with the block-structured sparsity and can generate much better performance than the conventional sparse signal recovery (SSR) algorithms. In order to further improve the recovery performance, instead of the fixed grouping method used in the recent results, a novel dynamic grouping method will be first proposed in this paper, which classifies the segments due to the different levels of sparsity in a dynamic way. Then, by incorporating this technique into the block version of adaptive SSR algorithms. we developed recently, i.e., the block zero-attracting least-mean-square (BZA-LMS) algorithm and the block ℓ0-norm LMS (B ℓ0-LMS) algorithm, the corresponding new algorithms, i.e., the BZA-LMS-D and B ℓ0-LMS-D algorithms, will be established. The performance superiorities and the robustness against different block-sparsity and/or noise interference for the new algorithms based on dynamic grouping will be demonstrated by both analytic discussions and numerical simulations for a variety of scenarios.

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Doppler Analysis Based Fall Detection Using Array Antenna

Agata Y., Ohtsuki T., Toyoda K.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2018-May 2018年07月

ISSN  9781538631805

　概要を見る

© 2018 IEEE. The number of elderly people who live alone is increasing in many countries. Furthermore, many of their accidents occur at home. Hence, it is an urgent demand for a system monitoring their activities to detect accidents indoor such as falling. In conventional systems of fall detection using array antennas, falling after standing still can be detected with high accuracy by leveraging the features indicating the change of radio wave propagation. However, it is difficult to detect falling after walking correctly. In this paper, to improve fall detection accuracy including falling after walking, we propose an accurate fall detection system by leveraging the features indicating the change of Doppler signals during human activities in detail. Analyzing Doppler signals is useful to detect falling since they are observed when a radio wave reflects by moving objects. We conducted experiments in actual rooms to demonstrate that the proposed method can detect falling after both standing and walking with high accuracy.

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Robust Heartbeat Detection with Doppler Radar Based on Stochastic Gradient Approach

Ye C., Toyoda K., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2018-May 2018年07月

ISSN  9781538631805

　概要を見る

© 2018 IEEE. Heart-rate variability (HRV) is closely related with physical or mental conditions. It is rather necessary to develop remote monitoring technologies to reduce subjects' pressure, e.g., heart-rate (HR) monitoring to drivers. Recently, the contactless heartbeat detection with Doppler radar has drawn extensive attention, due to less burden on subjects. However, the received signals of Doppler radar are easily contaminated by respiration or body motion, resulting in performance degradation. In this paper, to realize robust heartbeat detection with Doppler radar, a stochastic gradient approach is first proposed to reconstruct heartbeat spectrum. By correcting the gradient of cost function constructed by recursion error, and utilizing the sparse characteristics of heartbeat spectrum, the reconstructed spectrum can be obtained with minimum deviation. Furthermore, the zero-attracting sign least mean square (ZA- SLMS) algorithm based on stochastic gradient descent (SGD) is proposed, to accomplish more stable sparse spectrum reconstruction (SSR), by quantizing the updating of recursion error. Finally, HR is estimated by spectral peak tracking consisting of peak selection and verification. Experimental results validate that the proposed method can significantly improve detection accuracy over the conventional methods, based on the measurements from 5 subjects during sitting still or typing with a laptop.

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Doppler Sensor-Based Blink Duration Estimation by Spectrogram Analysis

Yamamoto K., Toyoda K., Ohtsuki T.

IEEE International Conference on Communications （IEEE International Conference on Communications）  2018-May 2018年07月

ISSN  9781538631805

　概要を見る

© 2018 IEEE. It is known that the blink duration is highly related to drowsiness, where the blink duration is the entire duration of one blink. Hence, it is important to measure the blink duration without any special wearable devices in various application, e.g., driver's monitoring. Although a Doppler sensor could be a key device to realize it, no such blink duration estimation method has been realized so far, since estimating the blink duration is difficult because of the low SNR (Signal to Noise Ratio) of the signal reflected from eyelids. In this paper, we propose a Doppler sensor-based method that estimates the duration, tblink, that is proportional to the actual blink duration. In the proposed method, a spectrogram is firstly calculated from a received signal, and then the timings when eyelids close and open are extracted from that. More specifically, tblink is calculated based on the center-of-gravity of the energy on a spectrogram. We conducted the experiments on five subjects to show the estimation accuracy of our proposed method. We confirmed that our method achieved the average correlation coefficient R of 0.95, furthermore, the average RMSE (Root Mean Square Error) of 46 ms.

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Graph Coloring-Based Pilot Reuse with AOA and Distance in D2D Underlay Massive MIMO

Echigo H., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2018-June   1 - 6 2018年07月

ISSN  9781538663554

　概要を見る

© 2018 IEEE. Device-to-device (D2D) underlay massive multipleinput multiple-output (MIMO) is a notable technology to improve spectrum efficiency by using the same frequency among both cellular user equipments (CUEs) and D2D user equipments (DUEs). In terms of communication resources, it is required to avoid assigning orthogonal pilot sequences to all CUEs and DUEs. Although sharing the same pilot sequences among user equipments (UEs) can save the communication resources, interference arises among the same pilot sequences, which is known as pilot contamination. Because the impact of this interference is still large in the conventional pilot reuse, it is highly demanded to mitigate the effect of pilot contamination. In this paper, we propose a novel pilot reuse scheme based on a graph coloring technique to mitigate the effect of pilot contamination. By leveraging the fact that the interference is alleviated when the number of antennas is large and the angle of arrivals (AOAs) of the same pilot sequences does not overlap, our method assigns the same pilot sequences so that they are not overlapped. Furthermore, to improve the channel estimation accuracy between D2D pairs, our scheme restricts reusable pilot sequences by constructing an interference graph based on graph coloring. In this graph, the nodes near to each other are connected and assigned orthogonal pilot sequences. Simulation results show that the proposed scheme outperforms the conventional one in terms of channel estimation accuracy for UEs and spectral efficiency of the system.

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QoE-aware video streaming transmission optimization method for playout threshold adjustment in LTE

Qi Y., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2018-June   1 - 5 2018年07月

ISSN  9781538663554

　概要を見る

© 2018 IEEE. Rate control technology could adaptively adjust the bit rate of video according to the current throughput, which could better solve the video playout interruption. The playout threshold of video playout buffer influences the overflow and underflow probabilities of the video playout and result in video playout interruption or video frame skip. In this paper we propose an optimization method that considering Quality of Experience (QoE), joint bit rate and playout threshold control under Long Term Evolution (LTE) network. An analytical framework is presented to investigate the impact of the limited playout buffer dynamics on the wireless video QoE by analyzing the transient queue length of the buffer with the diffusion approximation method. The simulation results show that the proposed method could lower the occurrence of the video playout interruption and video frame skip under the constraints of limited resources so as to improve the QoE of video.

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Nonlinear Blind Source Separation Unifying Vanishing Component Analysis and Temporal Structure

Wang L., Ohtsuki T.

IEEE Access （IEEE Access）  6   42837 - 42850 2018年07月

　概要を見る

© 2013 IEEE. Nonlinear blind source separation (BSS) is one of the unsolved problems in unsupervised learning, because the solutions are highly non-unique when there is no prior information for the mixing functions. In this paper, we present a novel approach to tackle the ill-posedness of the nonlinear BSS problem with a few assumptions. The derivation of our algorithm is inspired by the idea of an efficient layer-by-layer representation to approximate the nonlinearity. Once such a representation is built, a final output layer is constructed by solving a convex optimization problem. Relying on the multi-layer architecture, the algorithm transforms a time-invariant nonlinear BSS to the local linear problem with a tolerable computational cost. Then, the projected data can break the nonlinear problem down into the version of a generalized joint diagonalization problem in the feature space. Importantly, the parameters and forms of polynomials depend solely on the input data, which guarantee the robustness of the structure. We thus address the general problem without being restricted to any specific mixture or parametric model. Experiments show that the proposed algorithm has a higher estimation accuracy on audio data sets from the real world for separating various nonlinear mixtures.

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Multi-Class Bitcoin-Enabled Service Identification Based on Transaction History Summarization

Toyoda K., Ohtsuki T., Mathiopoulos P.T.

Proceedings - IEEE 2018 International Congress on Cybermatics: 2018 IEEE Conferences on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology, iThings/GreenCom/CPSCom/SmartData/Blockchain/CIT 2018 （Proceedings - IEEE 2018 International Congress on Cybermatics: 2018 IEEE Conferences on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology, iThings/GreenCom/CPSCom/SmartData/Blockchain/CIT 2018）     1153 - 1160 2018年07月

ISSN  9781538679753

　概要を見る

© 2018 IEEE. In recent years, Bitcoin has been used for many services and purposes, e.g. gambling, marketplace, but also even as an investment scam. In order to clarify how Bitcoin is used, it is in great importance to identify what kind of services are operated by Bitcoin addresses. In this paper, we propose a multiclass service identification scheme in Bitcoin based on novel transaction history summarization. Our novelty is to propose how transaction history is retrieved and how the retrieved transactions are processed for better identification. When a Bitcoin address is given, the characteristics of its transaction history is calculated as features. Then, the set of calculated features is fed into a supervised classifier and the services operated by the given Bitcoin addresses are identified among seven major services: (i) exchange, (ii) faucet, (iii) gambling, (iv) investment scam, (v) marketplace, (vi) mining pool, and (vii) mixer. To our knowledge, we are the first to propose a multi-class identification. We show that our scheme achieves 72 % of accuracy through performance evaluation with more than 26,000 Bitcoin addresses that have been used for seven services/purposes from Jan. 2009 to Feb. 2017.

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Green Heterogeneous Networks via an Intelligent Sleep/Wake-Up Mechanism and D2D Communications

Panahi F., Panahi F., Hattab G., Ohtsuki T., Cabric D.

IEEE Transactions on Green Communications and Networking （IEEE Transactions on Green Communications and Networking）  1 （ 4 ） 915 - 931 2018年06月

　概要を見る

© 2017 IEEE. The growing environmental awareness coupled with the rising costs of energy have sparked a keen interest in the deployment of energy-efficient communication technologies over the infrastructure of cellular networks. Base stations (BSs) are responsible for the largest portion of power consumption and energy usage in cellular networks. Thus, sleep/wake-up scheduling strategies for BSs can significantly improve the energy-efficiency (EE) of cellular networks. In this paper, we propose a Fuzzy Q-Learning based energy-efficient sleep/wake-up mechanism for BSs in a heterogeneous network. The goal is to save energy, without compromising the offered Quality of Service (QoS), by switching off the redundant BSs according to the local traffic profile and depending on the required area coverage and cell EE. The energy savings achieved under our proposed sleep scheduling strategy may lead to inevitable coverage holes as fewer BSs are active. To this end, we propose to enhance the sleep/wake-up mechanism with device-to-device communications to compensate for the coverage loss in areas where BSs are switched off. Simulation results validate that the proposed framework provides significant improvements in power consumption and the EE while satisfying the minimum coverage and QoS requirements.

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Transmit control for secure information sharing in untrusted relay with two antennas

Noguchi T., Takyu O., Sasamori F., Handa S., Fujii T., Ohtsuki T.

International Conference on Information Networking （International Conference on Information Networking）  2018-January   771 - 775 2018年04月

ISSN  9781538622896

　概要を見る

© 2018 IEEE. Relay network assisted wireless terminal has an advantage for easily constructing wireless network but a disadvantage for information leak to the wireless terminal. For secure wireless communication, an encryption but the sharing of secrete key between information source and destination are required. A secure information sharing with artificial noise and mirroring null steering for avoiding the information leak to untrusted relay has been proposed. In this paper, for enhancing the security of this proposed secure information sharing, we consider two transmit controls, transmit power control (TPC) and the adaptive node selection of information source. We evaluate the secure capacity by computer simulation.

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Consideration of security for PLNC with untrusted relay in game theoretic perspective

Shirasaki Y., Takyu O., Fujii T., Ohtsuki T., Sasamori F., Handa S.

IEEE Radio and Wireless Symposium, RWS （IEEE Radio and Wireless Symposium, RWS）  2018-January   109 - 112 2018年02月

ISSN  9781538607091

　概要を見る

© 2018 IEEE. A physical layer network coding (PLNC) is a highly efficient scheme for exchanging information between two nodes. Since the relay receives the interfered signal between two signals sent by two nodes, it hardly decodes any information from received signal. Therefore, the secure wireless communication link to the untrusted relay is constructed. The two nodes optimize the transmit power control for maximizing the secure capacity but these depend on the channel state information informed by the relay station. Therefore, the untrusted relay disguises the informed CSI for exploiting the information from two nodes. This paper constructs the game of two optimizations between the legitimate two nodes and the untrusted relay for clarifying the security of PLNC with untrusted relay.

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Secure information sharing with mirroring null steering through untrusted relay with two antennas

Noguchi T., Takyu O., Fujii T., Ohtsuki T., Sasamori F., Handa S.

IEEE Radio and Wireless Symposium, RWS （IEEE Radio and Wireless Symposium, RWS）  2018-January   203 - 205 2018年02月

ISSN  9781538607091

　概要を見る

© 2018 IEEE. This paper proposes the novel secure information sharing among wireless nodes by the physical layer security based on multi-antenna null steering. For secure wireless communication, the secure information like the common key of cypher should be shared among nodes before data transmission. However, in wireless adhoc network, the untrusted relay node may steal the data under relay process. In the proposed sharing, a source node and two sink nodes send the information conveying signal and the artificial noises to relay station, simultaneously for avoiding decoding the information in the untrusted relay. After amplifying and forwarding relay, the two nodes do not decode the information from the received signal without removing artificial noises. The untrusted relay constructs the null steering with two antennas for removing the artificial noise. Since the channel transfer functions through which the two artificial noises are passed are common, the null points to two sink nodes can be constructed. As a result, the two nodes can remove the artificial noises. This null steering is mirroring null steering. The computer simulation shows the effect of proposed information sharing in terms of secure capacity.

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Design of privacy-preserving mobile bitcoin client based on γ-deniability enabled bloom filter

Kanemura K., Toyoda K., Ohtsuki T.

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC （IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC）  2017-October   1 - 6 2018年02月

ISSN  9781538635315

　概要を見る

© 2017 IEEE. Bitcoin is a decentralized currency system that does not need any central authorities. All transactions issued by users have been recorded in the common ledger, called blockchain, which is shared by all users. In Bitcoin, an SPV (Simplified Payment Verification) client, which is a lightweight client that does not possess the entire blockchain, are developed for storage constrained devices such as a mobile phone. For an SPV client to check if there are transactions related to it, a Bloom filter where their Bitcoin addresses are involved is sent to a full client that possesses the entire blockchain. The full client only transfers transactions of which Bitcoin addresses are positive on the received Bloom filter. However, it is necessary to preserve the privacy of SPV clients when designing a Bloom filter because SPV clients' Bitcoin addresses will be identified by a full client with high probability. In this paper, we propose a privacy-preserving Bloom filter design for SPV clients based on γ-Deniability. γ-Deniability is a privacy metric that shows how much true positive Bitcoin addresses are hidden by the false positives in a Bloom filter. Furthermore, in order to design a Bloom Filter that satisfies a certain γ-Deniability, it is necessary to know the number of unique Bitcoin addresses that appear for the first time since the queried time. Based on our manual inspection, we propose to estimate it based on the linear regression. We show that our scheme achieves good estimation accuracy and γ through the simulation with a real Bitcoin blockchain.

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Green heterogeneous networks via an intelligent power control strategy and D2D communications

Panahi F., Panahi F., Hattab G., Ohtsuki T., Cabric D.

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC （IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC）  2017-October   1 - 8 2018年02月

ISSN  9781538635315

　概要を見る

© 2017 IEEE. Increased environmental awareness coupled with the rising cost of energy have sparked a keen interest in the deployment of energy-efficient communication technologies over the infrastructure of cellular networks. Base stations (BSs) are responsible for the largest portion of power consumption and energy usage in cellular networks. Thus, sleep/wake-up scheduling strategies for BSs can significantly improve energy-efficiency (EE) of cellular networks. In this paper, we propose a Fuzzy Q-Learning (FQL) based energy-efficient sleep/wake-up mechanism for BSs in a heterogeneous network (HetNet). The goal is to save energy, without compromising the offered Quality of Service (QoS), by switching off the redundant BSs according to the local traffic profile and depending on the required area coverage and cell EE. The introduction of sleep mode for BSs may lead to a large-scale coverage loss, unless a specific remedial solution is exploited at the same time. To this end, we also propose to use device-to-device (D2D) communications to extend network coverage to the service areas of the switched-off BSs. Simulation results validate that the proposed framework provides significant improvements in power consumption and the EE.

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Driver's blink detection using doppler sensor

Yamamoto K., Toyoda K., Ohtsuki T.

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC （IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC）  2017-October   1 - 6 2018年02月

ISSN  9781538635315

　概要を見る

© 2017 IEEE. Blink is a physiological signal that reflects drowsiness and concentration. It is important to detect driver's blinks without any wearable devices. For this purpose, a Doppler sensor has been used and several blink detection methods where a subject sits in front of such sensor have been proposed. However, it is challenging to detect driver's blinks because of face and body movement. In this paper, we propose a Doppler sensor-based driver's blink detection method in existence of face and body movement in a car. In the proposed method, blinks are detected through two steps: pre-detection and classification. In the first step which we call pre-detection, the time candidates of subject's blinks are detected based on spectrograms calculated from a received signal. Then, in the second one which we call classification, a set of features are calculated from a spectrogram and are fed into a supervised machine learning classifier to identify which time candidates are truly blinks. We leverage the fact that the distribution of the energy on a spectrogram differs between a blink and non-blink. Specifically, features are extracted based on the distribution of energy on a spectrogram. We conducted a series of experiments for the evaluation in the situation where a subject drives a real car in public road. As a result, we confirmed our method outperforms the conventional one in terms of F-measure calculated from recall rate and precision rate.

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Antenna parameters optimization in self-organizing networks: Multi-armed bandits with Pareto search

Dhahri C., Ohtsuki T.

IEEE Vehicular Technology Conference （IEEE Vehicular Technology Conference）  2017-September   1 - 5 2018年02月

ISSN  9781509059355

　概要を見る

© 2017 IEEE. With the huge increases in traffic volumes and subscribers, diverse devices, and rich media applications, manual management of mobile network becomes highly challenging in terms of optimization and management. Self-organizing networks (SON) has been introduced to optimize the network in an automatic manner. In this paper, we address the coverage and capacity joint optimization (CCO) by adaptively and simultaneously adjusting both antenna tilt and power. To this end, we propose: · a multi-player multi-armed bandit (MAB) framework (decentralized restless upper confidence bound (RUCB) algorithm) with a change point detection test based on Page-Hinkley (PH) statistics used to decide whether some change has occurred in the environment. Then, the strategy is designed to deal with such a change. · a central unit to deal with simultaneous conflicting actions when many cells decide to start the optimization process at the same time. · a Pareto search framework to deal with multi-objective optimization (CCO). To evaluate our work, we compared our proposal with the fixed antenna parameter scheme and with the linear scalarization function that transforms the multi-objective optimization problem into a scalar function. Simulation results show that the proposed method could improve user experience in terms of cell-center capacity and cell-edge coverage compared to different conventional methods and under different number of users.

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Hate Speech on Twitter: A Pragmatic Approach to Collect Hateful and Offensive Expressions and Perform Hate Speech Detection

大槻 知明

IEEE Access （IEEE Access）  6   13825 - 13835 2018年

研究論文（学術雑誌）,  ISSN  2169-3536

　概要を見る

© 2018 IEEE. With the rapid growth of social networks and microblogging websites, communication between people from different cultural and psychological backgrounds has become more direct, resulting in more and more 'cyber' conflicts between these people. Consequently, hate speech is used more and more, to the point where it has become a serious problem invading these open spaces. Hate speech refers to the use of aggressive, violent or offensive language, targeting a specific group of people sharing a common property, whether this property is their gender (i.e., sexism), their ethnic group or race (i.e., racism) or their believes and religion. While most of the online social networks and microblogging websites forbid the use of hate speech, the size of these networks and websites makes it almost impossible to control all of their content. Therefore, arises the necessity to detect such speech automatically and filter any content that presents hateful language or language inciting to hatred. In this paper, we propose an approach to detect hate expressions on Twitter. Our approach is based on unigrams and patterns that are automatically collected from the training set. These patterns and unigrams are later used, among others, as features to train a machine learning algorithm. Our experiments on a test set composed of 2010 tweets show that our approach reaches an accuracy equal to 87.4% on detecting whether a tweet is offensive or not (binary classification), and an accuracy equal to 78.4% on detecting whether a tweet is hateful, offensive, or clean (ternary classification).

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Superposed Multicarrier Transmission

大槻 知明

ITE Transactions on Media Technology and Applications ６ （ 1 ） 91 - 97 2018年

研究論文（学術雑誌）, 単著, 査読有り

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A closed-form expression for performance optimization of subcarrier intensity QAM signals-based relay-added FSO systems with APD

Trung H., Tien Hoa N., Huu Trung N., Ohtsuki T.

Physical Communication （Physical Communication）  31   203 - 211 2018年

研究論文（学術雑誌）, 共著, 査読有り,  ISSN  18744907

　概要を見る

© 2018 Elsevier B.V. This study characterizes the overall bit-error-rate (BER) performance optimization of avalanche photodiode (APD) receiver-based amplify-and-forward (AF) relay-added free-space optical (FSO) communication system. The system of interest uses subcarrier intensity quadrature amplitude modulation (QAM) signals and subjects to scintillation due to optical turbulence. A weak atmospheric turbulence (clear-air condition) scenario is considered, for which the received signal intensity fluctuation can be modeled as a log-normal random process. A closed-form expression of the overall BER is derived by taking into account the impact of atmospheric loss, turbulence strength, thermal-noise and shot-noise limited conditions. It can be inferred from the numerical results that using AF relay nodes can extend the transmission distance and minimize BER performance of FSO system significantly compared with the direct transmission. Moreover, the selection of APD's gain values is indispensable to minimize the system's BER performance. Hence, the designed system could achieve the minimal BER by selecting an optimal APD gain value appropriately. In addition, the optimal gain values of APD significantly depend on various conditions, such as noise temperature, link distance, and the number of relay nodes.

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A Low-Complexity High-Accuracy AR Based Channel Prediction Method for Interference Alignment

Ozawa M., Ohtsuki T., Panahi F., Jiang W., Takatori Y., Nakagawa T.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

　概要を見る

© 2018 IEEE. Interference alignment (IA) is a technique that can suppress interference with a small number of antennas by aligning interference signals using transmit weights. These weights are designed based on the channel state information (CSI) fed back from each receiver, however, under the timevarying channel, the estimated CSI can be delayed/outdated, which will result in an imperfect IA. Therefore, IA with channel prediction has attracted much attention. The auto regressive (AR) model is known as a prediction method that predicts a future state based on only the past states. In the conventional channel prediction based IA method, the past channels are used directly for prediction. Therefore, the number of calculations for prediction can be too large. In this paper, based on the AR model, we describe a low complexity and high accuracy channel prediction method for IA. To predict the future channel, we only use the differences of channels between adjacent times instead of using the past channels directly. This will lead to a very low channel prediction error. Simulations show that the proposed method improves prediction accuracy and requires less calculation than the conventional one. Moreover, the IA with the proposed channel prediction method will achieve a higher transmission rate.

• Access to Document (DOI)

Signal Restoration Based on Temporal Structure and Multi-Layer Architecture

Wang L., Ohtsuki T.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

　概要を見る

© 2018 IEEE. Signal restoration involves the removal or minimization of degradation such as attenuation, interference, and noise. Blind signal restoration is the process of estimating either the original signals or mixture functions from the degraded signals, without any prior information about original sources. In this paper, we present a novel approach to tackle the ill-posedness of the nonlinear blind source separation problem. The derivation of our algorithm is inspired by the idea of an efficient layer-by-layer representation to approximate the nonlinearity. Once such representations are built, a final output layer is constructed by solving a convex optimization problem. Thus, the projected data can break a nonlinear problem down into the version of generalized joint diagonalization problem in the feature space. Importantly, the parameters and forms of polynomials depend solely on the input data, which guarantee the robustness of the structure. We thus address the general problem without being restricted to any specific mixture or parametric model. Experimental results show that the proposed algorithm is able to recover the nonlinear mixture with higher separation accuracy on audio datasets from the real world.

• Access to Document (DOI)

Non-Contact Heartbeat Detection by MUSIC with Discrete Cosine Transform-Based Parameter Adjustment

Yamamoto K., Toyoda K., Ohtsuki T.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

　概要を見る

© 2018 IEEE. Heartbeat detection are receiving a lot of attention in the field of health care, since cardiac activity reflects various information of a subject, e.g., the stress. Many Doppler sensor-based heartbeat detection methods have been proposed so far. As one of such methods, the MUSIC (MUltiple SIgnal Classification)-based HR (Heart Rate) estimation method has been proposed. However, the conventional MUSIC-based HR estimation method not only needs a long time window, but also requires to know the number of sinusoidal signals composing the analyzed signals, P, in advance of applying MUSIC to the analyzed signal, which is challenge. In this paper, we propose a MUSIC-based HR estimation method with the DCT (Discrete Cosine Transform)-based parameter P selection. In the proposed method, the analyzed signal is firstly decomposed by DCT. The inverse DCT is then performed based on only components that might be related with heartbeats. The number of components used in the inverse DCT is selected as P. Through the experiments, we confirmed that our method outperformed the conventional one by the estimation accuracy of the HR and the stress indexes such as CVI (Cardiac Vagal Index) and CSI (Cardiac Sympathetic Index).

• Access to Document (DOI)

Learning-Based Optimal Channel Selection in the Presence of Jammer for Cognitive Radio Networks

Li A., Panahi F., Ohtsuki T., Han G.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

　概要を見る

© 2018 IEEE. Cognitive Radio (CR) technique has been proposed for improving spectrum efficiency by dynamic spectrum access. In Cognitive Radio Networks (CRNs), unlicensed Secondary Users (SUs) with CR can utilize licensed spectrum without interfering licensed Primary Users (PUs). For effectively avoiding interference with licensed PUs and malicious attacks from jammers, a two-stage Learning-based Optimal Channel Selection (LOCS) algorithm for unlicensed SUs in distributed heterogeneous CRNs is proposed in this paper. The LOCS algorithm enables SUs to obtain real states of the licensed channels without knowing their information. Hence, SUs using LOCS algorithm can efficiently avoid collision and attack with PUs and jammers. Besides, the LOCS algorithm considers hardware limitation of the SUs, i.e., SUs can only sense and access parts of the license spectrum during any given time. SUs can select the optimal channels for spectrum sensing and data transmission by using the LOCS algorithm. Simulation results show the efficiency of our proposed algorithm in terms of collision and attack avoidance.

• Access to Document (DOI)

Fall Detection Using UHF Passive RFID Based on the Neighborhood Preservation Principle

Kamoi H., Toyoda K., Ohtsuki T.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

　概要を見る

© 2018 IEEE. Injuries caused by falls are one of the major threats for the elderly. Thus, the demand for a fall detection system without requiring a user to wear or equip any devices is rapidly increasing. In this paper, a fall detection system using Ultra-High Frequency (UHF) passive Radio-Frequency IDentification (RFID) is presented. With the RFID reader on the ceiling and multiple RFID tags on the floor, anomaly scores are computed based on the neighborhood preservation principle. This focuses only on the correlation between RSSI fluctuations between a pair of tags. Therefore, unlike the conventional methods, our method manages to detect falls without requiring a large number of reference data. To detect falls in several areas of the room, our system only requires a simple reference data with one subject walking around the room. Experiments are conducted in an indoor environment with two different setups to show the effectiveness of our method. Our method improves the conventional one, and achieves a high Area Under Curve (AUC) of 0.98.

• Access to Document (DOI)

Enhanced Channel Hopping Algorithm for Heterogeneous Cognitive Radio Networks

Li A., Han G., Ohtsuki T.

2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings （2018 IEEE Global Communications Conference, GLOBECOM 2018 - Proceedings）  2018年

ISSN  9781538647271

　概要を見る

© 2018 IEEE. In Cognitive Radio Networks (CRNs), the available channels for the unlicensed Secondary Users (SUs) may be varying. When SUs want to communicate with each other, they must first access the same channel simultaneously. The process of accessing the same channel is referred to as a rendezvous process, by which SUs can exchange control information for establishing data transmission link. Channel Hoping (CH) is one of the most representative techniques for letting SUs rendezvous with each other. At the beginning of each time slot, SUs access available channels according to their CH Sequences (CHSs) generated by the CH algorithm. In our previous work, we have proposed a Heterogeneous Radio Rendezvous (HRR) algorithm to address the rendezvous problem for heterogeneous CRNs, where SUs may be equipped with different numbers of radios. In this paper, we propose an Enhanced HRR (EHRR) algorithm, which can further shorten the length of period for the CHSs. Compared with the HRR algorithm, the EHRR algorithm lowers the upper bounds of Maximum Time To Rendezvous (MTTR). Moreover, the upper bounds of MTTR for the EHRR algorithm are derived by theoretical analysis. In addition, the performance of the EHRR algorithm in terms of MTTR is evaluated by simulation. Simulation results show the superiority of the EHRR algorithm compared with the HRR algorithm in terms of MTTR.

• Access to Document (DOI)

A Smart City Based on Ambient Intelligence

大槻 知明

IEICE TRANSACTIONS ON COMMUNICATIONS E100B （ 9 ） 1547 - 1553 2017年09月

研究論文（学術雑誌）,  ISSN  0916-8516

• Access to Document (DOI)

Impact of Pointing Errors on the Error Performance of Intersatellite Laser Communications

大槻 知明

JOURNAL OF LIGHTWAVE TECHNOLOGY 35 （ 14 ） 3082 - 3091 2017年07月

研究論文（学術雑誌）,  ISSN  0733-8724

• Access to Document (DOI)

Improvements of femto-base station resource utilisation and ABS assignment convergence for dynamic ABS assignment

大槻 知明

IET COMMUNICATIONS 11 （ 3 ） 370 - 376 2017年02月

研究論文（学術雑誌）,  ISSN  1751-8628

• Access to Document (DOI)

Low Computational Complexity Direction-of-Arrival Estimation of Wideband Signal Sources Based on Squared TOPS

大槻 知明

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E100A （ 1 ） 219 - 226 2017年01月

研究論文（学術雑誌）,  ISSN  1745-1337

• Access to Document (DOI)

A Lower Bound on Secrecy Capacity for MIMO Wiretap Channel Aided by a Cooperative Jammer With Channel Estimation Error

大槻 知明

IEEE ACCESS 5   4636 - 4645 2017年

研究論文（学術雑誌）,  ISSN  2169-3536

• Access to Document (DOI)

Exploiting Interference for Capacity Improvement in Software-Defined Vehicular Networks

大槻 知明

IEEE ACCESS 5   10662 - 10673 2017年

研究論文（学術雑誌）,  ISSN  2169-3536

• Access to Document (DOI)

Novel Unsupervised SPITters Detection Scheme by Automatically Solving Unbalanced Situation

大槻 知明

IEEE ACCESS 5   6746 - 6756 2017年

研究論文（学術雑誌）,  ISSN  2169-3536

• Access to Document (DOI)

A low-complexity cell clustering algorithm in dense small cell networks

大槻 知明

EURASIP Journal on Wireless Communications and Networking 2016年12月

共著

DOA estimation for wideband signals based on weighted Squared TOPS

大槻 知明

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2016年10月

研究論文（学術雑誌）,  ISSN  1687-1499

• Access to Document (DOI)

DOA Estimation Using Temporal Spatial Virtual Array Based on Doppler Shift with Adaptive PRI Control

大槻 知明

Trans. of IEICE, EB 99-B （ 9 ） 2009 - 2018 2016年09月

共著

Suppress of wideband interferences based on superposition probability

大槻 知明

EURASIP Journal on Wireless Communications and Networking 2016 2016:198 DOI: 10.1186/s13638-016-0699-9 2016年08月

共著

Algorithm Design of Relative Magnitude Coefficients Using Brent's Method on the K-User MIMO-IFC

大槻 知明

Trans. of IEICE, EB 99-B （ 8 ） 1902 - 1909 2016年08月

Suppression of multiple wideband interferences based on superposition probability

大槻 知明

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2016年08月

研究論文（学術雑誌）,  ISSN  1687-1499

• Access to Document (DOI)

IGMM-Based Co-localization of Mobile Users With Ambient Radio Signals

大槻 知明

IEEE Internet of Things Journal, 2016 Accepted 2016年

共著

Multi-Point Fairness in Resource Allocation for C-RAN Downlink CoMP Transmission

大槻 知明

EURASIP Journal on Wireless Communications and Networking, 2016:12 DOI: 10.1186/s13638-015-0501-4 2016年

共著

A Pattern-Based Approach for Sarcasm Detection on Twitter

大槻 知明

IEEE Access, 10.1109/ACCESS.2016.2594194 2016年

共著

Power-absorption-based Model for Power Compensation in WLAN Positioning Using Smartphone

大槻 知明

Trans. of IEICE, EB 2015年06月

研究論文（学術雑誌）

Coalition Graph Game for Robust Routing in Cooperative Cognitive Radio Networks

大槻 知明

Mobile Networks and Applications, Springer Netherlands 2015年04月

共著

Signal Eigenvector-based Device-Free Passive Localization using Array Sensor

大槻 知明

IEEE Trans. on Vehicular Technology 2015年04月

研究論文（学術雑誌）, 共著

Intersection-Based Forwarding Protocol for Vehicular Ad Hoc Networks

大槻 知明

Telecommunication Systems, Springer Netherlands 2015年03月

研究論文（学術雑誌）, 共著

Analytical modeling of cognitive heterogeneous cellular networks over Nakagami-m fading

大槻 知明

EURASIP Journal on Wireless Communications and Networking 2015年03月

研究論文（学術雑誌）

Stochastic Geometry Modeling and Analysis of Cognitive Heterogeneous Cellular Networks

大槻 知明

EURASIP Journal on Wireless Communications and Networking 2015年03月

研究論文（学術雑誌）, 共著

Iterative estimation of undesired signal power for superposed multicarrier transmission with channel estimation error

大槻 知明

EURASIP Journal on Wireless Communications and Networking 2015年02月

研究論文（学術雑誌）

Impact of the CSI on the Performance of Cognitive Relay Networks with Partial Relay Selection

大槻 知明

IEEE Trans. on Vehicular Technology 2015年02月

研究論文（学術雑誌）

Ergodic Capacity Analysis of Full-duplex Mimo Relay Channel using Tracy-Widom Distribution with Processing Delay

大槻 知明

International Journal of Technology, Vol. 6, No. 2, 2015. http://dx.doi.org/10.14716/ijtech.v6i2.1003 2015年

共著

A Model Based on Poisson Point Process for Analyzing MIMO Heterogeneous Networks Utilizing Fractional Frequency Reuse

大槻 知明

IEEE Trans. on Wireless Communications 2014年12月

研究論文（学術雑誌）, 共著

Pollution-Free Regenerating Codes With Fast Reconstruction Verification for Verifiable Cloud Storage

大槻 知明

Journal of Networks 2014年11月

研究論文（学術雑誌）, 共著, 査読有り

Adaptive Q-Learning Cell Selection Method for Open-access Femtocell Networks: Multi- User Case

大槻 知明

Trans. of IEICE, EB 2014年08月

研究論文（学術雑誌）, 共著, 査読有り

A Model Based on Poison Point Process for Downlink K Tiers Fractional Frequency Reuse Heterogeneous Networks

大槻 知明

Physical Communications （Elsevier）  2014年04月

研究論文（学術雑誌）, 共著, 査読有り

• Access to Document (DOI)

Cell Selection for Open-Acces Femtocell Networks: Learning in Changing Environment

大槻 知明

Physical Communications （Elsevier）  2014年04月

研究論文（学術雑誌）, 共著, 査読有り

• Access to Document (DOI)

Optimal Channel-Sensing Scheme for Cognitive Radio Systems Based on Fuzzy Q-Learning

大槻 知明

Trans. of IEICE 2014年02月

研究論文（学術雑誌）, 共著, 査読有り

Capacity of Data Collection in Wireless Sensor Networks based on Mutual Information and MMSE Estimation

大槻 知明

International Scholarly Research Network (ISRN) Communications and Networking 2014年02月

研究論文（学術雑誌）, 共著, 査読有り

• Access to Document (DOI)

Development and Performance Evaluation of MMSE Macro-Diversity Reception System with Delay Difference Correction

大槻 知明

IEEE Trans. on Vehicular Technology 2013年11月

研究論文（学術雑誌）, 共著, 査読有り

Non-Cooperative Game based Packet Ferry Forwarding for Sparse Mobile Wireless Networks

大槻 知明

Wireless Communications and Mobile Computing 2013年11月

研究論文（学術雑誌）, 共著, 査読有り

Water-Filling Solution for Distributed Estimation of Correlated Data in WSN MIMO System

大槻 知明

International Scholarly Research Network (ISRN) Communications and Networking 2013年08月

研究論文（学術雑誌）, 共著, 査読有り

• Access to Document (DOI)

Learning Based Falling Detection Using Multiple Doppler Sensors

大槻 知明

Advances in Internet of Things 2013年06月

研究論文（学術雑誌）, 共著, 査読有り

Hidden Markov Model Based Localization Using Array Antenna

大槻 知明

International Journal of Wireless Information Networks 2013年06月

研究論文（学術雑誌）, 共著, 査読有り

MIMO Receiver with Antenna Subset Selection: Algorithm and Hardware Implementation

大槻 知明

Trans. of IEICE （IEICE）  E96-B （ 4 ） 1039-1050 2013年04月

研究論文（学術雑誌）, 共著, 査読有り

Cell Range Expansion Using Distributed Q-Learning in Heterogeneous Networks

大槻 知明

EURASIP Journal on Wireless Communications and Networking 2013年03月

研究論文（学術雑誌）, 共著, 査読有り

Benefits of Rich Scattering in MIMO Channels: A Graph-Theoretical Perspective

大槻 知明

IEEE Communications Letters 17 （ 1 ） 23-26 2013年01月

研究論文（学術雑誌）, 共著, 査読有り

Secure and Fast Aggregation of Financial Data in Cloud-Based Expense Tracking Applications

大槻 知明

Journal of Networks and Systems Management 20 （ 4 ） 534-560 2012年12月

研究論文（学術雑誌）, 共著, 査読有り

State Classification with Array Sensor using Support Vector Machine for Wireless Monitoring Systems

大槻 知明

Trans. of IEICE Vol. E95-B （ 10 ） 3088-3095 2012年10月

研究論文（学術雑誌）, 共著, 査読有り

Array Antenna based Localization Using Spatial Smoothing Processing

大槻 知明

Journal of Communications 7 （ 6 ） 427-435 2012年06月

研究論文（学術雑誌）, 共著, 査読有り

PAPR Reduction of TDD-CDMA Using Joint Transmission Technique

大槻 知明

Trans. of IEICE Vol.E95-B （ 3 ） 933-942 2012年03月

研究論文（学術雑誌）, 共著, 査読有り

Epidemic Theory Based H + 1 Hop Forwarding for Intermittently Connected Mobile Ad Hoc Networks

大槻 知明

EURASIP Journal on Wireless Communications and Networking 2012 （ 76 ）  2012年03月

研究論文（学術雑誌）, 共著, 査読有り

Transparent Inband Feedback for Training-Based MIMO Systems

大槻 知明

Wireless Personal Communications 63 （ 3 ） 587-599 2012年

研究論文（学術雑誌）, 共著, 査読有り

Joint SVD-GSVD precoding technique and secrecy capacity lower bound for the MIMO relay wire-tap channel

大槻 知明

EURASIP Journal on Wireless Communications and Networking 2012 2012年

研究論文（学術雑誌）, 共著, 査読有り

Subcarrier Mapping for Single-User SC-FDMA Relay Communications

大槻 知明

Trans. of IEICE Vol. E94-A （ 12 ） 2776-2779 2011年12月

研究論文（学術雑誌）, 共著, 査読有り

Robust DOA Estimation for Uncorrelated and Coherent Signals

大槻 知明

Trans. of IEICE Vol. EB94-A （ 10 ） 2035-2038 2011年10月

研究論文（学術雑誌）, 共著, 査読有り

Localization Using Iterative Angle of Arrival Method Sharing Snapshots of Coherent Subarrays

大槻 知明

EURASIP Journal on Advances in Signal Processing 2011 2011年08月

研究論文（学術雑誌）, 共著, 査読有り

Orthogonal Beamforming Using Gram-Schmidt Orthogonalization for Multi-User MIMO Downlink System

大槻 知明

EURASIP Journal on Wireless Communications and Networking 2011 2011年07月

研究論文（学術雑誌）, 共著, 査読有り

An autonomous cooperative ARQ Protocol for Hybrid Mobile Wireless Sensor Networks

大槻 知明

Journal of Communications vol. 6 （ no. 2 ） 157-167 2011年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Improving Accuracy for Range-Based Localization in Multiple Target Wireless Sensor Networks

大槻 知明

International Scholarly Research Network (ISRN) Communications and Networking 2011 2011年

研究論文（学術雑誌）, 共著, 査読有り

Typical Set Cognitive Sensing

大槻 知明

International Scholarly Research Network (ISRN) Communications and Networking 2011 2011年

研究論文（学術雑誌）, 共著, 査読有り

6Axes Sensor Assisted Low Complexity High Accuracy Visible Light Communication Based Indoor Positioning System,

大槻 知明

Trans. of IEICE （電子情報通信学会）  E93-B （ no. 11 ） 2879-2891 2010年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Evaluation of Iterative LDPC-Coded MIMO OFDM System with Time Interleaving

大槻 知明

Trans. of IEICE （電子情報通信学会）  E93-A （ no.12 ） 2654-2662 2010年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Transparent Inband Feedback for Training-Based MIMO Systems

大槻 知明

Wireless Personal Communications （Springer）     8 2010年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Multi-band Received Signal Strength Fingerprinting Based Indoor Location System

C. Sertthin, T. Fujii, T. Ohtsuki, and M. Nakagawa

Trans. of IEICE （IEICE）  E93-B （ no.8 ） 129-135 2010年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Half-Duplex Relaying with Serially-Concatenated Low-Density Generator Matrix (SCLDGM) Codes

YUSUKE KUMANO, TOMOAKI OHTSUKI

Elsevier Physical Communications （Elsevier）  3 （ 2 ） 129-165 2010年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Joint Feedback and Scheduling Scheme For Training-Based Multiuser MIMO Systems With Service Differentiation

O. Souihli and T. Ohtsuki

IEEE Trans. on Wireless Communications （IEEE）  9 （ 2 ） 528-533 2010年02月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

A New Load Balancing And Data Collection Algorithm For Energy Saving In Wireless Sensor Networks

X. Guan, L. Guan, X.G. Wang, and T. Ohtsuki

Telecommunication Systems （Springer Netherlands）  2010年02月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Detection Performance of Security System Using Radio Waves Based on Space-Time Signal Processing

S. Kawakami, K. Okane, and T. Ohtsuki

Procedia-Social and Behavioral Sciences （Elsevier）  vol.2 （ issue 1 ） 171-178 2010年02月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

User Selection Scheme with Secrecy Capacity in MIMO Downlink Systems

M. Yanase and T. Ohtsuki

Procedia-Social and Behavioral Sciences （Elsevier）  vol. 2 （ 1 ） 161-170 2010年02月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

An Improved User Selection Algorithm in Multiuser MIMO Broadcast with Channel Prediction

Z. Min and T. Ohtsuki

Trans. of IEICE （IEICE）  E92-B （ 8 ） 2667-2674 2009年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Indoor Event Detection with Signal Subspace Spanned by Eigenvector for Home or Office Security

S. Ikeda, H. Tsuji, and T. Ohtsuki

Trans. of IEICE （IEICE）  E92-B （ 7 ） 2406-2412 2009年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Random Beamforming Using Iterative Power Allocation with Small Feedback Information and Low Latency

Y. Tsuchiya and T. Ohtsuki

Trans. of IEICE （IEICE）  E92-B （ 5 ） 1908-1911 2009年05月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Turbo Equalized Double Window Cancellation and Combining Robust to Large Delay Spread Channel

J.H. Lee, T. Ohtsuki, and M. Nakagawa

Trans. of IEICE （IEICE）  E92-B （ 2 ） 517-526 2009年02月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Convergence Acceleration of Iterative Signal Detection for MIMO System with Belief Propagation

S. Gounai and T. Ohtsuki

Trans. of IEICE （IEICE）  E91-B （ 8 ） 2640-2648 2008年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Mapping for Iterative MMSE-SIC with Belief Propagation

S. Gounai, T. Ohtsuki, and T. Kaneko

Trans. of IEICE （IEICE）  E91-B （ 7 ） 2187-2197 2008年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

MIMO Systems in the Presence of Feedback Delay

K. Kobayashi, T. Ohtsuki, and T. Kaneko

Trans. of IEICE （IEICE）  E91-B （ 3 ） 829-836 2008年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

An indoor position estimation method by maximum likelihood algorithm using received signal strength

I. Yamada, T. Ohtsuki, T. Hisanaga, and Li Zheng

SICE Journal of Control, Measurement, and System Integration （SICE）  1 （ 3 ） 251-256 2008年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

MIMO System with Relative Phase Difference Time-Shift Modulation for Rician Fading Environment

K. Kobayashi, T. Someya, T. Ohtsuki, S. PW. Sigit, and T. Kashima

Trans. of IEICE （IEICE）  E91-B （ 2 ） 459-465 2008年02月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Scheduling Algorithm with Power Allocation for Random Unitary Beamforming

Y. Tsuchiya, T. Ohtsuki, and T. Kaneko

Trans. of IEICE （IEICE）  E91-B （ 1 ） 232-238 2008年01月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Double window cancellation and combining for OFDM in time-invariant large delay spread channel

J.H. Lee, Y. Kishiyama, T. Ohtsuki, and M. Nakagawa

Trans. of IEICE （IEICE）  E90-A （ 10 ） 2066-2078 2007年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Evaluation of SAGE Algorithm for Channel Estimation and Data Detection Using Superimposed Training in MIMO System

F. Tsuzuki and T. Ohtsuki

Trans. of IEICE （IEICE）  E90-B （ 6 ） 1460-1466 2007年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Lowering Error Floors of Irregular LDPC Code on Fast Fading Environment with Perfect and Imperfect CSIs

S. Gounai, T. Ohtsuki, and T. Kaneko

Trans. of IEICE （IEICE）  E90-B （ 3 ） 569-577 2007年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

LDPC codes in communications and broadcasting

大槻 知明

Trans. of IEICE （IEICE）  E90-B （ 3 ） 440-453 2007年03月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Companding System Based on Time Clustering for Reducing Peak Power of OFDM Symbol in Wireless Communications

O. Takyu, T. Ohtsuki, and M. Nakagawa

Trans. of IEICE （IEICE）  E89-A （ 7 ） 1884-1891 2006年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Chip-level Detection in Optical Frequency Hopping Code Division Multiple Access

S. Ichiki and T. Ohtsuki

Trans. of IEICE （IEICE）  E89-A （ 7 ） 2056-2062 2006年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Reliability-Based Hybrid ARQ (RB-HARQ) Schemes using Low-Density Parity-Check (LDPC) Codes

Y. Inaba, T. Saito, and T. Ohtsuki

Trans. of IEICE （IEICE）  E89-B （ 4 ） 1170-1177 2006年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Spatial Multiplexing in Optical Wireless MIMO Communications Over Indoor Environment

大槻 知明

Trans. of IEICE （IEICE）  E89-B （ 4 ） 1364-1371 2006年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

A Sign Selection Method of Orthogonal Variable Spreading Factor Code for Peak Power Reduction in Multi-Rate OFCDM Systems

O. Takyu, T. Ohtsuki, and M. Nakagawa

Trans. of IEICE （IEICE）  E89-B （ 4 ） 1333-1345 2006年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Bootstrapped Modified Weighted Bit Flipping Decoding of Low Density Parity Check Codes

Y. Inaba and T. Ohtsuki

Trans. of IEICE （IEICE）  E89-A （ 4 ） 1145-1149 2006年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Computational Complexity Reduction of MLD Based on SINR in MIMO Spatial Multiplexing Systems

K. Honjo and T. Ohtsuki

Trans. of IEICE （IEICE）  E89-B （ 3 ） 914-921 2006年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Analysis of Statistical STBC Cooperative Diversity Using Binary Sensors with Observation Noise

大槻 知明

Trans. of IEICE （IEICE）  E89-B （ 3 ） 970-973 2006年03月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Channel Estimation with Selective Superimposed Pilot Sequences under Fast Fading Environments

F. Tsuzuki and T. Ohtsuki

Trans. of IEICE （IEICE）  E89-B （ 2 ） 556-563 2006年02月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Frequency Domain Channel Estimation and Equalization for Direct Sequence-Ultra Wideband (DS-UWB) System

H. Sato and T. Ohtsuki

IEE Proc. Commun., （IEE）  vol. 153 （ 1 ） 93-98 2006年02月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Lowering Error Floor of Irregular LDPC Codes by CRC and OSD Algorithm

S. Gounai and T. Ohtsuki

Trans. of IEICE （IEICE）  E89-B （ 1 ） 1-10 2006年01月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Rake Performance for UWB-IR system with SISO and MISO

T. Ezaki and T. Ohtsuki

Trans. of IEICE （IEICE）  E88-B （ 10 ） 4112-4116 2005年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Throughput Maximization Transmission Control Scheme for MIMO Systems

K. Suto, Y. Hara, T. Ohtsuki, and Y. Takei

Trans. of IEICE （IEICE）  E88-B （ 10 ） 4040-4046 2005年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Multiple-subcarrier optical communication systems with subcarrier signal point sequence

S. Teramoto and T. Ohtsuki

IEEE Trans. on Communications （IEEE）   （ 10 ） 1738-1743 2005年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Computational Complexity and Performance of RAKE Receivers with Channel Estimation for DS-UWB

H. Sato and T. Ohtsuki

Trans. of IEICE （IEICE）  E88-A （ 9 ） 2318-2326 2005年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Decoding Algorithms Based on Oscillation for Low-Density Parity Check Codes

S. Gounai and T. Ohtsuki

Trans. of IEICE （IEICE）  E88-A （ 8 ） 2216-2226 2005年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Criterion for Improving Error Rate Degradation by Nonlinear Amplifier for Multicarrier Transmission

O. Takyu, T. Ohtsuki, and M. Nakagawa

Trans. of IEICE （IEICE）  E88-B （ 7 ） 3057-3069 2005年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Pre-Rake Diversity Combining for UWB Systems in IEEE 802.15 UWB Multipath Channel

S. Imada and T. Ohtsuki

Trans. of IEICE （IEICE）  E88-B （ 5 ） 2193-2199 2005年05月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Analysis of Atmospheric Optical Subcarrier Multiplexing Systems and Atmospheric Optical Subcarrier Modulated Code Division Multiplexing Systems

K. Wakafuji and T. Ohtsuki

IEEE Journal of Lightwave Technology （IEEE）  23 （ 10 ） 1676-1682 2005年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Optical Wireless Sensor Network System Using Corner Cube Retroreflectors (CCRs)

S. Teramoto and T. Ohtsuki

EURASIP Journal on Wireless Communications and Networking （EURASIP）  2005 （ 1 ） 39-44 2005年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Parallel Combinatory Multiple-Subcarrier Optical Wireless Communication Systems

N. Kitamoto and T. Ohtsuki

Wiley International Journal of Communication Systems （Wiley）  18 （ 3 ） 195-203 2005年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Multiple-Subcarrier Optical Communication System with Peak Reduction Carriers

S. Teramoto and T. Ohtsuki

Trans. of IEICE （IEICE）  E87-B （ 11 ） 3385-3388 2004年11月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Information and Signal Processing for Sensor Networks

A. Ogawa, T. Yamazato, and T. Ohtsuki

Trans. of IEICE （IEICE）  E87-A （ 10 ） 2599-2606 2004年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

OFDM Packet Transmission System with Added Pilot Semi-Blind Iterative Channel Estimation for Tracking Fast Time-Varying Channel

N. Ohkubo and T. Ohtsuki

Trans. of IEICE （IEICE）  E87-B （ 9 ） 2729-2737 2004年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Diversity Gain in Ultra Wideband Impulse Radio (UWB-IR)

T. Ezaki and T. Ohtsuki

Trans. of IEICE （IEICE）  E87-B （ 8 ） 2385-2390 2004年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Space-Time/Space-Frequency Block Coded OFDM with Diagonalized Maximum Likelihood Decoder (ST/SF-OFDM with DMLD)

H. Kanemaru and T. Ohtsuki

Trans. of IEICE （IEICE）  E87-B （ 7 ） 2034-2039 2004年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Space-Time-Frequency Block Codes over Frequency Selective Fading Channels

K. Suto and T. Ohtsuki

Trans. of IEICE （IEICE）  E87-B （ 7 ） 1939-1945 2004年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Frequency Offset Compensation with MMSE-MUD for Multi-Carrier CDMA in Quasi-Synchronous Uplink

O. Takyu, T. Ohtsuki, and M. Nakagawa

Trans. of IEICE （IEICE）  E87-B （ 6 ） 1495-1504 2004年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

高速フェージング環境における積距離分布及びトレース分布に基づく空間時間トレリス符号の設計基準

笹崎幸弘, 大槻知明

電子情報通信学会論文誌(B) （電子情報通信学会）  B （ 6 ） 773-783 2004年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Decoding Algorithms for Low-Density Parity-Check Codes with Multilevel Modulations

H. Futaki and T. Ohtsuki

Trans. of IEICE （IEICE）  E87-B （ 5 ） 1282-1289 2004年05月

研究論文（学術雑誌）, 共著, 査読有り

Low-Density Parity-Check (LDPC) Coded OFDM Systems: Bit Error Rate and The Number of Decoding Iterations

H. Futaki and T. Ohtsuki

Trans. of IEICE （IEICE）  E86-B （ 11 ） 3310-3336 2003年11月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Multibits/Sequence-Period Optical CDMA Receiver with Double Optical Hardlimiters

K. Wakafuji and T. Ohtsuki

Trans. of IEICE （IEICE）  E86-A （ 10 ） 2670-2677 2003年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Direct-Detection Optical CDMA Receiver With Interference Estimation and Double Optical Hardlimiters

K. Wakafuji and T. Ohtsuki

IEEE Journal of Lightwave Technology （IEEE）  21 （ 10 ） 2182-2188 2003年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Space-Time Transmit Diversity Schemes with Low-Density Parity-Check (LDPC) Codes

H. Futaki and T. Ohtsuki

Trans. of IEICE （IEICE）  E86-B （ 10 ） 3131-3136 2003年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Design Criteria for Phase Sequences in Selected Mapping

N. Okubo and T. Ohtsuki

Trans. of IEICE （IEICE）  E86-B （ 9 ） 2628-2636 2003年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Multiple Subcarrier Modulation for Infrared Wireless Systems Using Punctured Convolutional Codes and Variable Amplitude Block Codes

H. Yamaguchi, T. Ohtsuki, and I. Sasase

Trans. of IEICE （IEICE）  E86-B （ 8 ） 2433-2439 2003年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Improved Design Criteria and New Trellis Codes for Space-Time Trellis Coded Modulation in Fast Fading Channels

Y. Sasazaki and T. Ohtsuki

Trans. of IEICE （IEICE）  E86-B （ 3 ） 1057-1062 2003年03月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Multiple-Subcarrier Modulation in Optical Wireless Communications

大槻 知明

IEEE Communications Magazine （IEEE）  41 （ 3 ） 74-79 2003年03月

研究論文（学術雑誌）, 単著, 査読有り

Performance Analysis of Atmospheric Optical PPM CDMA Systems

大槻 知明

IEEE Journal of Lightwave Technology （IEEE）  21 （ 2 ） 406-411 2003年02月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

プレディストーション及び部分系列伝送を用いたOFDM非線形歪み補償法の特性評価

堀内隆明, 楊偉祖, 大槻知明, 笹瀬巌

電子情報通信学会論文誌(B) （電子情報通信学会）  B （ 11 ） 1865-1873 2002年11月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Equalization for Infrared Wireless Systems Using OOK-CDMA

H. Yamaguchi, R. Matsuo, T. Ohtsuki, and I. Sasase

Trans. of IEICE （IEICE）  E85-B （ 10 ） 2292-2299 2002年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

EMアルゴリズムを用いた空間時間符号化でのチャネル推定及び送信信号検出の簡略化

松尾綾子, 大槻知明, 笹瀬巌

電子情報通信学会論文誌(B) （電子情報通信学会論文誌）  B （ 1 ） 20-30 2002年01月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Indoor Infrared Wireless Systems using OOK-CDMA with Decision-Feedback Equalizer on Diffuse Channels

H. Yamaguchi, R. Matsuo, M. Matsuo, T. Ohtsuki, and I. Sasase

Trans. of IEICE （IEICE）  E84-B （ 4 ） 960-966 2001年04月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

光クリッパを設けた干渉除去器を適用した光符号分割多元接続方式

新宮秀樹, 鎌倉功弘, 大槻知明, 笹瀬巌

電子情報通信学会論文誌(B) （電子情報通信学会）  B （ 1 ） 50-60 2001年01月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

サイトダイバーシチを用いた無指向性直射室内赤外線無線通信システムの光路しゃへいに関する一検討

佐藤章博, 大槻知明, 笹瀬巌

電子情報通信学会論文誌(B) （電子情報通信学会）  B （ 12 ） 1692-1702 2000年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

BER Performance of Turbo-Coded PPM CDMA Systems on Optical Fiber

T. Ohtsuki and J. M. Kahn

IEEE Journal of Lightwave Technology （IEEE）  18 （ 12 ） 1776-1784 2000年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Direct-Detection Optical Synchronous CDMA Systems with Interference Canceller Using Group Information Codes

H. Sawagashira, K. Kamakura, T. Ohtsuki, and I. Sasase

Trans. of IEICE （IEICE）  E83-A （ 11 ） 2138-2142 2000年11月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

時間領域符号化を適用した光周波数領域符号化符号分割多元接続方式の特性解析

園田敏雄, 新宮秀樹, 鎌倉功弘, 大槻知明, 笹瀬巌

電子情報通信学会論文誌(B) （電子情報通信学会）  B （ 8 ） 1114-1124 2000年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

拡散反射通信路におけるOOK CDMA室内赤外線無線通信方式の特性解析

松尾綾子, 松尾道人, 大槻知明, 笹瀬巌, 宇多川智之

電子情報通信学会論文誌(B) （電子情報通信学会）  B （ 7 ） 970-979 2000年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

PPM CDMAを用いた室内赤外線無線通信方式の特性解析

大槻 知明

電子情報通信学会論文誌(B （電子情報通信学会）  J83-B （ 7 ） 937-947 2000年07月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Rate-Adaptive Indoor Infrared Wireless Communication Systems Using Repeated and Punctured Convolutional Code

T. Ohtsuki

IEEE Communications Letters （IEEE）  4 （ 2 ） 56-58 2000年02月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

パンクチャド畳込み符号と適応PPM変調を用いた適応ビットレート室内赤外線無線通信方式

松尾道人, 大槻知明, 笹瀬巌, 宇多川智之

電子情報通信学会論文誌(B) （電子情報通信学会）  J83-B （ 1 ） 14-21 2000年01月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

マルチパルス位置変調を用いた光符号分割多重方式

新宮秀樹, 蒲地嘉展, 大槻知明, 笹瀬巌

電子情報通信学会論文誌(B) （電子情報通信学会）  J82-B （ 9 ） 14-21 1999年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Analysis of ADSL with Punctured Convolutional Code

W. Yang, T. Ohtsuki, M. Ueda, K. Matsumoto, and I. Sasase

Trans. of IEICE （IEICE）  E82-B （ 7 ） 1030-1037 1999年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Optical Spread Time CDMA Communication Systems with PPM Signaling

K. Kamakura, T. Ohtsuki, and I. Sasase

Trans. of IEICE （IEICE）  E82-B （ 7 ） 1038-1047 1999年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Analysis of Direct-Detection Optical CDMA Systems with Optical Hard-Limiter using Equal-Weight Orthogonal Signaling

T. Ohtsuki

Trans. of IEICE （IEICE）  E82-B （ 3 ） 512-520 1999年03月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

干渉キャンセラを適用した光同期M-ary FSK/CDMA方式

蒲地嘉展, 大槻知明, 上原秀幸, 笹瀬巌

電子情報通信学会論文誌(B) （電子情報通信学会）  J82-B （ 1 ） 43-55 1999年01月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

拡散反射通信路を利用した室内赤外線無線通信における送信パルス波形の検討

佐藤章博, 大槻知明, 上原秀幸, 笹瀬巌

電子情報通信学会論文誌(B) （電子情報通信学会）  J81-BI （ 1 ） 861-871 1998年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Analysis of Optical Frequency Domain Encoding CDMA Enhancement of Frequency Division Multiplexing

K. Kamakura, Y. Gamachi, H. Uehara, T. Ohtsuki, and I. Sasase

Trans. of IEICE （IEICE）  E81-B （ 9 ） 1749-1757 1998年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Direct-Detection Optical Asynchronous CDMA Systems with Double Optical Hard-Limiters: APD Noise and Thermal Noise

T. Ohtsuki

Trans. of IEICE （IEICE）  E81-B （ 7 ） 1491-1499 1998年07月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Channel Interference Cancellation Using Electrooptic Switch and Optical Hard-Limiters for Direct-Detection Optical CDMA Systems

T. Ohtsuki

IEEE Journal of Lightwave Technology （IEEE）  16 （ 4 ） 520-526 1998年04月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Performance Analysis of Direct-Detection Optical Synchronous CDMA Systems with Co-Channel Interference Canceller

T. Ohtsuki, M. Takeoka, and E. Iwahashi

Trans. of IEICE （IEICE）  E80-A （ 11 ） 2260-2263 1997年11月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Analysis of Direct-Detection Optical Asynchronous CDMA Systems with Double Optical Hard-Limiters

T. Ohtsuki

IEEE Journal of Lightwave Technology （IEEE）  15 （ 3 ） 452-457 1997年03月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Direct-Detection Optical Synchronous CDMA Systems with Channel Interference Canceller using Time Division Reference Signal

T. Ohtsuki

Trans. of IEICE （IEICE）  E79-A （ 12 ） 1948-1956 1996年12月

研究論文（研究会，シンポジウム資料等）, 単著, 査読有り

Direct-Detection Optical Synchronous CDMA Systems with Double Optical Hard-Limiters Using Modified Prime Sequence Codes

T. Ohtsuki, K. Sato, I. Sasase, and S. Mori

IEEE Journal on Selected Areas on Communications （IEEE）  14 （ 9 ） 1879-1887 1996年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Cutoff Rate Analysis of Overlapping Multi-pulse Pulse Position Modulation (OMPPM) in Optical Direct-Detection Channel

T. Ohtsuki, I. Sasase, and S. Mori

Trans. of IEICE （IEICE）  E79-A （ 9 ） 1471-1474 1996年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Access Timing Controlled Direct-Detection Optical CDMA Systems with PPM Signaling

T. Ohtsuki, I. Sasase, and S. Mori

Trans. of IEICE （IEICE）  E79-A （ 9 ） 1392-1400 1996年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Effect of Imperfect Slot Synchronization on Direct-Detection Optical Synchronous CDMA Communication Systems with PPM Signaling

K. Sato, T. Ohtsuki, H. Uehara, and I. Sasase

IEEE Journal of Lightwave Technology （IEEE）  14 （ 9 ） 1963-1969 1996年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Analysis of Optical Synchronous PPM/CDMA Systems with Interference Canceller Under Number-State Light Field

Y. Gamachi, T. Ohtsuki, H. Uehara, and I. Sasase

Trans. of IEICE （IEICE）  E79-B （ 7 ） 915-922 1996年07月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Analysis of Multi-Pulse Pulse Position Modulation Using Avalanche Photodiode in Optical Intersatellite Links

N. Aoki, T. Ohtsuki, and I. Sasase

Trans. of IEICE （IEICE）  E79-B （ 1 ） 52-56 1996年01月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Effects of Hard-Limiter and Error Correction Coding on Performance of Direct-Detection Optical CDMA Systems with PPM Signaling

T. Ohtsuki, I. Sasase, and S. Mori

Trans. of IEICE （IEICE）  E78-A （ 9 ） 1092-1101 1995年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Coding for Multi-pulse PPM with Imperfect Slot Synchronization in Optical Direct-Detection Channels

K. Sato, T. Ohtsuki and I. Sasase

Trans. of IEICE （IEICE）  E78-B （ 6 ） 916-922 1995年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Lower Bounds on Capacity and Cutoff Rate of Differential Overlapping Pulse Position Modulation in Optical Direct-Detection Channel

T. Ohtsuki, I. Sasase, and S. Mori

Trans. of IEICE （IEICE）  E77-B （ 10 ） 1230-1237 1994年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Analysis of Coherent Optical POLSK Receivers with Local Oscillator Intensity Noise and Unmatched Quantum Efficiencies

H. Uehara, T. Ohtsuki, and I. Sasase

Trans. of IEICE （IEICE）  E77-B （ 10 ） 1590-1599 1994年10月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Error Performance of Overlapping Multi-Pulse Pulse Position Modulation (OMPPM) and Trellis Coded OMPPM in Optical Direct-Detection Channel

T. Ohtsuki, I. Sasase, and S. Mori

Trans. of IEICE （IEICE）  E77-B （ 9 ） 1133-1143 1994年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Analysis of Multi-Pulse PPM With Imperfect Slot Synchronization in Optical Direct-Detection Channel

K. Sato, T. Ohtsuki, and I. Sasase

Trans. of IEICE （IEICE）  E77-B （ 8 ） 1032-1039 1994年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Capacity and Cutoff Rate of Overlapping Multi-pulse Pulse Position Modulation (OMPPM) in Optical Direct-Detection Channel: Quantum Limited Case

T. Ohtsuki, I. Sasase, and S. Mori

Trans. of IEICE （IEICE）  E77-B （ 8 ） 1298-1308 1994年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Performance Analysis of Multi-pulse Pulse Position Modulation (MPPM) in Noisy Photon Counting Channel

T. Ohtsuki, I. Sasase, and S. Mori

Trans. of IEICE （IEICE）  E77-A （ 8 ） 1381-1386 1994年08月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Polarization Shift Keying (POLSK) Phase Diversity Homodyne System with Multiplying Detection in Coherent Optical Communications

I. Seto, T. Ohtsuki, I. Sasase, and S. Mori

Journal of Fibre and Integrated Optics 12 （ 4 ） 381-395 1993年09月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

The Effects of Laser Phase Noise on Optical Coherent Coded Subcarrier Multiplexing System with Distributing Local Oscillator in Local Loop

T. Ohtsuki, I. Sasase, and S. Mori

Trans. of IEICE （IEICE）  E76-C （ 2 ） 293-300 1993年06月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Parallel Rate-Variable Punctured Convolutional Coded PPM in Photon Communication

T. Ohtsuki, H. Yashima, I. Sasase, and S. Mori

Trans. of IEICE （IEICE）  E75-A,C （ 1 ） 46-51,44-49 1992年01月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Coherent Optical Polarization-Shift-Keying (POLSK) Homodyne System Using Phase-Diversity Receivers

I. Seto, T. Ohtsuki, H. Yashima, I. Sasase, and S. Mori

Trans. of IEICE （IEICE）  E75-A,C （ 1 ） 52-59,50-57 1992年01月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り

Cutoff Rate and Capacity of MPPM in Noiseless Photon Counting Channel

T. Ohtsuki, H. Yashima, I. Sasase, and S. Mori

Trans. of IEICE （IEICE）  E74 （ 12 ） 4080-4084 1991年12月

研究論文（研究会，シンポジウム資料等）, 共著, 査読有り