論文 - 大槻　知明

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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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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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

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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