Details of a Researcher - Ohtsuki, Tomoaki

Papers - Ohtsuki, Tomoaki

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

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

IEEE Transactions on Vehicular Technology 2025

Corresponding author

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

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

IEEE Signal Processing Letters 32 271 - 275 2025

Corresponding author

Deep Learning-based Channel Estimation to Mitigate Channel Aging in Massive MIMO with Pilot Contamination

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

IEEE Access 13 1834 - 1845 2025

Research paper (scientific journal), Corresponding author

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

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

IEEE Internet of Things Journal 2025

Research paper (scientific journal), Last author

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

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

IEEE Trans. on Wireless Communications 2025

Research paper (scientific journal), Corresponding author

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

Research paper (scientific journal), Corresponding author

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

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

Bioengineering 2025 20 2025

Research paper (scientific journal), Last author

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

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

IEEE Transactions on Vehicular Technology 20 2025

Corresponding author

Toward 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 20 1624 - 1635 2025

Research paper (scientific journal), Corresponding author

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

H. Zhangcheng and T. Ohtsuki

IEEE Internet of Things Journal 2025

Research paper (scientific journal), Last author

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

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

IEEE Internet of Things Journal 2025

Research paper (scientific journal), Last author

A Cross-subject Transfer Learning Method for CSI-based Wireless Sensing

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

IEEE Internet of Things Journal 2025

Research paper (scientific journal), Last author

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

S. Yang, T. Ohtsuki, and X. Jiang

IEEE Internet of Things Journal 2025

Research paper (scientific journal), Corresponding author

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

N. Ji, Y. Zhang, T. Ohtsuki, G. Gui, C. Yuen, and F. Adachi

IEEE Transactions on Cognitive Communications and Networking 2025

Research paper (scientific journal), Corresponding author

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

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

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025

Research paper (scientific journal), Corresponding author

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

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

IEEE Internet of Things Journal 2025

Research paper (scientific journal), Last author

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

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

IEEE Wireless Communications Letters 2025

Research paper (scientific journal), Corresponding author

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

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

IEEE Access 2025

Research paper (scientific journal), Corresponding author

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

Research paper (scientific journal), Corresponding author

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

Research paper (scientific journal), Last author

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

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

IEEE Transactions on Vehicular Technology 74 ( 3 ) 5114 - 5124 2025.03

Research paper (scientific journal)

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

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

ACM Computing Surveys 57 ( 107 ) 1 - 30 2025.01

Research paper (scientific journal)

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

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

Sensors 25 ( 1 ) 271 2025.01

Research paper (scientific journal), Last author

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

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

IEEE Journal of Biomedical and Health Informatics 2025.01

Research paper (scientific journal), Corresponding author

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 2025.01

Research paper (scientific journal), Last author

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

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

Computer Networks 2024.10

Research paper (scientific journal), Corresponding author

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

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

IEEE Transactions on Network Science and Engineering 11 ( 4 ) 3876 - 3890 2024.07

Research paper (scientific journal), Corresponding author

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

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

IEEE Geoscience and Remote Sensing Letters 2024

Research paper (scientific journal), Corresponding author

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

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

IEEE Internet of Things Journal 2024

Research paper (scientific journal), Corresponding author

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

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

IEEE Geoscience and Remote Sensing Letters 2024

Research paper (scientific journal), Corresponding author

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

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

IEEE Internet of Things Journal 2024

Research paper (scientific journal), Corresponding author

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

Research paper (scientific journal), Last author

Improved Deep Learning Methods for Beamforming in Secure SWIPT Networks

V. Ganapathy, R. Ramachandran, and T. Ohtsuki

IEEE Transactions on Green Communications and Networking 2024

Research paper (scientific journal), Last author

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

Research paper (scientific journal), Last author

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

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

Sensors 2024 2024

Research paper (scientific journal), Last author

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

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

Bioengineering 11 ( 11 ) 1124 2024

Research paper (scientific journal), Corresponding author

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

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

IEEE Access 12 55506 - 55520 2024

Research paper (scientific journal), Last author

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

Y. Wang, H. Zhao, T. Ohtsuki, C. Yuen, H. Sari, and G. Gui

IEEE Trans. on Wireless Communications 2024

Research paper (scientific journal), Corresponding author

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

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

IEEE Access 2024

Research paper (scientific journal), Last author

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

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

IEEE Access (73, no. 3, pp. 3445-3456) 3 ( 73 ) 3445 - 3456 2024.03

Research paper (scientific journal), Corresponding author

Deep Learning Methods for Secure IoT SWIPT Networks

V. Ganapathy, R. Ramachandran, and T. Ohtsuki

IEEE Internet of Things Journal 2024

Research paper (scientific journal), Last author

Self-Supervised Learning Malware Traffic Classification Based on Masked Auto-Encoder

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

IEEE Internet of Things Journal 2024

Research paper (scientific journal), Corresponding author

Activity Detection in Indoor Environments using Multiple 2D LiDARs

M. Bouazizi, A. L. Mora, K. Feghou, and T. Ohtsuki

IEEE Internet of Things Journal 2024

Research paper (scientific journal), Last author

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

IEEE Access 2024

Corresponding author

Dementia Detection from Speech: What if Language Models Are not the Answer?

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

Information 2024

Research paper (scientific journal), Last author

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

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

Sensors 2023

Research paper (scientific journal), Corresponding author

A Novel Approach for Heartbeat Rate Estimation using 3D Lidar and Multi-Beam MIMO Doppler Radar

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

IEEE Sensors Journal 2023

Research paper (scientific journal), Last author

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

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

IEEE Transactions on Communications 2023

Research paper (scientific journal), Last author, Corresponding author

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

Research paper (scientific journal), Corresponding author

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

V. Ganapathy, R. Ramachandran, and T. Ohtsuki

Sensors 2023

Research paper (scientific journal), Last author

Specific Emitter Identification Using Adaptive Signal Feature Embedded Knowledge Graph

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

IEEE Internet of Things Journal 2023

Research paper (scientific journal), Corresponding author

Detecting Dementia from Face-Related Features with Automated Computational Methods

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

Bioengineering 2023

Research paper (scientific journal), Corresponding author

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

Research paper (scientific journal), Joint Work, Last author

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

Research paper (scientific journal), Joint Work, Last author

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

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

IEEE Trans. on Vehicular Technology 2023

Research paper (scientific journal), Joint Work

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

Research paper (scientific journal), Joint Work

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

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

Sensors 2023 2023

Research paper (scientific journal), Joint Work, Last author

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

Research paper (scientific journal), Joint Work, Last author

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

Research paper (scientific journal), Joint Work, Last author

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

Research paper (scientific journal), Joint Work

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

Research paper (scientific journal), Joint Work, Last author

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 2023.02

Research paper (scientific journal), Joint Work, Last author

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

Research paper (scientific journal), Joint Work

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

Research paper (scientific journal), Joint Work, Last author

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 2022.12

Research paper (scientific journal), Joint Work

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

Research paper (scientific journal), Joint Work

Fetal Arrhythmia Detection based on Labeling Considering Heartbeat Interval

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

Bioengineering 2023

Research paper (scientific journal), Joint Work, Last author

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

Research paper (scientific journal), Joint Work, Last author

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 2023.01

Research paper (scientific journal), Joint Work, Last author

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 2023.06

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.

Access to Document (DOI)

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 2023.05

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 2023.05

ISSN 15513203

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 ) 2023.03

ISSN 14248220

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.

Access to Document (DOI)

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 2023.02

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 2023.02

ISSN 09168516

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 ) 2023.01

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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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 2023.01

ISSN 09521976

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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Fetal Arrhythmia Detection Based on Labeling Considering Heartbeat Interval

Nakatani S., Yamamoto K., Ohtsuki T.

Bioengineering (Bioengineering) 10 ( 1 ) 2023.01

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 ) 2023.01

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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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

ISSN 00189456

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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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

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

ISSN 15361276

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

ISSN 1530437X

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’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

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’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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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

ISSN 23319860

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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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

ISSN 15253511

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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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

ISSN 1545598X

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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Intelligent Cellular Offloading with VLC-enabled Unmanned Aerial Vehicles

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

IEEE Internet of Things Journal 2023

Research paper (scientific journal), Last author

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

Research paper (scientific journal), Corresponding author

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 2022.12

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 2022.12

ISSN 15361233

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 ) 2022.12

ISSN 14248220

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 2022.09

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 2022.09

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 2022.09

ISSN 00189545

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 ) 2022.08

ISSN 14248220

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 2022.07

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 2022.06

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 2022.06

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 ) 2022.05

ISSN 14248220

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 2022.03

ISSN 00189545

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 2022.03

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 ) 2022.03

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 2022.02

ISSN 07338716

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 2022.01

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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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

ISSN 1557170X

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

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

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

ISSN 15502252

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

ISSN 15503607

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

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

ISSN 15502252

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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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

ISSN 15503607

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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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

ISSN 15502252

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

ISSN 15502252

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

ISSN 15502252

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

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

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.

ISSN 1557170X

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) 2022

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

ISSN 15502252

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

ISSN 15502252

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

ISSN 15503607

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

ISSN 10709908

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

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

ISSN 1557170X

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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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

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

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

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

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

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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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 2021.12

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 2021.12

ISSN 00906778

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 2021.09

ISSN 00906778

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 2021.09

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 2021.09

ISSN 21622337

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 2021.08

ISSN 21622337

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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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 2021.08

ISSN 21622337

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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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 2021.08

ISSN 07338716

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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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) 2021.06

ISSN 15503607

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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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 2021.06

ISSN 15361276

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) 2021.06

ISSN 15503607

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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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) 2021.06

ISSN 15503607

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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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 2021.05

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 2021.05

ISSN 00189480

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 ) 2021.04

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 2021.01

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 2021.01

ISSN 00189545

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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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

ISSN 15502252

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

ISSN 15502252

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

ISSN 15253511

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

ISSN 15502252

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.

ISSN 1557170X

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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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

ISSN 15502252

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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Discrete BP Polar Decoder Using Information Bottleneck Method

Yamada A., Ohtsuki T.

IEEE Access (IEEE Access) 9 10645 - 10656 2021

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

ISSN 15253511

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

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

ISSN 15502252

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

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

ISSN 15502252

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.

ISSN 1557170X

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

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

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

ISSN 15253511

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

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

ISSN 15253511

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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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

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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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

ISSN 15502252

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

ISSN 09168516

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

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

ISSN 09168516

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

ISSN 15206149

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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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 2020.12

ISSN 15361284

© 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 2020.11

ISSN 00906778

© 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 2020.09

ISSN 0167739X

© 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 2020.08

ISSN 00189545

© 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.

ISSN 9781728119908

© 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.

ISSN 9781728119908

© 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.

ISSN 9781728119908

© 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 2020.07

© 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 2020.06

ISSN 9781728150895

© 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 2020.06

ISSN 21563357

© 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 2020.06

© 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 2020.06

ISSN 9781728150895

© 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 2020.06

ISSN 9781728150895

© 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 2020.05

ISSN 9781728152073

© 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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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 2020.05

ISSN 9781728152073

© 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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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 2020.05

ISSN 9781728152073

© 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 2020.05

ISSN 9781728152073

© 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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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 ) 2020.04

© 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 2020.02

ISSN 00189294

© 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

© 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

© 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

© 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

© 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

© 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) 2019.12

ISSN 9781728109626

© 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) 2019.12

ISSN 9781728109626

© 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) 2019.12

ISSN 9781728109626

© 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 2019.10

ISSN 21622337

© 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 2019.09

ISSN 9781728112206

© 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 2019.09

ISSN 20960654

© 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.

ISSN 9781538613115

© 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 2019.07

ISSN 9781728113395

© 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.

ISSN 9781538613115

© 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 2019.05

ISSN 9781538683507

© 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 2019.05

ISSN 9781728113289

© 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 2019.05

ISSN 9781538680889

© 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 2019.05

ISSN 9781538680889

© 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 ) 2019.05

© 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 2019.05

ISSN 9781538680889

© 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.

ISSN 9781479981311

© 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 2019.05

ISSN 9781538680889

© 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 2019.04

ISSN 9781728115085

© 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 2019.03

ISSN 9789881476852

© 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 2019.02

ISSN 9781538692882

© 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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Underdetermined Blind Source Separation with Multi-Subspace for Nonlinear Representation

Wang L., Ohtsuki T.

IEEE Access (IEEE Access) 7 84545 - 84557 2019

© 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

© 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

ISSN 9783030011765

© 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

© 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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A Novel Methodology for HYIP Operators' Bitcoin Addresses Identification

Toyoda K., Takis Mathiopoulos P., Ohtsuki T.

IEEE Access (IEEE Access) 7 74835 - 74848 2019

© 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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Precoding Aided Generalized Spatial Modulation With an Iterative Greedy Algorithm

Yuwen Cao, Tomoaki Ohtsuki

IEEE Access (IEEE Access) 6 72449 - 72457 2018.11

© 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) 2018.11

ISSN 9781728111384

© 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) 2018.11

ISSN 9781728111384

© 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) 2018.11

ISSN 9781728111384

© 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.

ISSN 9781538636466

© 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

Chen Ye, Kentaro Toyoda, Tomoaki Ohtsuki

IEEE Transactions on Biomedical Engineering (IEEE Transactions on Biomedical Engineering) 66 ( 6 ) 1730 - 1741 2018.10

ISSN 00189294

© 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

Mondher Bouazizi, Tomoaki Ohtsuki

IEEE Access (IEEE Access) 6 64486 - 64502 2018.10

© 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 2018.10

© 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 2018.10

ISSN 10897798

© 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

Chen Ye,Guan Gui, Li Xu, Tomoaki Ohtsuki

IEEE Access (IEEE Access) 6 56069 - 56083 2018.09

© 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 2018.07

ISSN 9781538631805

© 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 2018.07

ISSN 9781538631805

© 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 2018.07

ISSN 9781538631805

© 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 2018.07

ISSN 9781538663554

© 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 2018.07

ISSN 9781538663554

© 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

Lu Wang, Tomoaki Ohtsuki

IEEE Access (IEEE Access) 6 42837 - 42850 2018.07

© 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 2018.07

ISSN 9781538679753

© 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

Fereidoun H. Panahi , Farzad H. Panahi , Ghaith Hattab , Tomoaki Ohtsuki, Danijela Cabric

IEEE Transactions on Green Communications and Networking (IEEE Transactions on Green Communications and Networking) 1 ( 4 ) 915 - 931 2018.06

© 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 2018.04

ISSN 9781538622896

© 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 2018.02

ISSN 9781538607091

© 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 2018.02

ISSN 9781538607091

© 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 2018.02

ISSN 9781538635315

© 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.

ISSN 9781538635315

© 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.

Access to Document (DOI)

Driver's blink detection using doppler sensor

Yamamoto K., Toyoda K., Ohtsuki T.

ISSN 9781538635315

© 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 2018.02

ISSN 9781509059355

© 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

Watanabe, Hajime, Bouazizi, Mondher, Ohtsuki, Tomoaki

IEEE ACCESS (IEEE Access) 6 13825 - 13835 2018

Research paper (scientific journal), ISSN 2169-3536

© 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

OHTSUKI TOMOAKI

ITE Transactions on Media Technology and Applications ６ ( 1 ) 91 - 97 2018

Research paper (scientific journal), Single Work, Accepted

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A Closed-form Expression for Performance Optimization of Subcarrier Intensity QAM Signals-Based Relay-Added FSO Systems with APD

H.D. Trung, N.T. Hoa, N.H. Trung, T. Ohtsuki,

Physical Communications (Physical Communication) 31 203 - 211 2018

Research paper (scientific journal), Joint Work, Accepted, ISSN 18744907

© 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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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

ISSN 9781538647271

© 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.

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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

ISSN 9781538647271

© 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).

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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

ISSN 9781538647271

© 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.

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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

ISSN 9781538647271

© 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.

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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

ISSN 9781538647271

© 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.

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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

ISSN 9781538647271

© 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.

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A Smart City Based on Ambient Intelligence

Ohtsuki, Tomoaki

IEICE TRANSACTIONS ON COMMUNICATIONS E100B ( 9 ) 1547 - 1553 2017.09

Research paper (scientific journal), ISSN 0916-8516

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Impact of Pointing Errors on the Error Performance of Intersatellite Laser Communications

Song, Tianyu, Wang, Qian, Wu, Ming-Wei, Ohtsuki, Tomoaki, Gurusamy, Mohan, Kam, Pooi-Yuen

JOURNAL OF LIGHTWAVE TECHNOLOGY 35 ( 14 ) 3082 - 3091 2017.07

Research paper (scientific journal), ISSN 0733-8724

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Improvements of femto-base station resource utilisation and ABS assignment convergence for dynamic ABS assignment

Ozawa, Masayoshi, Ohtsuki, Tomoaki

IET COMMUNICATIONS 11 ( 3 ) 370 - 376 2017.02

Research paper (scientific journal), ISSN 1751-8628

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Low Computational Complexity Direction-of-Arrival Estimation of Wideband Signal Sources Based on Squared TOPS

Hayashi, Hirotaka, Ohtsuki, Tomoaki

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES E100A ( 1 ) 219 - 226 2017.01

Research paper (scientific journal), ISSN 1745-1337

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A Lower Bound on Secrecy Capacity for MIMO Wiretap Channel Aided by a Cooperative Jammer With Channel Estimation Error

Iwata, Shunya, Ohtsuki, Tomoaki, Kam, Pooi-Yuen

IEEE ACCESS 5 4636 - 4645 2017

Research paper (scientific journal), ISSN 2169-3536

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Exploiting Interference for Capacity Improvement in Software-Defined Vehicular Networks

Guan, Xin, Huang, Yan, Chen, Min, Wu, Huayang, Ohtsuki, Tomoaki, Zhang, Yan

IEEE ACCESS 5 10662 - 10673 2017

Research paper (scientific journal), ISSN 2169-3536

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Novel Unsupervised SPITters Detection Scheme by Automatically Solving Unbalanced Situation

Toyoda, Kentaroh, Park, Mirang, Okazaki, Naonobu, Ohtsuki, Tomoaki

IEEE ACCESS 5 6746 - 6756 2017

Research paper (scientific journal), ISSN 2169-3536

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A low-complexity cell clustering algorithm in dense small cell networks

R. Seno, T. Ohtsuki, W. Jiang, and Y. Takatori

EURASIP Journal on Wireless Communications and Networking 2016.12

Joint Work

DOA estimation for wideband signals based on weighted Squared TOPS

Hayashi, Hirotaka, Ohtsuki, Tomoaki

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2016.10

Research paper (scientific journal), ISSN 1687-1499

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DOA Estimation Using Temporal Spatial Virtual Array Based on Doppler Shift with Adaptive PRI Control

H. Hayashi and T. Ohtsuki

Trans. of IEICE, EB 99-B ( 9 ) 2009 - 2018 2016.09

Joint Work

Suppress of wideband interferences based on superposition probability

Y. Kakizaki, Y. Shibata, T. Ohtsuki, and J. Mashino

EURASIP Journal on Wireless Communications and Networking 2016 2016:198 DOI: 10.1186/s13638-016-0699-9 2016.08

Joint Work

Algorithm Design of Relative Magnitude Coefficients Using Brent's Method on the K-User MIMO-IFC

K. Matsumura and T. Ohtsuki

Trans. of IEICE, EB 99-B ( 8 ) 1902 - 1909 2016.08

Suppression of multiple wideband interferences based on superposition probability

Kakizaki, Yuto, Shibata, Yohei, Ohtsuki, Tomoaki, Mashino, Jun

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2016.08

Research paper (scientific journal), ISSN 1687-1499

Access to Document (DOI)

IGMM-Based Co-localization of Mobile Users With Ambient Radio Signals

P. Varela, J. Hong, T. Ohtsuki, and X. Qin

IEEE Internet of Things Journal, 2016 Accepted 2016

Joint Work

Multi-Point Fairness in Resource Allocation for C-RAN Downlink CoMP Transmission

A. Beylerian and T. Ohtsuki

EURASIP Journal on Wireless Communications and Networking, 2016:12 DOI: 10.1186/s13638-015-0501-4 2016

Joint Work

A Pattern-Based Approach for Sarcasm Detection on Twitter

B. Mondher and T. Ohtsuki

IEEE Access, 10.1109/ACCESS.2016.2594194 2016

Joint Work

Power-absorption-based Model for Power Compensation in WLAN Positioning Using Smartphone

M. Yamamoto and T. Ohtsuki

Trans. of IEICE, EB 2015.06

Research paper (scientific journal)

Coalition Graph Game for Robust Routing in Cooperative Cognitive Radio Networks

X. Guan, A. Li, Z. Cai, and T. Ohtsuki

Mobile Networks and Applications, Springer Netherlands 2015.04

Joint Work

Signal Eigenvector-based Device-Free Passive Localization using Array Sensor

J. Hong and T. Ohtsuki

IEEE Trans. on Vehicular Technology 2015.04

Research paper (scientific journal), Joint Work

Intersection-Based Forwarding Protocol for Vehicular Ad Hoc Networks

X. Guan, Y. Huang, Z. Cai, and T. Ohtsuki

Telecommunication Systems, Springer Netherlands 2015.03

Research paper (scientific journal), Joint Work

Analytical modeling of cognitive heterogeneous cellular networks over Nakagami-m fading

F.H. Panahi and T. Ohtsuki

EURASIP Journal on Wireless Communications and Networking 2015.03

Research paper (scientific journal)

Stochastic Geometry Modeling and Analysis of Cognitive Heterogeneous Cellular Networks

F.H. Panahi and T. Ohtsuki

EURASIP Journal on Wireless Communications and Networking 2015.03

Research paper (scientific journal), Joint Work

Impact of the CSI on the Performance of Cognitive Relay Networks with Partial Relay Selection

W. Jaafar, T. Ohtsuki, W. Ajib, and D. Haccoun

IEEE Trans. on Vehicular Technology 2015.02

Research paper (scientific journal)

Iterative estimation of undesired signal power for superposed multicarrier transmission with channel estimation error

Y. Shibata, N. Yoda, T. Ohtsuki, J. Mashino, and T.Sugiyama

EURASIP Journal on Wireless Communications and Networking 2015.02

Research paper (scientific journal)

Ergodic Capacity Analysis of Full-duplex Mimo Relay Channel using Tracy-Widom Distribution with Processing Delay

A.S. Arifin and T. Ohtsuki,

International Journal of Technology, Vol. 6, No. 2, 2015. http://dx.doi.org/10.14716/ijtech.v6i2.1003 2015

Joint Work

A Model Based on Poisson Point Process for Analyzing MIMO Heterogeneous Networks Utilizing Fractional Frequency Reuse

H. Zhuang and T. Ohtsuki

IEEE Trans. on Wireless Communications 2014.12

Research paper (scientific journal), Joint Work

Pollution-Free Regenerating Codes With Fast Reconstruction Verification for Verifiable Cloud Storage

J.C. Corena and T. Ohtsuki

Journal of Networks 2014.11

Research paper (scientific journal), Joint Work, Accepted

Adaptive Q-Learning Cell Selection Method for Open-access Femtocell Networks: Multi- User Case

D. Chaima and T. Ohtsuki

Trans. of IEICE, EB 2014.08

Research paper (scientific journal), Joint Work, Accepted

A Model Based on Poison Point Process for Downlink K Tiers Fractional Frequency Reuse Heterogeneous Networks

H. Zhuang and T. Ohtsuki

Physical Communications (Elsevier) 2014.04

Research paper (scientific journal), Joint Work, Accepted

Access to Document (DOI)

Cell Selection for Open-Acces Femtocell Networks: Learning in Changing Environment

D. Chaima and T. Ohtsuki

Physical Communications (Elsevier) 2014.04

Research paper (scientific journal), Joint Work, Accepted

Access to Document (DOI)

Optimal Channel-Sensing Scheme for Cognitive Radio Systems Based on Fuzzy Q-Learning

F.H. Panahi and T. Ohtsuki

Trans. of IEICE 2014.02

Research paper (scientific journal), Joint Work, Accepted

Capacity of Data Collection in Wireless Sensor Networks based on Mutual Information and MMSE Estimation

A.S. Arifin and T. Ohtsuki

International Scholarly Research Network (ISRN) Communications and Networking 2014.02

Research paper (scientific journal), Joint Work, Accepted

Access to Document (DOI)

Development and Performance Evaluation of MMSE Macro-Diversity Reception System with Delay Difference Correction

OHTSUKI TOMOAKI

IEEE Trans. on Vehicular Technology 2013.11

Research paper (scientific journal), Joint Work, Accepted

Non-Cooperative Game based Packet Ferry Forwarding for Sparse Mobile Wireless Networks

OHTSUKI TOMOAKI

Wireless Communications and Mobile Computing 2013.11

Research paper (scientific journal), Joint Work, Accepted

Water-Filling Solution for Distributed Estimation of Correlated Data in WSN MIMO System

OHTSUKI TOMOAKI

International Scholarly Research Network (ISRN) Communications and Networking 2013.08

Research paper (scientific journal), Joint Work, Accepted

Access to Document (DOI)

Learning Based Falling Detection Using Multiple Doppler Sensors

OHTSUKI TOMOAKI

Advances in Internet of Things 2013.06

Research paper (scientific journal), Joint Work, Accepted

Hidden Markov Model Based Localization Using Array Antenna

OHTSUKI TOMOAKI

International Journal of Wireless Information Networks 2013.06

Research paper (scientific journal), Joint Work, Accepted

MIMO Receiver with Antenna Subset Selection: Algorithm and Hardware Implementation

OHTSUKI TOMOAKI

Trans. of IEICE (IEICE) E96-B ( 4 ) 1039-1050 2013.04

Research paper (scientific journal), Joint Work, Accepted

Cell Range Expansion Using Distributed Q-Learning in Heterogeneous Networks

OHTSUKI TOMOAKI

EURASIP Journal on Wireless Communications and Networking 2013.03

Research paper (scientific journal), Joint Work, Accepted

Benefits of Rich Scattering in MIMO Channels: A Graph-Theoretical Perspective

OHTSUKI TOMOAKI

IEEE Communications Letters 17 ( 1 ) 23-26 2013.01

Research paper (scientific journal), Joint Work, Accepted

Secure and Fast Aggregation of Financial Data in Cloud-Based Expense Tracking Applications

OHTSUKI TOMOAKI

Journal of Networks and Systems Management 20 ( 4 ) 534-560 2012.12

Research paper (scientific journal), Joint Work, Accepted

State Classification with Array Sensor using Support Vector Machine for Wireless Monitoring Systems

OHTSUKI TOMOAKI

Trans. of IEICE Vol. E95-B ( 10 ) 3088-3095 2012.10

Research paper (scientific journal), Joint Work, Accepted

Array Antenna based Localization Using Spatial Smoothing Processing

OHTSUKI TOMOAKI

Journal of Communications 7 ( 6 ) 427-435 2012.06

Research paper (scientific journal), Joint Work, Accepted

PAPR Reduction of TDD-CDMA Using Joint Transmission Technique

OHTSUKI TOMOAKI

Trans. of IEICE Vol.E95-B ( 3 ) 933-942 2012.03

Research paper (scientific journal), Joint Work, Accepted

Epidemic Theory Based H + 1 Hop Forwarding for Intermittently Connected Mobile Ad Hoc Networks

OHTSUKI TOMOAKI

EURASIP Journal on Wireless Communications and Networking 2012 ( 76 ) 2012.03

Research paper (scientific journal), Joint Work, Accepted

Transparent Inband Feedback for Training-Based MIMO Systems

OHTSUKI TOMOAKI

Wireless Personal Communications 63 ( 3 ) 587-599 2012

Research paper (scientific journal), Joint Work, Accepted

Joint SVD-GSVD precoding technique and secrecy capacity lower bound for the MIMO relay wire-tap channel

OHTSUKI TOMOAKI

EURASIP Journal on Wireless Communications and Networking 2012 2012

Research paper (scientific journal), Joint Work, Accepted

Subcarrier Mapping for Single-User SC-FDMA Relay Communications

OHTSUKI TOMOAKI

Trans. of IEICE Vol. E94-A ( 12 ) 2776-2779 2011.12

Research paper (scientific journal), Joint Work, Accepted

Robust DOA Estimation for Uncorrelated and Coherent Signals

OHTSUKI TOMOAKI

Trans. of IEICE Vol. EB94-A ( 10 ) 2035-2038 2011.10

Research paper (scientific journal), Joint Work, Accepted

Localization Using Iterative Angle of Arrival Method Sharing Snapshots of Coherent Subarrays

OHTSUKI TOMOAKI

EURASIP Journal on Advances in Signal Processing 2011 2011.08

Research paper (scientific journal), Joint Work, Accepted

Orthogonal Beamforming Using Gram-Schmidt Orthogonalization for Multi-User MIMO Downlink System

OHTSUKI TOMOAKI

EURASIP Journal on Wireless Communications and Networking 2011 2011.07

Research paper (scientific journal), Joint Work, Accepted

An autonomous cooperative ARQ Protocol for Hybrid Mobile Wireless Sensor Networks

N. Lynn, O. Takyu, K. Adachi, M. Nakagawa, and T. Ohtsuki

Journal of Communications vol. 6 ( no. 2 ) 157-167 2011.04

Research paper (conference, symposium, etc.), Joint Work, Accepted

Improving Accuracy for Range-Based Localization in Multiple Target Wireless Sensor Networks

OHTSUKI TOMOAKI

International Scholarly Research Network (ISRN) Communications and Networking 2011 2011

Research paper (scientific journal), Joint Work, Accepted

Typical Set Cognitive Sensing

OHTSUKI TOMOAKI

International Scholarly Research Network (ISRN) Communications and Networking 2011 2011

Research paper (scientific journal), Joint Work, Accepted

6Axes Sensor Assisted Low Complexity High Accuracy Visible Light Communication Based Indoor Positioning System,

C. Sertthin, T. Ohtsuki, and M. Nakagawa

Trans. of IEICE (IEICE) E93-B ( no. 11 ) 2879-2891 2010.12

Research paper (conference, symposium, etc.), Joint Work, Accepted

Performance Evaluation of Iterative LDPC-Coded MIMO OFDM System with Time Interleaving

K. Mitsuyama, T. Ikeda, and T. Ohtsuki

Trans. of IEICE (IEICE) E93-A ( no.12 ) 2654-2662 2010.12

Research paper (conference, symposium, etc.), Joint Work, Accepted

Transparent Inband Feedback for Training-Based MIMO Systems

O. Souihli and T. Ohtsuki

Wireless Personal Communications (Springer) 8 2010.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2010.08

Research paper (conference, symposium, etc.), Joint Work, Accepted

Half-Duplex Relaying with Serially-Concatenated Low-Density Generator Matrix (SCLDGM) Codes

YUSUKE KUMANO, TOMOAKI OHTSUKI

Elsevier Physical Communications (Elsevier) 3 ( 2 ) 129-165 2010.06

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2010.02

Research paper (conference, symposium, etc.), Single Work, Accepted

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) 2010.02

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2010.02

Research paper (conference, symposium, etc.), Single Work, Accepted

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 2010.02

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2009.08

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2009.07

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2009.05

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2009.02

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2008.08

Research paper (conference, symposium, etc.), Joint Work, Accepted

Mapping for Iterative MMSE-SIC with Belief Propagation

S. Gounai, T. Ohtsuki, and T. Kaneko

Trans. of IEICE (IEICE) E91-B ( 7 ) 2187-2197 2008.07

Research paper (conference, symposium, etc.), Joint Work, Accepted

MIMO Systems in the Presence of Feedback Delay

K. Kobayashi, T. Ohtsuki, and T. Kaneko

Trans. of IEICE (IEICE) E91-B ( 3 ) 829-836 2008.03

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2008.03

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2008.02

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2008.01

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2007.10

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2007.06

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2007.03

Research paper (conference, symposium, etc.), Joint Work, Accepted

LDPC codes in communications and broadcasting

OHTSUKI TOMOAKI

Trans. of IEICE (IEICE) E90-B ( 3 ) 440-453 2007.03

Research paper (conference, symposium, etc.), Single Work, Accepted

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 2006.07

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2006.07

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2006.04

Research paper (conference, symposium, etc.), Joint Work, Accepted

Spatial Multiplexing in Optical Wireless MIMO Communications Over Indoor Environment

D. Takase and T. Ohtsuki

Trans. of IEICE (IEICE) E89-B ( 4 ) 1364-1371 2006.04

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2006.04

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2006.04

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2006.03

Research paper (conference, symposium, etc.), Joint Work, Accepted

Performance Analysis of Statistical STBC Cooperative Diversity Using Binary Sensors with Observation Noise

OHTSUKI TOMOAKI

Trans. of IEICE (IEICE) E89-B ( 3 ) 970-973 2006.03

Research paper (conference, symposium, etc.), Single Work, Accepted

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 2006.02

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2006.02

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2006.01

Research paper (conference, symposium, etc.), Joint Work, Accepted

Rake Performance for UWB-IR system with SISO and MISO

T. Ezaki and T. Ohtsuki

Trans. of IEICE (IEICE) E88-B ( 10 ) 4112-4116 2005.10

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2005.10

Research paper (conference, symposium, etc.), Joint Work, Accepted

Multiple-subcarrier optical communication systems with subcarrier signal point sequence

S. Teramoto and T. Ohtsuki

IEEE Trans. on Communications (IEEE) ( 10 ) 1738-1743 2005.10

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2005.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2005.08

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2005.07

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2005.05

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2005.04

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2005.03

Research paper (conference, symposium, etc.), Joint Work, Accepted

Parallel Combinatory Multiple-Subcarrier Optical Wireless Communication Systems

N. Kitamoto and T. Ohtsuki

Wiley International Journal of Communication Systems (Wiley) 18 ( 3 ) 195-203 2005.03

Research paper (conference, symposium, etc.), Joint Work, Accepted

Multiple-Subcarrier Optical Communication System with Peak Reduction Carriers

S. Teramoto and T. Ohtsuki

Trans. of IEICE (IEICE) E87-B ( 11 ) 3385-3388 2004.11

Research paper (conference, symposium, etc.), Joint Work, Accepted

Information and Signal Processing for Sensor Networks

A. Ogawa, T. Yamazato, and T. Ohtsuki

Trans. of IEICE (IEICE) E87-A ( 10 ) 2599-2606 2004.10

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2004.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

Diversity Gain in Ultra Wideband Impulse Radio (UWB-IR)

T. Ezaki and T. Ohtsuki

Trans. of IEICE (IEICE) E87-B ( 8 ) 2385-2390 2004.08

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2004.07

Research paper (conference, symposium, etc.), Joint Work, Accepted

Space-Time-Frequency Block Codes over Frequency Selective Fading Channels

K. Suto and T. Ohtsuki

Trans. of IEICE (IEICE) E87-B ( 7 ) 1939-1945 2004.07

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2004.06

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2004.06

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2004.05

Research paper (scientific journal), Joint Work, Accepted

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 2003.11

Research paper (conference, symposium, etc.), Joint Work, Accepted

Multibits/Sequence-Period Optical CDMA Receiver with Double Optical Hardlimiters

K. Wakafuji and T. Ohtsuki

Trans. of IEICE (IEICE) E86-A ( 10 ) 2670-2677 2003.10

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2003.10

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2003.10

Research paper (conference, symposium, etc.), Joint Work, Accepted

Design Criteria for Phase Sequences in Selected Mapping

N. Okubo and T. Ohtsuki

Trans. of IEICE (IEICE) E86-B ( 9 ) 2628-2636 2003.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2003.08

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2003.03

Research paper (conference, symposium, etc.), Joint Work, Accepted

Multiple-Subcarrier Modulation in Optical Wireless Communications

OHTSUKI TOMOAKI

IEEE Communications Magazine (IEEE) 41 ( 3 ) 74-79 2003.03

Research paper (scientific journal), Single Work, Accepted

Performance Analysis of Atmospheric Optical PPM CDMA Systems

OHTSUKI TOMOAKI

IEEE Journal of Lightwave Technology (IEEE) 21 ( 2 ) 406-411 2003.02

Research paper (conference, symposium, etc.), Single Work, Accepted

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 2002.11

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2002.10

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2002.01

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2001.04

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2001.01

Research paper (conference, symposium, etc.), Single Work, Accepted

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 2000.12

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2000.12

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2000.11

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2000.08

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 2000.07

Research paper (conference, symposium, etc.), Joint Work, Accepted

PPM CDMAを用いた室内赤外線無線通信方式の特性解析

OHTSUKI TOMOAKI

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 2000.07

Research paper (conference, symposium, etc.), Single Work, Accepted

Rate-Adaptive Indoor Infrared Wireless Communication Systems Using Repeated and Punctured Convolutional Code

T. Ohtsuki

IEEE Communications Letters (IEEE) 4 ( 2 ) 56-58 2000.02

Research paper (conference, symposium, etc.), Single Work, Accepted

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 2000.01

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1999.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1999.07

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1999.07

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1999.03

Research paper (conference, symposium, etc.), Single Work, Accepted

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 1999.01

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1998.12

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1998.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1998.07

Research paper (conference, symposium, etc.), Single Work, Accepted

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 1998.04

Research paper (conference, symposium, etc.), Single Work, Accepted

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 1997.11

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1997.03

Research paper (conference, symposium, etc.), Single Work, Accepted

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 1996.12

Research paper (conference, symposium, etc.), Single Work, Accepted

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 1996.12

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1996.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1996.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1996.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1996.07

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1996.01

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1995.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1995.06

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1994.10

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1994.10

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1994.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1994.08

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1994.08

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1994.08

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1993.09

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1993.06

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1992.01

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1992.01

Research paper (conference, symposium, etc.), Joint Work, Accepted

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 1991.12

Research paper (conference, symposium, etc.), Joint Work, Accepted

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