Stitch-able Split Learning Assisted Multi-UAV Systems
Tingkai Sun; Xiaoyan Wang; Xiucai Ye; Biao Han, 責任著者
IEEE Open Journal of the Computer Society, 2024年12月, ［査読有り］
Comparison of Learning Models for Wideband Interference Mitigation in Automotive Radar Systems
Yudai Suzuki; Xiaoyan Wang; Masahiro Umehira, 責任著者
IEICE Communications Express, 2024年12月, ［査読有り］
Wideband Interference Suppression for Automotive mmWave CS Radar: From Algorithm-based to Learning-based Approaches
Xiaoyan Wang; Ryoto Koizumi; Masahiro Umehira; Ran Sun; Shigeki Takeda, 筆頭著者
IEICE Transactions on Communications, 2024年12月, ［査読有り］, ［招待有り］
Adaptive Interference Mitigation for Time-varing Narrowband Interference in Automotive CS Radars
Masahiro Umehira; Yoshihiro Takeuchi; Xiaoyan Wang, ラスト(シニア)オーサー
International Radar Conference (RADAR 2024), 2024年10月, ［査読有り］
CoLLM: A Collaborative LLM Inference Framework for Resource-Constrained Devices
Jinrong Li; Biao Han; Sudan Li; Xiaoyan Wang; Jie Li
IEEE/CIC International Conference on Communications in China (ICCC 2024), 2024年08月, ［査読有り］
Electric Semantic Compression based 6G Wireless Sensing and Communication Integrated Resource Allocation
Haijun Liao; Jinchao Fan; Haoyu Ci; Jiahua Gu; Zhenyu Zhou; Bin Liao; Xiaoyan Wang; Shahid Mumtaz
IEEE Internet of Things Journal, 2024年08月, ［査読有り］
Performance and Inference Time Tradeoff for RNN Model Based Wideband Inter-Radar Interference Mitigation
Yudai Suzuki; Xiaoyan Wang; Masahiro Umehira; Ran Sun; Shigeki Takeda, 責任著者
International Conference on Ubiquitous and Future Networks (ICUFN 2024), 2024年07月, ［査読有り］
Measurement and Analysis of Narrowband Interference in Automotive CS Radars and Impact on Interference Suppression Techniques
Masahiro Umehira; Yoshihiro Takeuchi; Xiaoyan Wang, ラスト(シニア)オーサー
International Radar Symposium (IRS 2024), 2024年07月, ［査読有り］
Multi-radar interference experiment and performance evaluations on algorithm-based and learning-based schemes
Ryoto Koizumi; Xiaoyan Wang and Masahiro Umehira, 責任著者, ACM
International Symposium on Information and Communication Technology (SOICT 2023), 2023年12月07日, ［査読有り］
〔主要な業績〕Experimental Evaluations on Learning-based Inter-radar Wideband Interference Mitigation Method
Ryoto Koizumi; Xiaoyan Wang; Masahiro Umehira; Ran Sun and Shigeki Takeda, 責任著者, IEICE
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2023年12月01日, ［査読有り］
Wideband Interference Mitigation for Automotive mmWave Radar: From Algorithm-based to Learning-based Methods
Xiaoyan Wang, 筆頭著者, IEICE
International Conference on Emerging Technologies for Communications (ICETC 2023), 2023年11月29日, ［査読有り］, ［招待有り］
Deep Reinforcement Learning-Based On-off Analog Beamforming Coordination for Downlink MISO Networks
Hang Zhou; Xiaoyan Wang; Masahiro Umehira; Yusheng Ji, 責任著者, IEEE
IEEE Cyber Science and Technology Congress (IEEE CyberSciTech 2023), 2023年11月23日, ［査読有り］
〔主要な業績〕Energy Efficient Beamforming for Small Cell Systems: A distributed Learning and Multicell Coordination Approach
Hang Zhou; Xiaoyan Wang; Masahiro Umehira; Biao Han and Hao Zhou, 責任著者, ACM
ACM Transactions on Sensor Networks, 2023年10月01日, ［査読有り］
Split Learning Assisted Multi-UAV System for Image Classification Task
Tingkai Sun; Xiaoyan Wang; Masahiro Umehira; and Yusheng Ji, 責任著者, IEEE
IEEE Vehicular Technology Conference (IEEE VTC 2023-spring), 2023年06月20日, ［査読有り］
Time Synchronization-Aware Edge-End Collaborative Network Routing Management for FL-Assisted Distributed Energy Scheduling
Zijia Yao; Lurui Jia; Yutong Wang; Zhao Wang; Zhenyu Zhou; Bin Liao; Shahid Mumtaz; and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
IEEE International Conference on Communications (IEEE ICC 2023), 2023年05月28日, ［査読有り］
Endogenous Security-Aware Device Scheduling for Federated Learning-Assisted Low-Carbon Smart Park
Zijia Yao; Sunxuan Zhang; Ziqi Yu; Zhenyu Zhou; Shahid Mumtaz; and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
IEEE International Conference on Communications (IEEE ICC 2023), 2023年05月28日, ［査読有り］
Experimental Evaluation of Interference Suppression using Interference Replica to Improve Spectrum Efficiency for Automotive CS Radars
Masahiro Umehira; Takahiro Maruyama; Xiaoyan Wang and Shigeki Takeda, DGON
International Radar Symposium (IRS 2023), 2023年05月24日, ［査読有り］
Asynchronous FDRL-based Low-Latency Computation Offloading for Integrated Terrestrial and Non-Terrestrial Power IoT
Sifeng Li; Sunxuan Zhang; Zhao Wang; Zhenyu Zhou; Xiaoyan Wang; Shahid Mumtaz and Mohsen Guizani, IEEE
IEEE Network, 2023年03月01日, ［査読有り］
Integrated Sensing, Communication, and Computing for Self-Powered UAV-Assisted Corona Detection in High-Voltage Substations
Yiling Shu; Haijun Liao; Zijia Yao; Zhenyu Zhou and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
IEEE Sensor journal, 2023年03月01日, ［査読有り］
RNN-based Interference Suppression Method for CS radar: Simulation and Experimental Evaluations
Ryoto Koizumi; Xiaoyan Wang; Masahiro Umehira; Shigeki Takeda and Ran Sun, 責任著者, IEEE
International Conference on Artificial Intelligence in Information and Communication (ICAIIC 2023), 2023年02月20日, ［査読有り］
A Millimeter-Wave Automotive Radar with High Angular Resolution for Identification of Closely Spaced On-road Obstacles
Ran Sun; Kouhei Suzuki; Yuri Owada; Shigeki Takeda; Masahiro Umehira; Xiaoyan Wang and Hiroshi Kuroda, Springer Nature
Scientific Reports, 2023年02月01日, ［査読有り］
IMRG: Impedance Matching Oriented Receiver Grouping for MIMO WPT System
Lulu Tang; Hao Zhou; Weiming Guo; Wangqiu Zhou; Xing Guo and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
International Conference on Mobility, Sensing and Networking (IEEE MSN 2022), 2022年12月14日, ［査読有り］
MHMC: Real-Time Hand Motion Capture Using Millimeter-Wave Radar
Fan Deng; Hao Zhou; Peng Xia; Zian Wang; Xiaoyan Wang and Xiang-Yang Li, IEEE
IEEE International Conference on Parallel and Distributed Systems (IEEE ICPADS 2022), 2022年12月14日, ［査読有り］
Dispatching and Control Information Freshness-Aware Federated Learning for Simplified Power IoT
Zehan Jia; Ziqi Yu; Haijun Liao; Zhao Wang; Zhenyu Zhou; Xiaoyan Wang; Guoqing He; Shahid Mumtaz and Mohsen Guizani, IEEE
IEEE Global Communications Conference (IEEE Globecom 2022), 2022年12月04日, ［査読有り］
Adaptive Learning-Based Secure and Energy-Aware Resource Management for Multi-Mode Low-Carbon PIoT
Haijun Liao; Zehan Jia; Zhao Wang; Zhenyu Zhou; Xiaoyan Wang; Shahid Mumtaz and Mohsen Guizani, IEEE
IEEE Global Communications Conference (IEEE Globecom 2022), 2022年12月04日, ［査読有り］
Deep Reinforcement Learning based Secondary User Transmit Power Control for Underlay Cognitive Radio Networks
Kouhei Kato; Xiaoyan Wang; Masahiro Umehira and Yusheng Ji, 責任著者, ACM
ACM Research in Adaptive and Convergent Systems (ACM RACS 2022), 2022年10月03日, ［査読有り］
FLoRa: Sequential fuzzy extractor based physical layer key generation for LPWAN
Biao Han; Yahui Li; Xiaoyan Wang; Hanxun Li; Jinsen Huang, Elsevier
Future Generation Computer Systems, 2022年10月01日, ［査読有り］
A Usage Aware Dynamic Spectrum Access Scheme by Exploiting Deep Reinforcement Learning
Xiaoyan Wang; Yuto Teraki; Masahiro Umehira; Hao Zhou and Yusheng Ji, 筆頭著者, MDPI
Sensors, 2022年09月12日, ［査読有り］
Narrowband Interference Suppression Using Envelop Detection-Based Interference Replica Regeneration for Automotive CS Radars
Masahiro Umehira; Takahiro Maruyama; Yuu Watanabe; Xiaoyan Wang and Shigeki Takeda
International Radar Symposium (IRS 2022), 2022年09月12日, ［査読有り］
Green Spectrum Sharing Framework in B5G Era by Exploiting Crowdsensing
Xiaoyan Wang; Masahiro Umehira; Mina Akimoto; Biao Han and Hao Zhou, 筆頭著者, IEEE
IEEE Transactions on Green Communications and Networking, 2022年09月01日, ［査読有り］
DRL-assisted Topology Identification and Time Synchronization for PIoT-empowered Distributed Renewable Resource Dispatch
Zhenyu Zhou; Chen Liu; Zhao Wang; Lei Lv; Le Zhang; Xing Li; Xiaoyan Wang; Wenwen Sun; Guoqing He; and Yun Liu, IEEE
IEEE Internet of Things Magazine, 2022年09月01日, ［査読有り］
Social-Aware Learning-Based Online Energy Scheduling for 5G Integrated Smart Distribution Power Grid
Lurui Jia; Haijun Liao; Zhenyu Zhou; Xiyang Yin; Guoyuan Lv; Zhongyu Wang; Zhixin Lu; Yizhao Liu; Wenbing Lu; Xiufan Ma; and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
IEEE Transactions on Computational Social Systems, 2022年09月01日, ［査読有り］
A Deep Reinforcement Learning based Analog Beamforming Approach in Downlink MISO Systems
Hang Zhou; Xiaoyan Wang; Masahiro Umehira; and Yusheng Ji, 責任著者, IEEE
IEEE Vehicular Technology Conference (IEEE VTC 2022-spring), 2022年06月19日, ［査読有り］
Digital Twin-Empowered Communication Network Resource Management for Low-Carbon Smart Park
Xiaoyu Su; Zehan Jia; Zhenyu Zhou; Zhong Gan; Xiaoyan Wang and Shahid Mumtaz, IEEE
IEEE International Conference on Communications (IEEE ICC 2022), 2022年05月16日, ［査読有り］
Efficient Synchronous MAC Protocols for Terahertz Networking in Wireless Data Center
Tao Wang; Xiangquan Shi; Jing Tao; Xiaoyan Wang and Biao Han, IEEE
IEEE Conference on Computer Communications (IEEE INFOCOM) Workshops on Intelligent Cloud Computing and Networking, 2022年05月02日, ［査読有り］
Asynchronous Federated Learning Empowered Computation Offloading in Collaborative Vehicular Networks
Gexing Tian; Yifei Ren; Chao Pan; Zhenyu Zhou and Xiaoyan Wang, IEEE
IEEE Wireless Communications and Networking Conference (IEEE WCNC 2022) Workshop on Intelligent Computing and Caching at the Network Edge, 2022年04月10日, ［査読有り］
An Advanced Wideband Interference Suppression Technique using Envelope Detection and Sorting for Automotive FMCW Radar
Takuto Shimura; Masahiro Umehira; Yuu Watanabe; Xiaoyan Wang and Shigeki Takeda, IEEE
IEEE Radar Conference, 2022年03月21日, ［査読有り］
Asynchronous Federated Deep Reinforcement Learning-Based URLLC-Aware Computation Offloading in Space-Assisted Vehicular Networks
Chao Pan; Zhao Wang; Haijun Liao; Zhenyu Zhou; Xiaoyan Wang; Muhammad Tariq; and Sattam Al-Otaibi, IEEE
IEEE Transactions on Intelligent Transportation Systems, 2022年02月28日, ［査読有り］
A Simplified DPD Linearizer using Operation Point Estimation Pilot Signals for Mobile Terminal Application in TDD Wireless Systems
Atsushi Masubuchi; Masahiro Umehira; Xiaoyan Wang; and Shigeki Takeda, IEEE
nternational Symposium on Wireless Personal Multimedia Communications (WPMC 2021), 2021年12月14日, ［査読有り］
Deep Reinforcement Learning based Usage Aware Spectrum Access Scheme
Yuto Teraki; Xiaoyan Wang; Masahiro Umehira and Yusheng Ji, 責任著者, IEEE
International Symposium on Wireless Personal Multimedia Communications (WPMC 2021), 2021年12月14日, ［査読有り］
TraceModel: An Automatic Anomaly Detection and Root Cause Localization Framework for Microservice Systems
Yang Cai; Biao Han; Jinshu Su and Xiaoyan Wan, IEEE
International Conference on Mobility, Sensing and Networking (IEEE MSN 2021), 2021年12月13日, ［査読有り］
IMFi: IMU-WiFi based Cross-modal Gait Recognition System with Hot-Deployment
Zengyu Song; Hao Zhou; Shan Wang; Jinmeng Fan; Kaiwen Guo; Wangqiu Zhou; Xiaoyan Wang and Xiang-Yang Li, IEEE
International Conference on Mobility, Sensing and Networking (IEEE MSN 2021), 2021年12月13日, ［査読有り］
Federated Deep Actor-Critic-Based Task Offloading in Air-Ground Integrated PIoT
Sunxuan Zhang; Haijun Liao; Zhenyu Zhou; Yang Wang; Hui Zhang; Xiaoyan Wang; Shahid Mumtaz and Mohsen Guizani, IEEE
IEEE Global Communications Conference (IEEE Globecom 2021), 2021年12月07日, ［査読有り］
Deep Learning Based Power Optimizing for NOMA based Relay Aided D2D Transmissions
Zain Ali; Guftaar Ahmad Sardar Sidhu; Feifei Gao; Jing Jiang and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
IEEE Transactions on Cognitive Communications and Networking, 2021年09月01日, ［査読有り］
Multi-Timescale Multi-Dimension Resource Allocation for NOMA-Edge Computing-based Power IoT with Massive Connectivity
Haijun Yu; Zhenyu Zhou; Zehan Jia; Xiongwen Zhao; Lei Zhang and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
IEEE Transactions on Green Communications and Networking, 2021年09月01日, ［査読有り］
Learning-Based Intent-Aware Task Offloading for Air-Ground Integrated Vehicular Edge Computing
Haijun Liao; Zhenyu Zhou; Wenxuan Kong; Yapeng Chen; Xiaoyan Wang; Zhongyuan Wang and Sattam Otaibi, IEEE
IEEE Transactions on Intelligent Transportation Systems, 2021年08月01日, ［査読有り］
Better Platooning Toward Autonomous Driving: Inter-Vehicle Communications with Directional Antenna
Xiaoyan Wang; Diquan Wang; Nobuhiro Ariyasu and and Masahiro Umehira, 筆頭著者
China Communications, 2021年07月01日, ［査読有り］
A Novel Ghost Target Cancellation Scheme using Periodical Interference Sensing for Automotive Chirp Sequence Radar
Masahiro Umehira; Daiki Ammen; Yuu Watanabe; Xiaoyan Wang and Shigeki Takeda
International Radar Symposium (IRS 2021), 2021年06月21日, ［査読有り］
Learning-Based Queuing Delay-Aware Task Offloading in Collaborative Vehicular Networks
Zehan Jia; Zhenyu Zhou; Xiaoyan Wang and Shahid Mumtaz, IEEE
IEEE International Conference on Communications (IEEE ICC 2021), 2021年06月14日, ［査読有り］
Reinforcement Learning for Joint Channel/Subframe Selection of LTE in the Unlicensed Spectrum
Yuki Kishimoto; Xiaoyan Wang; and Masahiro Umehira, 責任著者, Wiley & Hindawi
Wireless Communications and Mobile Computing, 2021年06月02日, ［査読有り］
Antenna Element Space Interference Cancelling Radar for Angle Estimations of Multiple Targets
Ran Sun; Junichirou Sakai; Kouhei Suzuki; Jiaying Zheng; Shigeki Takeda; Masahiro Umehira; Xiaoyan Wang and Hiroshi Kuroda, IEEE
IEEE Access, 2021年05月11日, ［査読有り］
Neural Networks with Improved Extreme Learning Machine for Demand Prediction of Bike-sharing
Fan Wu; Si Hong; Wei Zhao; Xiaoyan Wang; Xun Shao; Xiujun Wang and Xiao Zheng, Springer
Mobile Networks and Applications (MONET), 2021年05月01日, ［査読有り］
FFT-based frequency domain filter design for multi-channel overlap-windowed-DFTs-OFDM signals
Motoki Ishibashi; Masahiro Umehira; Xiaoyan Wang and Shigeki Takeda, IEEE
IEEE Vehicular Technology Conference (IEEE VTC 2021-spring), 2021年04月25日, ［査読有り］
Reinforcement Learning based Joint Channel/Subframe Selection Scheme for Fair LTE-WiFi Coexistence
Yuki Kishimoto; Xiaoyan Wang and Masahiro Umehira, 責任著者, IEEE
International Conference on Mobility, Sensing and Networking (IEEE MSN 2020), 2020年12月17日, ［査読有り］
DPD based HPA linearizer using in-band operation point estimation pilot for mobile device applications
Akira Tada; Masahiro Umehira; Xiaoyan Wang and Shigeki Takeda, IEEE
Proc. of the International Conference on Signal Processing and Communication Systems (ICSPCS 2020), 2020年12月14日, ［査読有り］
A TORA-based Wireless Protocol for MANET with Low Routing Overhead at Link Layer
Han Biao; Li Ding; Yusheng Ji; Xiaoyan Wang and Baosheng Wang, IEEE
IEEE International Conference on Mobile Ad Hoc and Sensor Systems (IEEE MASS 2020), 2020年12月10日, ［査読有り］, ［招待有り］
Deep Reinforcement Learning based Access Control for Disaster Response Networks
Hang Zhou; Xiaoyan Wang; Masahiro Umehira; Xianfu Chen; Celimuge Wu; Yusheng Ji, 責任著者, IEEE
IEEE Global Communications Conference (IEEE Globecom 2020), 2020年12月07日, ［査読有り］
On Link Performance According to Flight Route in Drone-Based Wide Area Wireless Sensor Networks
Budi Rahmadya; Masahiro Umehira; Xiaoyan Wang and Shigeki Takeda, IEEE
Proc. of IEEE International Conference on Space-Air-Ground Computing (IEEE SAGC 2020), 2020年12月04日, ［査読有り］
When Vehicular Fog Computing Meets Autonomous Driving: Computational Resource Management and Task Offloading
Zhenyu Zhou; Haijun Liao; Xiaoyan Wang; Shahid Mumtaz; Jonathan Rodriguez, 責任著者, IEEE
IEEE Network, 2020年10月02日, ［査読有り］
An Adaptive Interference Detection and Suppression Scheme Using Iterative Processing for Automotive FMCW Radars
Masahiro Umehira; Takeo Okuda; Xiaoyan Wang; Shigeki Takeda; Hiroshi Kuroda, IEEE
Proc. of the IEEE Radar Conference, 2020年09月21日, ［査読有り］
A Ghost Target Suppression Technique Using Interference Replica for Automotive FMCW Radars
Daiki Ammen; Masahiro Umehira; Xiaoyan Wang; Shigeki Takeda; Hiroshi Kuroda, IEEE
Proc. of the IEEE Radar Conference, 2020年09月21日, ［査読有り］
Inter-radar Interference in Automotive FMCW Radars and Its Mitigation Challenges
Masahiro Umehira; Yuu Watabe; Xiaoyan Wang; Shigeki Takeda; Hiroshi Kuroda, IEEE
Proc. of IEEE International Symposium on Radio-Frequency Integration Technology (RFIT 2020), 2020年09月02日, ［査読有り］, ［招待有り］
ecUWB: A Energy-Centric Communication Scheme for Unstable WiFi Based Backscatter Systems
Yue Yu; Jingwen Zhang; Hao Zhou; Xiaoyan Wang; Zhi Liu; Yusheng Ji, IEEE
Proc. of the 29th International Conference on Computer Communications and Networks (ICCCN 2020), 2020年08月03日, ［査読有り］
Distributed Intelligence Empowered Data Aggregation and Distribution for Multi-robot Cooperative Communication
Li Ding; Biao Han; Xiaoyan Wang; Peng Li and Baosheng Wang, IEEE
Proc. of the IEEE Conference on Computer Communications (IEEE INFOCOM) Workshops on Data Driven Intelligence for Networks and Systems (DDINS), 2020年07月06日, ［査読有り］
An Efficient Privacy Preserving Spectrum Sharing Framework for Internet of Things
Xiaoyan Wang; Masahiro Umehira; Biao Han; Hao Zhou; Peng Li and Celimuge Wu, 筆頭著者, IEEE
IEEE ACCESS, 2020年03月01日, ［査読有り］
LoRa-Based Physical Layer Key Generation for Secure V2V/V2I Communications
Biao Han; Sirui Peng; Celimuge Wu; Xiaoyan Wang; and Baosheng Wang, 責任著者, MDPI
Sensors, 2020年01月01日, ［査読有り］
A VDTN scheme with enhanced buffer management
Zhaoyang Du; Celimuge Wu; Xianfu Chen; Xiaoyan Wang; Tsutomu Yoshinaga; Yusheng Ji
accepted by Wireless Networks, Springer, published online, 2020年01月, ［査読有り］
Audio Data Mining for Anthropogenic Disaster Identification: An Automatic Taxonomy Approach
Jiaxing Ye; Takumi Kobayashi; Xiaoyan Wang; Hiroshi Tsuda; Murakawa Masahiro, IEEE
IEEE Transactions on Emerging Topics in Computing (TETC), 2020年01月01日, ［査読有り］
Topology-Aware Job Scheduling for Machine Learning Cluster
Jingyuan Lu; Peng Li; Kun Wang; Huibin Feng; Enting Guo; Xiaoyan Wang and Song Guo, IEEE
Proc. of the IEEE Global Communications Conference (IEEE Globecom 2019), 2019年12月09日, ［査読有り］
Distributed Physical Layer Key Generation for Secure LPWAN Communication
Biao Han; Sirui Peng; Xiaoyan Wang and Baosheng Wang, IEEE
Proc. of the IEEE International Conference on Parallel and Distributed Systems (IEEE ICPADS 2019), 2019年12月04日, ［査読有り］
Numerical Link Outage Probability Assessment using Meteorological Data for THz-Band Outdoor Wireless Communication Systems
Yuki Hatahara; Masahiro Umehira; Xiaoyan Wang and Shigeki Takeda, IEEE
Proc. of the International Symposium on Wireless Personal Multimedia Communications (WPMC 2019), 2019年11月24日, ［査読有り］
Crowdsourced Radio Environment Mapping by Exploiting Machine Learning
Mina Akimoto; Xiaoyan Wang; Masahiro Umehira and Yusheng Ji, 責任著者, IEEE
Proc. of the International Symposium on Wireless Personal Multimedia Communications (WPMC 2019), 2019年11月24日, ［査読有り］
Inter-radar Interference Analysis of FMCW radars with Different Chirp Rates
Yuya Makino; Takuya Nozawa; Masahiro Umehira; Xiaoyan Wang; Shigeki Takeda and Hiroshi Kuroda, IET
Journal of Engineering, 2019年10月15日, ［査読有り］
A Proposal of Multiple Access FMCW Radar for Inter-radar Interference Avoidance
Mikihiro Kurosawa; Takuya Nozawa; Masahiro Umehira; Xiaoyan Wang; Shigeki Takeda and Hiroshi Kuroda, IET
Journal of Engineering, 2019年10月15日, ［査読有り］
Packet-based FMCW Radar using CSMA Technique to Avoid Narrowband Interference
Shintaro Ishikawa; Mikihiro Kurosawa; Masahiro Umehira; Xiaoyan Wang; Shigeki Takeda and Hiroshi Kuroda, IET
Proc. of the IET International Conference on Radar Systems (RADAR 2019), 2019年09月23日, ［査読有り］
Prototype Development and Experimental Performance Evaluation of FMCW Radar Using Iterative Interference Suppression Technique
Takeo Okuda; Yuya Makino; Masahiro Umehira; Xiaoyan Wang; Shigeki Takeda and Hiroshi Kuroda, IET
Proc. of the IET International Conference on Radar Systems (RADAR 2019), 2019年09月23日, ［査読有り］
An Overlap-Windowed-OQAM-DFTs-OFDM Scheme to Achieve Low PAPR and ACLP
Mizuki Imai; Takuya Okamoto; Takahiro Okano; Masahiro Umehira and Xiaoyan Wang, IEEE
Proc. of the International Symposium on Wireless Communication Systems (ISWCS 2019), 2019年08月27日, ［査読有り］
Dynamic Controller-Switch Mapping Assignment with Genetic Algorithm for Multi-Controller SDN
Biao Han; Xiangrui Yang and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
Proc. of the IEEE International Conference on Cloud and Big Data Computing (IEEE CBDCom 2019), 2019年08月05日, ［査読有り］
Inter-Radar Interference Analysis and Concept of Scalable Fast Chirp FMCW Radar for Automotive Applications
Masahiro Umehira; Yuya Makino; Takeo Okuda; Xiaoyan Wang; Shigeki Takeda and Hiroshi Kuroda, DGON
Proc. of the International Radar Symposium (IRS 2019), 2019年06月26日, ［査読有り］
Joint Workload Scheduling and Energy Management for Green Data Centers Powered by Fuel Cells
Xiaoxuan Hu; Peng Li; Kun Wang; Yanfei Sun; Deze Zeng; Xiaoyan Wang and Song Guo, IEEE
IEEE Transactions on Green Communications and Networking, 2019年06月01日, ［査読有り］
Online Incentive Mechanism for Crowdsourced Radio Environment Map Construction
Xiaoyan Wang; Masahiro Umehira; Biao Han; Peng Li; Yu Gu and Celimuge Wu, 筆頭著者, IEEE
Proc. of the IEEE International Conference on Communications (IEEE ICC 2019), 2019年05月20日, ［査読有り］
Your WiFi Knows You Fall: A Channel Data-driven Device-free Fall Sensing System
Mengmeng Huang; Jun Liu; Yu Gu; Yifan Zhang; Fuji Ren; Xiaoyan Wang and Jie Li, IEEE
Proc. of the IEEE International Conference on Communications (IEEE ICC 2019), 2019年05月20日, ［査読有り］
Investigating and Revealing Privacy Leaks in Mobile Application Traffic
Shuhui Chen; Shuang Zhao; Biao Han and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
Proc. of the Wireless Days 2019, 2019年04月24日, ［査読有り］
QoE-Oriented Rate Control and Resource Allocation for Cognitive M2M Communication in Spectrum-Sharing OFDM Networks
Junjie Yin; Yapeng Chen; Gan Sang; Bin Liao and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
IEEE Access, 2019年04月01日, ［査読有り］
Exploiting Packet-level Parallelism of Packet Parsing for FPGA-based Switches
Junnan Li; Biao Han; Zhigang Sun; Tao Li and Xiaoyan Wang, ラスト(シニア)オーサー, IEICE
IEICE Transactions on Communications, 2019年03月01日, ［査読有り］
Intelligent Post-Disaster Networking by Exploiting Crowd Big Data
Xiaoyan Wang; Fangzhou Jiang; Lei Zhong; Yusheng Ji; Shigeki Yamada; Kiyoshi Takano; Guoliang Xue
accepted by IEEE Network Magazine, 2019年, ［査読有り］
Decentralized Trust Evaluation in Vehicular Internet of Things
Siri Guleng; Celimuge Wu; Xianfu Chen; Xiaoyan Wang; Tsutomu Yoshinaga and Yusheng Ji, IEEE
IEEE Access, 2019年01月01日, ［査読有り］
Feasibility of HPA Linearization System using Amplitude Reference Pilot Signal
Takuya Okamoto; Masahiro Umehira; Xiaoyan Wang and Shigeki Takeda, IEEE
Proc. of the Asia-Pacific Conference on Communications (APCC 2018), 2018年11月12日, ［査読有り］
Learning-based Radio Environment Mapping
Mina Akimoto; Xiaoyan Wang and Masahiro Umehira, 責任著者, IEICE
Proc. of the International Workshop on Smart Wireless Communications (Smartcom 2018), 2018年10月30日
Beaconing control for platoon safety: a braking scenario
Nobuhiro Ariyasu; Xiaoyan Wang and Masahiro Umehira, 責任著者, IEICE
Proc. of the International Workshop on Smart Wireless Communications (Smartcom 2018), 2018年10月30日
A Routing Protocol Considering Turning Behavior of Vehicles In VANETs
Hiroya Matsumoto; Bo Gu; Xiaoyan Wang and Osamu Mizuno, IEEE
Proc. of the IEEE International Conference on Cloud and Big Data Computing (CBDCom 2018), 2018年10月08日, ［査読有り］
A Novel Intrusion Detection Method Combining Unsupervised and Supervised learning
Hanwen Wang; Biao Han; Jinshu Su and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
Proc. of the IEEE International Conference on Cloud and Big Data Computing (CBDCom 2018), 2018年10月08日, ［査読有り］
Resource allocation for SVC streaming over cooperative vehicular networks
Hao Zhou; Xiaoyan Wang; Zhi Liu; Yusheng Ji and Shigeki Yamada, 責任著者, IEEE
IEEE Transactions on Vehicular Technology, 2018年09月01日, ［査読有り］
Energy Efficient Learning-based 60GHz Band Coverage Prediction for Multi-band WLAN
Xiaoyan Wang; Masahiro Umehira; Shigeki Takeda; Hiroyuki Otsu and Takyuya Kawatani, 筆頭著者, IEEE
Proc. of the IEEE Vehicular Technology Conference (IEEE VTC 2018-fall), 2018年08月27日, ［査読有り］
Overlap-Windowed-DFTs-OFDM with Overlap FFT Filter-Bank for Flexible Uplink Access in 5G and Beyond
Takahiro Okano; Masahiro Umehira; Xiaoyan Wang and Shigeki Takeda, IEEE
Proc. of the IEEE Vehicular Technology Conference (IEEE VTC 2018-fall), 2018年08月27日, ［査読有り］
Big Data Privacy Preserving in Multi-Access Edge Computing for Heterogeneous Internet of Things
Miao Du; Kun Wang; Yuanfang Chen; Xiaoyan Wang and Yanfei Sun, IEEE
IEEE Communications Magazine, 2018年08月01日, ［査読有り］
A Novel Iterative Inter-Radar Interference Reduction Scheme for Densely Deployed Automotive FMCW Radars
Masahiro Umehira; Takuya Nozawa; Yuya Makino; Xiaoyan Wang; Shigeki Takeda and Hiroshi Kuroda, DGON
Proc. of the International Radar Symposium (IRS 2018), 2018年06月20日, ［査読有り］
Energy Efficient Learning-based Indoor Multi-band WLAN for Smart Buildings
Xiaoyan Wang; Masahiro Umehira; Hiroyuki Otsu; Takyuya Kawatani and Shigeki Takeda, 筆頭著者, IEEE
IEEE ACCESS, 2018年06月15日, ［査読有り］
EmoSense: Data-driven Emotion Sensing via Off-the-shelf WiFi Devices
Yu Gu; Tao Lin; Jie Li; Fuji Ren; Zhi Liu; Xiaoyan Wang and Peng Li, IEEE
Proc. of the IEEE International Conference on Communications (IEEE ICC 2018), 2018年05月20日, ［査読有り］
Optimal Pricing Strategy for Resource Allocation in 5G Heterogeneous Cellular Networks
Xinyi Chen; Yapeng Chen; Wenlin Liao; Zhenyu Zhou; Bo Gu and Xiaoyan Wang, ラスト(シニア)オーサー, Wiley
Transactions on Emerging Telecommunications Technologies, 2018年05月01日, ［査読有り］
Sleepy: Adaptive Sleep Monitoring from Afar with Commodity WiFi Infrastructures
Yu Gu; Jinhai Zhan; Zhi Liu; Jie Li; Yusheng Ji and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
Proc. of the IEEE Wireless Communications and Networking Conference (IEEE WCNC 2018), 2018年04月18日, ［査読有り］
Litenet: Lightweight neural network for detecting arrhythmias at resource-constrained mobile devicesZiyang He; Xiaoqing Zhang; Yangjie Cao; Zhi Liu; Bo Zhang; Xiaoyan Wang, By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database
the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices., MDPI AG
Sensors (Switzerland), 2018年04月17日,
［査読有り］ LiteNet: Lite-Weighted Neural Network for Resource Constrained Network Edges to Detect Arrhythmias
Ziyang He; Xiaoqing Zhang; Yangjie Cao; Zhi Liu; Bo Zhang; Xiaoyan Wang
Sensors, 2018年04月, ［査読有り］
Online Internet traffic monitoring system using spark streaming
Baojun Zhou; Jie Li; Xiaoyan Wang; Yu Gu; Li Xu; Yongqiang Hu and Lihua Zhu, IEEE
Big Data Mining and Analytics, 2018年03月01日, ［査読有り］
Incentivizing Crowdsourcing for Exclusion Zone Refinement in Spectrum Sharing System
Xiaoyan Wang; Masahiro Umehira; Peng Li; Yu Gu and Yusheng Ji, 筆頭著者, IEEE
Proc. of the Asia-Pacific Conference on Communications (APCC 2017), 2017年12月13日, ［査読有り］
Kriging-based RSSI Prediction for Cell Coverage Discovery using Spectrum Database in 5G Multi-band Cellular Networks
Yuto Ogawa; Masahiro Umehira and Xiaoyan Wang, IEEE
Proc. of the Asia-Pacific Conference on Communications (APCC 2017), 2017年12月13日, ［査読有り］
Population-Aware Relay Placement for Wireless Multi-Hop Based Network Disaster Recovery
Lei Zhong; Yusheng Ji; Xiaoyan Wang; Shigeki Yamada; Kiyoshi Takano and Guoliang Xue, IEEE
Proc. of the IEEE Global Communications Conference (IEEE Globecom 2017), 2017年12月08日, ［査読有り］
Fine-grained Incentive Mechanism for Sensing Augmented Spectrum Database
Xiaoyan Wang; Masahiro Umehira; Peng Li; Yu Gu and Yusheng Ji, 筆頭著者, IEEE
Proc. of the IEEE Global Communications Conference (IEEE Globecom 2017), 2017年12月08日, ［査読有り］
Measuring QoE of Web Service with Mining DNS Resolution Data
Yongsheng Liu; Yu Gu; Xiangjiang Wen; Xiaoyan Wang and Yufei Fu, ZTE
ZTE Communications, 2017年12月01日, ［査読有り］
Pilot-based reference amplitude compensation for ultra-multi-level PAM-SSB-DFTs-OFDMTomoya Suzuki; Hirokazu Fusayasu; Masahiro Umehira; Shigeki Takeda; Xiaoyan Wang, Ultra-multi-level PAM-SSB-DFTs-OFDM (Single Side Band DFT spreading Orthogonal Frequency Division Multiplexing) was proposed as one of the attractive approaches to achieve higher spectrum efficiency since it has advantages of lower PAPR (Peak to Average Power Ratio) than OFDM and robustness against reference carrier phase error. However, BER performance degradation caused by reference amplitude error is as large as that of conventional QAM signals, especially for ultra-multi-level modulation. To solve this problem, this paper proposes pilot-based reference amplitude compensation to improve BER performance degradation caused by reference amplitude error. This paper describes pilot signal design and BER performance comparison evaluation when non-linear amplifier is used., Institute of Electrical and Electronics Engineers Inc.
2017 4th NAFOSTED Conference on Information and Computer Science, NICS 2017 - Proceedings, 2017年11月14日,
［査読有り］ An Anti-collision Automotive FMCW Radar Using Time-domain Interference Detection and Suppression
Takuya Nozawa; Yuya Makino; Nobuyuki Takaya; Masahiro Umehira; Shigeki Takeda; Xiaoyan Wang and Hiroshi Kuroda, IET
Proc. of the IET International Conference on Radar Systems (RADAR 2017), 2017年10月26日, ［査読有り］
Silence is Golden: Exploring Ambient Signals for Detecting Motions in a Real-time Manner
Yu Gu; Jinhai Zhan; Fuji Ren and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
Proc. of the IEEE Vehicular Technology Conference (IEEE VTC 2017-fall), 2017年09月27日, ［査読有り］
Activity Recognition via Channel Response: From Theoretical Analysis to Real-world Experiments
Yu Gu; Jianwen Tian; Liwen Zhang; Zhi Liu; Fuji Ren and Xiaoyan Wang, ラスト(シニア)オーサー, IEEE
Proc. of the IEEE Vehicular Technology Conference (IEEE VTC 2017-spring), 2017年06月07日, ［査読有り］
Auction-Based Frameworks for Secure Communications in Static and Dynamic Cognitive Radio NetworksXiaoyan Wang; Yusheng Ji; Hao Zhou; Jie Li, This paper investigates the secure communication issue for both static and dynamic cognitive radio networks (CRNs), where multiple nonaltruistic primary users (PUs), secondary users (SUs), and eavesdroppers exist. The design objective is to provide secure communications for PUs and, meanwhile, to ease the starvation of transmission opportunities for SUs. To achieve this goal, we propose a barter-like trading model to incentivize the cooperation among nonaltruistic users. Specifically, PUs leverage the assistance of SUs in the form of cooperative relaying or friendly jamming and, in return, yield certain licensed spectrum accessing time to the aided SUs. We propose a truthful nonmonetary double auction framework (FONDA) toward secure communications for static CRN where PUs and SUs interact in a single round. Then, we extend our framework to d-FONDA for dynamic CRN, where SUs who have patience (tolerant of traffic delay) arrive and leave the network dynamically. We prove that both FONDA and d-FONDA preserve nice economic properties, including truthfulness, individual rationality, and budget balance. Simulation results reveal that the proposed frameworks provide substantial performance gains compared with the baseline scheme and suffer acceptable performance degradation over ideal schemes., Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Vehicular Technology, 2017年03月01日,
［査読有り］ User-oriented intercell interference coordination in heterogeneous networks (HetNets)Zhi Liu; Mianxiong Dong; Hao Zhou; Xiaoyan Wang; Yusheng Ji; Yoshiaki Tanaka, HetNet is a hot research topic in the next generation broadband wireless access network and mitigating the intercell interference could improve the system throughput. Users care whether their requested data rates can be satisfied or not the most. Hence a user-centric intercell interference coordination scheme (i.e. resource allocation scheme considering user request) is necessary. In this paper, at each specific subframe, when users have data requests, the corresponding base station first selects which user to serve based on each user's 'instant data rate', data rate request and capacity gained. Then given the users selected, a method is proposed to help choose which intercell interference coordination scheme to use in order to maximize the users' data rate satisfaction ratios. Intensive simulations are conducted and the results demonstrate that the proposed scheme achieves considerable gains over competing schemes in terms of the data rate satisfaction ratio and system capacity in Config.4b scenarios defined by 3GPP., IGI Global
Resource Allocation in Next-Generation Broadband Wireless Access Networks, 2017年02月14日,
［査読有り］ EICIC Configuration Algorithm with Service Scalability in Heterogeneous Cellular NetworksHao Zhou; Yusheng Ji; Xiaoyan Wang; Shigeki Yamada, Interference management is one of the most important issues in heterogeneous cellular networks with multiple macro and pico cells. The enhanced inter cell interference coordination (eICIC) has been proposed to protect downlink pico cell transmissions by mitigating interference from neighboring macro cells. Therefore, the adaptive eICIC configuration problem is critical, which adjusts the parameters including the ratio of almost blank subframes (ABS) and the bias of cell range expansion (RE). This problem is challenging especially for the scenario with multiple coexisting network services, since different services have different user scheduling strategies and different evaluation metrics. By using a general service model, we formulate the eICIC configuration problem with multiple coexisting services as a general form consensus problem with regularization and solve the problem by proposing an efficient optimization algorithm based on the alternating direction method of multipliers. In particular, we perform local RE bias adaptation at service layer, local ABS ratio adaptation at BS layer, and coordination among local solutions for a global solution at a network layer. To provide the service scalability, we encapsulate the service details into the local RE bias adaptation subproblem, which is isolated from the other parts of the algorithm, and we also introduce some implementation examples of the subproblem for different services. The extensive simulation results demonstrate the efficiency of the proposed algorithm and verify the convergence property., Institute of Electrical and Electronics Engineers Inc.
IEEE/ACM Transactions on Networking, 2017年02月01日,
［査読有り］ A Nonmonetary QoS-Aware Auction Framework Toward Secure Communications for Cognitive Radio NetworksXiaoyan Wang; Yusheng Ji; Hao Zhou; Jie Li, This paper investigates the secure communication issue for cognitive radio networks with nonaltruistic users. The design objective is to improve the secrecy rate of the primary user (PU) and, at the same time, create transmission opportunities for the secondary users (SUs) to satisfy their diversified quality-of-service (QoS) demands. To achieve this goal, we propose a novel nonmonetary trading model, where the users are incentivized to participate in the market by a barter-like resource-to-resource exchange. Specifically, the PU leverages the assistance of the SU in the form of cooperative forwarding or friendly jamming and yields part of the spectrum accessing time to the aided SU. The proposed spectrum auction framework jointly formulates the optimal cooperator selection and the corresponding resource allocation problems by taking into consideration the QoS demands of individual users. The proposed framework ensures that bidding truthfully is the dominant strategy for all bidders and, thus, is invulnerable to market manipulation and eliminates the overhead of strategizing over other bidders. Simulation results reveal that the proposed framework could provide substantial gains for both the PU and the aided SU., Institute of Electrical and Electronics Engineers Inc.
IEEE Transactions on Vehicular Technology, 2016年07月01日,
［査読有り］ Device-to-device Assisted Video Frame Recovery for Picocell Edge Users in Heterogeneous NetworksZhi Liu; Mianxiong Dong; Hao Zhou; Xiaoyan Wang; Yusheng Ji; Yoshiaki Tanaka, Heterogeneous networks (HetNets) are intended to offer wide area coverage and high data rate transmission by deploying small cells besides macrocells. Device-to-device (D2D) communication as an underlay of cellular network enriches local service and offloads base station. In this paper, we target the video transmission demanded by picocell edge users (PEUEs), who suffer from low quality channel due to the inter-cell interference from macrocell and long physical distance from picocell. Moreover, the wireless channels are burst-loss prone for upper layer applications such as video on demand (VoD), which makes the traditional channel coding such as forward error correction (FEC) insufficient. In this paper, we address these issues and propose a cooperative video transmission scheme to improve PEUEs' received video quality by constructing two transmission paths from picocell to each PEUE. The two transmission paths are the direct transmission from pico-eNB (i.e., base station) to PEUE and a relay-assisted path by means of D2D communication for frame recovery, respectively. Reference frame selection and unequal error protection are adopted to further improve the overall performance. Extensive simulations are conducted and results demonstrate that the proposed scheme outperforms state-of-the-art scheme in Config. 4b scenarios defined by 3GPP., IEEE
2016 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2016年,
［査読有り］ Fingerprint in the Air: Using the RSS Data for Uniqueness IdentificationQiyue Li; Hailong Fan; Wei Sun; Jie Li; Xiaoyan Wang; Zhi Liu, Indoor localization, device identification, and wireless attendance security systems are widely used in recent years. There is always a premise that each person can carry only one wireless device by himself, which is no longer valid nowadays. To detect the uniqueness identification problem, the bio-assisted methods such as fingerprint, face or gait recognition systems are deployed near the entrance which are difficult to use. This paper studies such problem using RF RSS fingerprints which can be collected and modeled as time series. Then we can calculate the similarity of the time series to judge the uniqueness identification problem. Firstly, a naive algorithm using dynamic time warping is presented to simply compute the similarity the asynchronous time series. Then an improved algorithm is proposed to reduce the computational complexity while keeping the robustness. Simulation and experiments results show that our algorithms can perfectly detect the uniqueness identification problem with a reasonable cost., IEEE
PROCEEDINGS OF 45TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING WORKSHOPS (ICPPW 2016), 2016年,
［査読有り］ Large Scale Environmental Sound Classification based on Efficient Feature ExtractionXiaoyan Wang; Hao Zhou; Zhi Liu; Yu Gu, In recent years, plenty of studies endeavor to analyze the life auditory scenarios via mining non-speech sounds. Conventional audio recognition schemes clearly bound the feature extraction and recognition stages, such as in speech recognition. However, such separation leads to inconsistency in the purposes at each stage. The recognition stage contributes to portray the global data distribution focusing on "relationship" between signal samples. However, such consideration can hardly be embedded into feature extraction process which centered on the local structure; thus, the prominent "relation" information is destroyed. In this paper, we propose a unified acoustic recognition framework taking advantage of primitive feature input without injuring discriminant information and adopting effective classification scheme accordingly. We formulate the sound into subspace representation and initially adopt Grassmannian distance to classify the subspace-indexed non-speech sounds. To validate the proposed framework, we conducted experiments using RWCP Sound Scene Database. The experimental results demonstrated the proposed framework achieved fine recognition performance with high efficiency., IEEE
PROCEEDINGS OF 45TH INTERNATIONAL CONFERENCE ON PARALLEL PROCESSING WORKSHOPS (ICPPW 2016), 2016年,
［査読有り］ Capacity-aware Cost-efficient Network Reconstruction for Post-Disaster ScenarioXiaoyan Wang; Hao Zhou; Lei Zhong; Yusheng Ji; Kiyoshi Takano; Shigeki Yamada; Guoliang Xue, Natural disasters can result in severe damage to communication infrastructure, which leads to further chaos to the damaged area. After the disaster strikes, most of the victims would gather at the evacuation sites for food supplies and other necessities. Having a good communication network is very important to help the victims. In this paper, we aim at recovering the network from the still-alive mobile base stations to the out-of-service evacuation sites by using multi-hop relaying technique. We propose to reconstruct the post-disaster network in a capacity-aware way based on prize collecting Steiner tree. The purpose of the proposed scheme is to achieve high capacity connectivity ratio in a cost efficient way. To provide more accurate evaluation results, we evaluate the proposed scheme by using the real evacuation site and base station data in Tokyo area, and utilizing the big data analysis based post-disaster service availability model., IEEE
2016 IEEE 27TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR, AND MOBILE RADIO COMMUNICATIONS (PIMRC), 2016年,
［査読有り］ QoS-aware resource allocation for multicast service over vehicular networksHao Zhou; Xiaoyan Wang; Zhi Liu; Xiaoming Zhao; Yusheng Ji; Shigeki Yamada, Quality of service (QoS) constrained multicast service over vehicular networks has considerable benefit for both road safety and entertainment, while the resource allocation problem of it is challenging due to the high mobility of vehicles. In this paper, we manage the vehicle mobility by dividing one scheduling round into multiple segments, and investigate the resource allocation problem to answer the questions of which modulation and coding scheme (MCS) should be adopted for each flow in each segment, and how to schedule the radio resources among all the flows. We consider two kinds of multicast services. For the multicast service to cover all the recipients, we formulate the problem as a resource scheduling problem with fixed MCS profile, and propose a k-commodity packing based approximation algorithm to solve it with low complexity. For the multicast service with adaptive recipients, we notice that the number of valid MCS profiles for each flow is limited, and propose a heuristic algorithm to search for proper MCS profile assignments. The simulation results verify the efficiency of the proposed scheme., IEEE
2016 8TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS & SIGNAL PROCESSING (WCSP), 2016年,
［査読有り］ Spatio-Temporal Data-Driven Analysis of Mobile Network Availability During Natural DisastersLei Zhong; Kiyoshi Takano; Fangzhou Jiang; Xiaoyan Wang; Yusheng Ji; Shigeki Yamada, The accurate assessment of mobile network availability during large-scale natural disasters is essential for ensuring effective preparation and fast response. However, traditional network availability assessment models are ideal and cannot effectively take into account the spatio-temporal dynamics of mobile network failures in a disaster scenario. Therefore, their evaluation results are generally inaccurate and of coarse granularity, thus not meeting the strict requirements for disaster preparation and response. In this paper, we propose a data-driven analysis framework for the accurate assessment of mobile network availability by integrating essential geographical features from various sources, e.g., seismic intensity data, buildings and land usage data, base station location data, and many other data in related studies. Furthermore, we explore the spatio-temporal inter-correlations and dynamics of several key factors of network failures and their impacts on network availability by associating them with corresponding geographical features in a disaster scenario. We demonstrate our analysis framework with a synthetic earthquake scenario in the Tokyo area and validate our analysis by comparing to existing studies., IEEE
PROCEEDINGS OF THE 2016 3RD INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES FOR DISASTER MANAGEMENT (ICT-DM), 2016年,
［査読有り］ Joint Resource Allocation and User Association for SVC Multicast Over Heterogeneous Cellular NetworksHao Zhou; Yusheng Ji; Xiaoyan Wang; Baohua Zhao, Scalable video coding (SVC) is attractive technology for multicasting video to users with different available transmission capacities. In this paper, we investigate the joint optimization of resource allocation and user association problems for SVC multicast over heterogeous cellular networks (HetNet) employing the schemes of cell range expansion (RE) and almost blank subframe (ABS). We solve the joint optimization problem by decoupling it into two problems, namely, resource allocation (RA) subproblem and user association (UA) master problem. For the RA subproblem, we propose a dynamic programming based algorithm to optimally set the transmission profile. For the UA master problem, we propose a similarity-based negotiation protocol (SBNP) based algorithm to obtain the Pareto-optimal range expansion bias. The simulation results demonstrate the efficiency of these algorithms., IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2015年07月,
［査読有り］ Incentivize Spectrum Leasing in Cognitive Radio Networks by Exploiting Cooperative ARQ Retransmission
Xiaoyan Wang; Yusheng Ji; Hao Zhou; Zhi Liu and Jie Li, 筆頭著者, EAI
EAI Transactions on Wireless Spectrum, 2015年07月01日, ［査読有り］
An Instance Learning based Intrusion Detection System for WSNs
Shuai Fu; Xiaoyan Wang; and Jie Li, 責任著者, ZTE
ZTE Communications, 2015年06月01日, ［査読有り］
Improving the Network Lifetime of MANETs through Cooperative MAC Protocol DesignXiaoyan Wang; Jie Li, Cooperative communication, which utilizes nearby terminals to relay the overhearing information to achieve the diversity gains, has a great potential to improve the transmitting efficiency in wireless networks. To deal with the complicated medium access interactions induced by relaying and leverage the benefits of such cooperation, an efficient Cooperative Medium Access Control (CMAC) protocol is needed. In this paper, we propose a novel cross-layer distributed energy-adaptive location-based CMAC protocol, namely DEL-CMAC, for Mobile Ad-hoc NETworks (MANETs). The design objective of DEL-CMAC is to improve the performance of the MANETs in terms of network lifetime and energy efficiency. A practical energy consumption model is utilized in this paper, which takes the energy consumption on both transceiver circuitry and transmit amplifier into account. A distributed utility-based best relay selection strategy is incorporated, which selects the best relay based on location information and residual energy. Furthermore, with the purpose of enhancing the spatial reuse, an innovative network allocation vector setting is provided to deal with the varying transmitting power of the source and relay terminals. We show that the proposed DEL-CMAC significantly prolongs the network lifetime under various circumstances even for high circuitry energy consumption cases by comprehensive simulation study., IEEE COMPUTER SOC
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2015年04月,
［査読有り］ Location Privacy Protecting based on Anonymous Technology in Wireless Sensor NetworksXiaoyan Wang; Lu Dong; Chao Xu; Peng Li, Wireless sensor network is a type of information sharing network, where the attacker can monitor the network traffic or trace the transmission of packets to infer the position of the target node. Particularly, the target node mainly refers to the source node and the aggregation node. Firstly, we discuss the privacy protection method which is based on the anonymous location to prevent from the location privacy problems. Then, we suggest at least n anonymous nodes distributing near the target node, and select one of the fake nodes by routing protocol to replace the real one to carry out the location of the data communication. Finally, in order to improve the security of nodes and increase the difficulty of the attacker tracking, we select the routing tree which is generated via Collection Tree Protocol (CTP) to build the anonymous group and verified by simulation. Experiments show that anonymity of the proposed treatment increases the difficulty of the attackers significantly., IEEE
2015 SEVENTH INTERNATIONAL SYMPOSIUM ON PARALLEL ARCHITECTURES, ALGORITHMS AND PROGRAMMING (PAAP), 2015年,
［査読有り］ ADMM based algorithm for eICIC configuration in heterogeneous cellular networksHao Zhou; Yusheng Ji; Xiaoyan Wang; Baohua Zhao, Interference management is one of the most important issues in the heterogeneous cellular networks (HetNet) with macro and pico cells. The enhanced inter cell interference coordination (eICIC) has been proposed to protect downlink pico cell transmissions by mitigating interference from neighboring macro cells. The adaptive eICIC configuration problem is studied in this paper to adjust the parameters including the ratio of Almost Blank Subframes (ABS) and the bias of cell range expansion (RE). We formulate the problem as a general form consensus problem with regularization, and solve the problem by providing an efficient distributed optimization framework. Our algorithm is based on the alternating direction method of multipliers (ADMM) in which the solutions to local subproblems on each macro cell and pico cell are coordinated to find a solution to the global problem for the whole network. We also propose the dynamic programming based algorithms to solve the local subproblems on macro cell or pico cell. The simulation results demonstrate the efficiency of the proposed algorithm compared with existing approaches, and verify the convergence properties of the proposed algorithm., IEEE
2015 IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (INFOCOM), 2015年,
［査読有り］ Cooperative ARQ Retransmission based Spectrum Leasing for Cognitive Radio NetworksXiaoyan Wang; Yusheng Ji; Jie Li, This paper addresses the spectrum leasing issue in cognitive radio networks by exploiting the primary user's cooperative ARQ (automatic repeated-request). To incentivize the otherwise non-cooperative users, we propose a novel trading model to foster the cooperation in the context of cooperative retransmitting. By formulating the network as a Stackelberg game, we maximize the utilities of both primary and secondary users in terms of transmission rates and revenues. We analyze the existence of the unique Nash equilibrium of the game, and give the optimal solutions with corresponding constraints. Numerical results demonstrate the efficiency of the proposed framework, under which the performance of the whole system could be substantially improved., IEEE
2015 IEEE 81ST VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING), 2015年,
［査読有り］ DASI: A Truthful Double Auction Mechanism for Secure Information Transfer in Cognitive Radio NetworksXiaoyan Wang; Yusheng Ji; Hao Zhou; Jie Li, This paper investigates the secure information transfer issue for cognitive radio networks that have multiple non-altruistic primary users, secondary users and eavesdroppers. The design objective is to improve the secrecy rates of the primary users, and create the transmission opportunities for the secondary users. To achieve this goal, we propose to incentivize the non-altruistic users to cooperate by a barter-like exchange. Specifically, the primary users leverage the assist of the secondary users in the form of cooperative transmitting or friendly jamming, and in return, yield certain licensed spectrum accessing time to the aided secondary users. We propose a truthful Double Auction mechanism for Secure Information transfer in cognitive radio networks, namely DASI, to jointly formulate the cooperator/jammer assignment and the corresponding resource allocation problems. We prove that DASI preserves nice economic properties that are critical for the auction design, including truthfulness, individual rationality and budget balance. We also evaluate DASI in terms of aggregated throughput and spectrum utilization ratio by simulations., IEEE
2015 12TH ANNUAL IEEE INTERNATIONAL CONFERENCE ON SENSING, COMMUNICATION, AND NETWORKING (SECON), 2015年,
［査読有り］ Resource allocation for scalable video streaming in highway VANETYang Xu; Hao Zhou; Xiaoyan Wang; Baohua Zhao, In vehicular ad-hoc network (VANET), the popular video transmission service can serve people with live traffic information, real-time news or even high-definition movies, which can assist people to drive on the road and enhance their driving experience. In this paper, we propose a dynamic programming based algorithm for the resource allocation problem of scalable video streaming through long term evolution (LTE) networks in highway VANET. We manage vehicular mobility by using small group of pictures (GOP) sized scalable video coding (SVC) encoder and valid signal-to-interference-and-noise ratio (SINR) estimator. We also adopt adaptive receive window of GOP to enhance the resource utilization efficiency as well as video quality. The simulation results demonstrate the efficiency of the proposed algorithm., IEEE
2015 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS & SIGNAL PROCESSING (WCSP), 2015年,
［査読有り］ A Privacy Preserving Truthful Spectrum Auction Scheme using Homomorphic EncryptionXiaoyan Wang; Yusheng Ji; Hao Zhou; Zhi Liu; Yu Gu; Jie Li, Dynamic spectrum reallocation, under which the spectrum owners temporarily share the underutilized spectrum to secondary users for economic profit, is an important approach to improve the spectrum utilization ratio. Auction is believed to be a natural marketing tool to incentivize the spectrum owners, and thus redistribute the idle spectrum efficiently. Extensive researches have been done in the problem of truthful spectrum auction, in which the bidders bid based on their true valuations of the spectrum. The true valuation of the individual bidder, however, is a private information which should be protected against exposure. In this paper, we propose a privacy preserving truthful spectrum auction scheme by utilizing homomorphic encryption. The proposed scheme reveals the group bids but hides the users' bids even from the auctioneer. The evaluation results show that the proposed scheme achieves good spectrum utilization efficiency with low communication and computation overheads., IEEE
2015 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2015年,
［査読有り］ Joint Spectrum Sharing and ABS Adaptation for Network Virtualization in Heterogeneous Cellular NetworksHao Zhou; Yusheng Ji; Xiaoyan Wang; Shigeki Yamada, Network virtualization (NV) is a promising solution for higher resource utilization, improved system performance, and lower investment capitals for network operators. Spectrum sharing is an important issue for NV in the wireless networks. Meanwhile, the scheme of Almost Blank Subframe (ABS) causes new challenge for NV in the heterogeneous cellular networks (HetNet). This paper aims at investigating the joint optimization problem of spectrum sharing and ABS adaptation, and the optimization target is represented through general utility functions of logical virtual operators (LVOs). We formulate the problem, and decouple it into two subproblems. We propose a dynamic programming based algorithm for the spectrum sharing subproblem, and an alternating direction method of multipliers (ADMM) based algorithm for the ABS adaptation subproblem. The simulation results demonstrate the efficiency of the proposed algorithm., IEEE
2015 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2015年,
［査読有り］ Network Coding Aware Cooperative MAC Protocol for Wireless Ad Hoc NetworksXiaoyan Wang; Jie Li; Feilong Tang, Cooperative communication, which utilizes neighboring nodes to relay the overhearing information, has been employed as an effective technique to deal with the channel fading and to improve the network performances. Network coding, which combines several packets together for transmission, is very helpful to reduce the redundancy at the network and to increase the overall throughput. Introducing network coding into the cooperative retransmission process enables the relay node to assist other nodes while serving its own traffic simultaneously. To leverage the benefits brought by both of them, an efficient Medium Access Control (MAC) protocol is needed. In this paper, we propose a novel network coding aware cooperative MAC protocol, namely NCAC-MAC, for wireless ad hoc networks. The design objective of NCAC-MAC is to increase the throughput and reduce the delay. Simulation results reveal that NCAC-MAC can improve the network performance under general circumstances comparing with two benchmarks., IEEE COMPUTER SOC
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2014年01月,
［査読有り］ Cooperative Coding Based Retransmission Protocol for Cognitive Radio Networks by Exploiting Hybrid ARQXiaoyan Wang; Yusheng Ji; Jie Li, This paper deals with the retransmission protocol design for cognitive radio networks by exploiting the primary hybrid ARQ. In contrast with previous work that focuses on cancellation based retransmissions, we propose a novel cooperative coding based retransmission protocol for cognitive radio networks. The design objective is to improve the throughput of the primary user and create the transmission opportunity for the secondary user. By exploiting the primary retransmission appropriately, the knowledge on primary packet which is required by the cooperative coded retransmission can be obtained without any non-causal assumption. Performances on the proposed protocol are analyzed mathematically, and verified by numerical results., IEEE
2014 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC), 2014年,
［査読有り］ Auction-based Spectrum Leasing for Secure Information Transfer in Cognitive Radio NetworksXiaoyan Wang; Yusheng Ji; Hao Zhou; Jie Li, This paper investigates the secure information transfer issue for cognitive radio networks by exploiting the spectrum leasing technique. The design objective is to improve the secrecy rate of the primary user, and meanwhile, create the transmission opportunities for the secondary users. To achieve this goal, we consider a system model where the primary user harnesses the assist of the secondary users in the form of cooperative transmitting. And in return, the primary user provides certain transmission opportunities over licensed spectrum for the cooperating secondary users. We propose an auction-based spectrum leasing scheme to jointly formulate the optimal cooperator selection and resource allocation problems. By analyzing and solving the dominant strategy equilibrium for the proposed scheme, we present reliable predictions for the system behavior and the achievable performances. Simulation results reveal that the proposed scheme could provide substantial gains for both the primary user and the cooperating secondary user., IEEE
2014 IEEE 11TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SENSOR SYSTEMS (MASS), 2014年,
［査読有り］ Joint user scheduling, user association, and resource partition in heterogeneous cellular networksHao Zhou; Yusheng Ji; Xiaoyan Wang; Baohua Zhao, This paper investigates the joint optimization problem of user scheduling, user association, and resource partition in heterogeneous cellular networks (HetNet) with a general concave utility function used as the performance metric. We formulate the joint optimization problem, and decouple the problem into three subproblems. After proving the subproblems belong to the set of problems that maximizes a monotone submodular set function with matroid constraint, we solve them by the proposed greedy based algorithms with theoretical approximation factors. Extensive simulation results demonstrate the efficiency of the proposed algorithms in terms of system utility. In addition, we evaluate some assumptions and results in the related work to show their impacts and correctness., IEEE
2014 IEEE 11TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SENSOR SYSTEMS (MASS), 2014年,
［査読有り］ Transmission Rate Enhancement via Adaptive Relaying,
Xiaoyan Wang; Jie Li and Kui Wu, 筆頭著者, Taylor&Francis
International Journal of Parallel, Emergent and Distributed Systems,, 2012年01月01日, ［査読有り］
NCAC-MAC: Network Coding Aware Cooperative Medium Access Control for Wireless NetworksXiaoyan Wang; Jie Li; Mohsen Guizani, Cooperative communication, which utilizes neighboring nodes to relay the overhearing information, has been employed as an effective technique to deal with the channel fading and to improve the network performances. And network coding, which combines several packets together for transmission, is very helpful to reduce the redundancy at the network and to increase the overall throughput. Introducing network coding into the cooperative retransmission process, enables the relay node to assist other nodes while serving its own traffic simultaneously. To leverage the benefits brought by both of them, an efficient Medium Access Control (MAC) protocol is needed. In this paper, we propose a novel network coding aware cooperative MAC protocol, namely NCAC-MAC, for wireless networks. The design objective of NCAC-MAC is to increase the throughput and reduce the delay of the network. Simulation results reveal that our NCAC-MAC can improve the network performance under general circumstances., IEEE
2012 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC), 2012年,
［査読有り］ Transmission rate enhancement of cooperative communications in wireless networksXiaoyan Wang; Jie Li; Kui Wu, Recently, the cooperative communication has been explored more and more as an impressing technique to combat the effects of channel fading and to reduce the energy consumption in wireless networks. However, the impact of cooperative communications on transmission rate has not been well-addressed yet. In this paper, we provide an analytical framework for transmission rate enhancement issue in wireless communications. For a given transmit power and a desired probability of success, we investigate how much average transmission rate can be increased by cooperative communications. Three transmission models have been compared: the direct transmission, the cooperative transmission without maximal ratio combiner (MRC), and cooperative transmission with MRC. The analytical and numerical results show that around 1 ∼ 3b/s/Hz transmission rate can be improved when the source-destination distance is 20m. We also reveal that the MRC scheme is not always beneficial, especially when the source-destination distance is short and the relay is close to the destination. © 2011 IEEE., IEEE
Proceedings - International Conference on Distributed Computing Systems, 2011年,
［査読有り］ Secure and Efficient Data Aggregation for Wireless Sensor NetworksXiaoyan Wang; Jie Li; Xiaoning Peng; Beiji Zou, This paper addresses the secure data aggregation for wireless sensor networks (WSNs) with both static tree architecture and dynamic cluster-based architecture. For WSNs with static tree architecture, we propose the Leaf Node Representation (LNR) scheme to solve the Id problem and make the key stream-based encrypted data aggregation feasible and practical for large scale networks. For WSNs with dynamic cluster-based architectures, we propose the Delayed Hop-by-hop Authentication (DHA) scheme to provide hop-by-hop data integrity and data freshness only using individual keys. Analytical results show that the proposed scheme can reduce the communication overhead significantly compared to a well known existing scheme., IEEE
2010 IEEE 72ND VEHICULAR TECHNOLOGY CONFERENCE FALL, 2010年,
［査読有り］ Secure Data Aggregation for Sensor NetworksZhang Peng; Yu Jian-ping, Researchers show that data aggregation could save energy and bandwidth of the networks. But the unreliability of wireless links will weaken the performance of network aggregation deeply. Based on homomorphic encryption technology and the non-repudiation transmission protocol, a novel approach to protect sensor data secure is proposed. Because of fully using symmetric encryption algorithm, the protocol is efficient. Security analysis shows that the proposed protocol can guarantee data end-to-end confidentiality and authentication., IEEE
2010 IEEE 10TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS (ICSP2010), VOLS I-III, 2010年,
［査読有り］ Energy Efficient Secure Data Aggregation Framework in Wireless NetworksXiaoyan Wang; Jie Li, This paper constructs an energy efficient secure data aggregation framework for wireless networks, especially for wireless sensor networks with femtocells. We propose the Leaf Node Representation (LNR) scheme and the Hop-by-hop MAC Authentication (HMA) scheme, in order to provide a balance between the security and the communication cost. The ideas in this paper are not restricted to wireless sensor network, it can be used in other kind of wireless network after modification. Under the proposed scheme, keystream-based encrypted data aggregation is feasible and practical for large scale implementations. Analytical results show that the proposed scheme can reduce the communication overhead significantly compared to existing approaches., IEEE
2009 IEEE 20TH INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS, 2009年,
［査読有り］ Precision Constraint Data Aggregation for Dynamic Cluster-based Wireless Sensor NetworksXiaoyan Wang; Jie Li, This paper studies the precision-constraint data aggregation problem for dynamic cluster-based wireless sensor networks. The goal is to extend the network lifetime while keeping reasonable data quality. To achieve the target, we propose the dynamical precision allocation algorithm, which splits the application error bound which users can tolerate into individual local error bounds. We differentiate the sensor nodes in clustering architecture to cluster-heads and leaf nodes, arrange the error bounds to the nodes that can really reduce their transmitting messages. In order to reduce the overhead, our algorithm is merged to the cluster-head reelection process. Experimental results show that our scheme significantly improves the network lifetime compared to the existing methods., IEEE
2009 FIFTH INTERNATIONAL CONFERENCE ON MOBILE AD-HOC AND SENSOR NETWORKS, 2009年,
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