World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
41
Citations
8921
World Ranking
8704
National Ranking
1128

Overview

Gang Feng is affiliated with the University of Electronic Science and Technology of China. Their research primarily spans the fields of Computer Science and Engineering, with a substantial focus on various subfields including Computer Networks and Communications, Electrical and Electronic Engineering, Artificial Intelligence, Aerospace Engineering, and Information Systems.

The scientist's work encompasses several main research topics, highlighting areas such as:

  • Advanced MIMO Systems Optimization
  • Software-Defined Networks and 5G
  • Privacy-Preserving Technologies in Data
  • IoT and Edge/Fog Computing
  • Satellite Communication Systems
  • Mobile Crowdsensing and Crowdsourcing
  • Advanced Wireless Communication Technologies

Among recent publications, Gang Feng has contributed to:

  • Intelligent Reflecting Surface-Assisted Cognitive Radio System, 2020, IEEE Transactions on Communications
  • Deep Reinforcement Learning for Joint Channel Selection and Power Control in D2D Networks, 2020, IEEE Transactions on Wireless Communications
  • Network Slice Reconfiguration by Exploiting Deep Reinforcement Learning With Large Action Space, 2020, IEEE Transactions on Network and Service Management
  • Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning, 2020, IEEE Transactions on Vehicular Technology
  • Joint Computation Offloading and Resource Allocation for D2D-Assisted Mobile Edge Computing, 2022, IEEE Transactions on Services Computing

This body of work is published in several frequent venues, indicating the scientist's active involvement in communications and network-related fields. These venues include:

  • IEEE Transactions on Cognitive Communications and Networking
  • IEEE Transactions on Wireless Communications
  • IEEE Transactions on Network and Service Management
  • IEEE Transactions on Vehicular Technology
  • GLOBECOM 2022 - 2022 IEEE Global Communications Conference

Gang Feng has collaborated extensively with other researchers in the field. Frequent co-authors include:

  • Shuang Qin
  • Yao Sun
  • Xiaoqian Li
  • Yi-Jing Liu
  • Fengsheng Wei

Best Publications

  • A survey of energy-efficient wireless communications

    Daquan Feng;Chenzi Jiang;Gubong Lim;L.J. Cimini

  • Device-to-Device Communications Underlaying Cellular Networks

    Daquan Feng;Lu Lu;Yi Yuan-Wu;G. Y. Li

  • Device-to-device communications in cellular networks

    Daquan Feng;Lu Lu;Yi Yuan-Wu;Geoffrey Ye Li

  • Intelligent Reflecting Surface-Assisted Cognitive Radio System

    Jie Yuan;Ying-Chang Liang;Jingon Joung;Gang Feng

  • Blockchain-Enabled Wireless Internet of Things: Performance Analysis and Optimal Communication Node Deployment

    Yao Sun;Lei Zhang;Gang Feng;Bowen Yang

  • Optimal Cooperative Content Caching and Delivery Policy for Heterogeneous Cellular Networks

    Wei Jiang;Gang Feng;Shuang Qin

  • Proactive content caching by exploiting transfer learning for mobile edge computing

    Tingting Hou;Gang Feng;Shuang Qin;Wei Jiang

  • iRAF: A Deep Reinforcement Learning Approach for Collaborative Mobile Edge Computing IoT Networks

    Jienan Chen;Siyu Chen;Qi Wang;Bin Cao

  • Intelligent Resource Scheduling for 5G Radio Access Network Slicing

    Mu Yan;Gang Feng;Jianhong Zhou;Yao Sun

  • Multi-Agent Reinforcement Learning for Efficient Content Caching in Mobile D2D Networks

    Wei Jiang;Gang Feng;Shuang Qin;Tak Shing Peter Yum

  • Mode Switching for Energy-Efficient Device-to-Device Communications in Cellular Networks

    Daquan Feng;Guanding Yu;Cong Xiong;Yi Yuan-Wu

  • Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

    Lin Zhang;Junjie Tan;Ying-Chang Liang;Gang Feng

  • Deep Reinforcement Learning for Joint Channel Selection and Power Control in D2D Networks

    Junjie Tan;Ying-Chang Liang;Lin Zhang;Gang Feng

  • Network Slice Reconfiguration by Exploiting Deep Reinforcement Learning With Large Action Space

    Fengsheng Wei;Gang Feng;Yao Sun;Yatong Wang

  • The SMART Handoff Policy for Millimeter Wave Heterogeneous Cellular Networks

    Yao Sun;Gang Feng;Shuang Qin;Ying-Chang Liang

  • Resource Allocation for Network Slices in 5G with Network Resource Pricing

    Gang Wang;Gang Feng;Wei Tan;Shuang Qin

  • Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning

    Yi-Jing Liu;Gang Feng;Yao Sun;Shuang Qin

  • Joint Computation Offloading and Resource Allocation for D2D-Assisted Mobile Edge Computing

    Unknown

  • Energy-Efficient Downlink Resource Allocation in Heterogeneous OFDMA Networks

    Kai Yang;Steven Martin;Dominique Quadri;Jinsong Wu

  • Reconfiguration in Network Slicing—Optimizing the Profit and Performance

    Gang Wang;Gang Feng;Tony Q. S. Quek;Shuang Qin

  • QoS-Aware Resource Allocation for Device-to-Device Communications With Channel Uncertainty

    Daquan Feng;Lu Lu;Yuan-Wu Yi;Geoffrey Ye Li

  • On feasibility examination of DS-CDMA systems with imperfect successive interference cancellation

    Zhaorong Zhou;Gang Feng;Yide Zhang;Lemin Li

Frequent Co-Authors

Ying-Chang Liang
Ying-Chang Liang University of Electronic Science and Technology of China
Lei Zhang
Lei Zhang University of Glasgow
H. Vincent Poor
H. Vincent Poor Princeton University
Geoffrey Ye Li
Geoffrey Ye Li Imperial College London
Shaoqian Li
Shaoqian Li University of Electronic Science and Technology of China
Tony Q. S. Quek
Tony Q. S. Quek Singapore University of Technology and Design
Dusit Niyato
Dusit Niyato Nanyang Technological University
Chonggang Wang
Chonggang Wang InterDigital (United States)
Erik G. Larsson
Erik G. Larsson Linköping University
Zhi Chen
Zhi Chen University of Kentucky

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