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Electronics and Electrical Engineering
UK
2026
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Computer Science
UK
2025

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
118
Citations
68791
World Ranking
68
National Ranking
3

Research.com Recognitions

  • 2026 - Research.com Electronics and Electrical Engineering in United Kingdom Leader Award
  • 2025 - Research.com Computer Science in United Kingdom Leader Award
  • 2025 - Research.com Electronics and Electrical Engineering in United Kingdom Leader Award
  • 2023 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Computer Science in United Kingdom Leader Award
  • 2022 - Research.com Electronics and Electrical Engineering in United Kingdom Leader Award

Overview

Geoffrey Ye Li is affiliated with Imperial College London in the United Kingdom. Their research primarily spans the fields of engineering and computer science, with a significant focus on electrical and electronic engineering, artificial intelligence, aerospace engineering, computer networks and communications, and computer vision and pattern recognition.

The scientist's scholarly output includes numerous papers addressing advanced topics in wireless communications and machine learning. Recent publications include:

  • Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts, 2020, Science China Information Sciences
  • Deep Learning Enabled Semantic Communication Systems, 2021, IEEE Transactions on Signal Processing
  • Model-Driven Deep Learning for MIMO Detection, 2020, IEEE Transactions on Signal Processing
  • Deep Learning-Based End-to-End Wireless Communication Systems With Conditional GANs as Unknown Channels, 2020, IEEE Transactions on Wireless Communications
  • Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis, 2020, IEEE Transactions on Wireless Communications

The main topics covered in their work include:

  • Advanced MIMO Systems Optimization
  • Wireless Signal Modulation Classification
  • Millimeter-Wave Propagation and Modeling
  • Advanced Wireless Communication Technologies
  • Antenna Design and Analysis
  • Microwave Engineering and Waveguides
  • Antenna Design and Optimization

Geoffrey Ye Li collaborates frequently with various co-authors, including Shi Jin, Zhijin Qin, Chenhao Qi, Chao-Kai Wen, and Le Liang. These professional relationships contribute to a substantial collaborative research output.

The scientist's work appears in a number of venues, with repeated publications in:

  • arXiv (Cornell University)
  • IEEE Transactions on Wireless Communications
  • IEEE Transactions on Communications
  • IEEE Wireless Communications Letters
  • IEEE Journal on Selected Areas in Communications

In addition to journal articles, Geoffrey Ye Li has contributed to academic literature through book publication. One known book is titled Wireless Communications and Machine Learning, published by Cambridge University Press in 2025.

Best Publications

  • An Overview of Massive MIMO: Benefits and Challenges

    Lu Lu;Geoffrey Ye Li;A. Lee Swindlehurst;Alexei E. Ashikhmin

  • Robust channel estimation for OFDM systems with rapid dispersive fading channels

    Ye Geoffrey Li;L.J. Cimini;N.R. Sollenberger

  • Broadband MIMO-OFDM wireless communications

    G.L. Stuber;J.R. Barry;S.W. McLaughlin;Ye Li

  • Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems

    Hao Ye;Geoffrey Ye Li;Biing-Hwang Juang

  • Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts

    Xiaohu You;Cheng-Xiang Wang;Jie Huang;Xiqi Gao

  • Fundamental trade-offs on green wireless networks

    Yan Chen;Shunqing Zhang;Shugong Xu;Geoffrey Ye Li

  • OFDM and Its Wireless Applications: A Survey

    Taewon Hwang;Chenyang Yang;Gang Wu;Shaoqian Li

  • Cognitive radio networking and communications: an overview

    Ying-Chang Liang;Kwang-Cheng Chen;Geoffrey Ye Li;P. Mahonen

  • A survey of energy-efficient wireless communications

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

  • Deep Learning Enabled Semantic Communication Systems

    Huiqiang Xie;Zhijin Qin;Geoffrey Ye Li;Biing-Hwang Juang

  • Device-to-Device Communications Underlaying Cellular Networks

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

  • Energy-efficient wireless communications: tutorial, survey, and open issues

    G. Y. Li;Zhikun Xu;Cong Xiong;Chenyang Yang

  • Deep Reinforcement Learning Based Resource Allocation for V2V Communications

    Hao Ye;Geoffrey Ye Li;Biing-Hwang Fred Juang

  • Signal Processing in Cognitive Radio

    Jun Ma;G.Y. Li;Biing Hwang Juang

  • Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems

    Hengtao He;Chao-Kai Wen;Shi Jin;Geoffrey Ye Li

  • Energy-efficient link adaptation in frequency-selective channels

    Guowang Miao;N. Himayat;G.Y. Li

  • Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities

    Mohamed A. ElMossallamy;Hongliang Zhang;Lingyang Song;Karim G. Seddik

  • Model-Driven Deep Learning for Physical Layer Communications

    Hengtao He;Shi Jin;Chao-Kai Wen;Feifei Gao

  • Deep Learning in Physical Layer Communications

    Zhijin Qin;Hao Ye;Geoffrey Ye Li;Biing-Hwang Fred Juang

  • Collaborative Cloud and Edge Computing for Latency Minimization

    Jinke Ren;Guanding Yu;Yinghui He;Geoffrey Ye Li

  • Energy- and Spectral-Efficiency Tradeoff in Downlink OFDMA Networks

    Cong Xiong;Geoffrey Ye Li;Shunqing Zhang;Yan Chen

  • MIMO-OFDM for wireless communications: signal detection with enhanced channel estimation

    Y.G. Li;J.H. Winters;N.R. Sollenberger

Frequent Co-Authors

Guanding Yu
Guanding Yu Zhejiang University
Shi Jin
Shi Jin Southeast University
Feifei Gao
Feifei Gao Tsinghua University
Shugong Xu
Shugong Xu Shanghai University
Chenyang Yang
Chenyang Yang Beihang University
Lingyang Song
Lingyang Song Peking University
Yunlong Cai
Yunlong Cai Zhejiang University
Zhu Han
Zhu Han University of Houston
Shaoqian Li
Shaoqian Li University of Electronic Science and Technology of China
Gang Wu
Gang Wu Washington University in St. Louis

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Related Online Degrees & Career Pathways

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Competency-based learning also plays a growing role in engineering education, with competency based masters degree formats emphasizing skills mastery over traditional credit hours. This approach suits self-motivated engineers ready to demonstrate expertise at their own pace.

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