Geoffrey Li

Professor, Chair in Wireless Systems

Imperial College London

United Kingdom

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Geoffrey Li is a Professor and Chair in Wireless Systems at Imperial College London, United Kingdom. His research interests focus on advanced wireless communication systems, including deep learning applications in channel state information feedback and beamforming techniques for high-speed communications. His recent publications explore innovative approaches such as analog integrated circuits performance testing and semantic communication protocols in AI agent networks.

Geoffrey Li

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Articles (30)

Distributionally Robust Adaptive Beamforming

Distributionally Robust Receive Combining

Deep Learning-Based CSI Feedback for RIS-Assisted Multi-User Systems

Deep Learning-Based Performance Testing for Analog Integrated Circuits

Learning Rate-Compatible Linear Block Codes: An Auto-Encoder Based Approach

Beam Switching Based Beam Design for High-Speed Train mmWave Communications

Beam Prediction Based on Large Language Models

Beam Structured Precoder for HF Skywave Massive MIMO-OFDM Communications with Channel Smoothness Constraint

Distillation-Enabled Knowledge Alignment Protocol for Semantic Communication in AI Agent Networks

Downlink Beamforming for Cell-Free ISAC: A Fast Complex Oblique Manifold Approach

Beam Structured Channel Estimation for HF Skywave Massive MIMO-OFDM Communications

Accretionary Learning With Deep Neural Networks With Applications

CSI-PPPNet: A One-Sided One-for-All Deep Learning Framework for Massive MIMO CSI Feedback

Deep Learning Aided Low Complex Breadth-First Tree Search for MIMO Detection

Over-The-Air Federated Learning Over Scalable Cell-Free Massive MIMO

Compression Ratio Learning and Semantic Communications for Video Imaging

Federated Multi-View Synthesizing for Metaverse

Simultaneous Beam Training and Target Sensing in ISAC Systems With RIS

AI Empowered Wireless Communications: From Bits to Semantics

Beam Training and Tracking for Extremely Large-Scale MIMO Communications

Channel Acquisition for HF Skywave Massive MIMO-OFDM Communications

Federated Learning Via Inexact ADMM

Deep Learning Enabled Semantic Communications With Speech Recognition and Synthesis

CSI-Fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

Semantic Communication With Memory

Robust WMMSE Precoder With Deep Learning Design for Massive MIMO

Deep Learning-Based Channel Estimation for Wideband Hybrid MmWave Massive MIMO

Learn to Adapt to New Environments From Past Experience and Few Pilot Blocks

Reconfigurable Wireless Relaying With Multi-UAV-Carried Intelligent Reflecting Surfaces

Beamspace Channel Estimation for Wideband Millimeter-Wave MIMO: A Model-Driven Unsupervised Learning Approach

Collaborators (4)

Wei Dai

Shenglong Zhou

Cheng-Xiang Wang

Mats Bengtsson

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