Selected recent publications

Recent Publications
  • X.-Y. Wei, B.-H. F. Juang, O.-Y. Wang, S.-L. Zhou, and G. Y. Li, “Accretionary learning with deep neural networks with application,” IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 2, pp. 660-673, April 2024.
  • Y.-Y. Guo, Z.-J. Qin, X.-M. Tao, and G. Y. Li, “Federated multi-view synthesizing for metaverse,” IEEE Journal on Selected Areas in Communications, vol. 42, no. 4, pp. 867-879, April 2024. 
  • H. Sifaou and G. Y. Li, “Over-the-air federated learning over scalable cell-free massive MIMO,” IEEE Transactions on Wireless Communications, vol. 23, no. 5, pp. 4214-4227, May 2024.
  • K.-J. Chen, C.-H. Qi, C.-X. Wang, and G. Y. Li, “Beam training and tracking for extremely large-scale MIMO communications,” IEEE Transactions on Wireless Communications, vol. 23, no. 5, pp. 5048-5062, May 2024. 
  • K.-J. Chen, C.-H. Qi, O. A. Dobre, and G. Y. Li, “Near-field multiuser communications based on sparse arrays,” Journal of Selected Topics in Signal Processing, vol. 18, no. 4, pp. 619-632, May 2024.
  • J.-Y. Liao, J.-H. Zhao, F.-F. Gao, and G. Y. Li, “Deep learning aided low complex breath-first tree search for MIMO detection,” IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 6266-6278, June 2024.
  • S. Zargari, C. Tellambura, A. Maaref, and G. Y. Li, “Deep conditional generative adversarial networks for efficient channel estimation in AmBC systems,” IEEE Transactions on Machine Learning in Communications and Networking, vol. 2, no. 6, pp. 805-822, 2024.
  • W.-X. Wan, W. Chen, S.-Y. Wang, G. Y. Li, and B. Ai, “Deep plug-and play prior for multitask channel reconstruction in massive MIMO systems,” IEEE Transactions on Communications, vol. 72, no. 7, pp. 4149-4162, July 2024.
  • Z.-J. Qin, L. Liang, Z.-J. Wang, S. Jin, X.-M. Tao, W. Tong, G. Y. Li, “AI empowered wireless communications: from bits to semantics,” Proceedings of the IEEE, vol. 112, no. 7, pp. 621-652, July 2024.
  • S.-X. Wang, W. Dai, H.-W. Wang, and G. Y. Li, “Robust waveform design for integrated sensing and communication,” IEEE Transactions on Signal Processing, vol. 72, no. 7, pp. 3122-3138, 2024.
  • Y.-Z. Liu Z.-J. Qin, and G. Y. Li, “Energy-efficient distributed spiking neural network for wireless edge intelligence,” IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 10683-10697, September 2024.
  • B. Lin, C.-B. Zhao, F.-F. Gao, G. Y. Li, J. Qian, and H. Wang, “Environment reconstruction based on multi-user selection and multi-modal fusion in ISAC,” IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 15083-15095, October 2024.
  • Z.-X. Chen, W.-Q. Yi, A. Nallanathan, and G. Y. Li, “Efficient wireless federated learning with partial model aggregation,” IEEE Transactions on Communications, vol. 72, no. 10, pp. 6271-6286, October 2024.
  • K.-D. Xu, S.-L. Zhou, and G. Y. Li, “Federated reinforcement learning for resource allocation in V2X networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 18, no. 7, pp. 1210-1221, October 2024.
  • S.-Z. Hu, Y.-P. Duan, X.-M. Tao, G. Y. Li, J.-H. Lu, G.-Y. Liu, and Z.-M. Zheng, C.-K. Pan, “Brain-inspired image perceptual quality assessment based on EEG: A QoE perspective,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 12, pp. 8424-8441, December 2024.
  • K.-D. Xu, S.-L. Zhou, and G. Y. Li, “Rescale-invariant federated reinforcement learning for resource allocation in V2X networks,” IEEE Communications Letters, vol. 46, no. 12, pp. 2799-2803, December 2024.
  • S.-L. Zhou, L. Pan, N. Xiu, and G. Y. Li, (2024). “A 0/1 constrained optimization solving sample average approximation for chance constrained programming,” Mathematics of Operations Research, DOI: https://doi.org/10.1287/moor.2023.0149
  • H. Zhang, S.-L. Zhou, G. Y. Li, N. Xiu, and Y. Wang, (2025). “A step function based recursion method for 0/1 deep neural networks,” Applied Mathematics and Computation, 488, 129129.
  • Y.-C. Sheng, K. Huang, L. Liang, P. Liu, S. Jin, and G. Y. Li, “Beam prediction based on large language models,” IEEE Wireless Communications Letters, vol. 14, no. 5, pp. 1406-1410, May 2025.
  • J.-J. Guo, X. Yang, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based CSI feedback for RIS-assisted multi-user systems,” IEEE Transactions on Communications, vol. 73, no. 7, pp. 4974-4989, July 2025.
  • P.-W. Jiang, C.-K. Wen, S. Jin, and G. Y. Li, “Semantic satellite communications based on generative foundation model,” IEEE Journal on Selected Area in Communications, vol. 43, no. 7, pp. 2431-2445, July 2025.
  • S.-X. Wang, W. Dai, and G. Y. Li, “Distributionally robust receive combining,” IEEE Transactions on Signal Processing, vol. 73, pp. 2736-2752, 2025.
  • S.-X. Wang, W. Dai, and G. Y. Li, “Distributionally robust adaptive beamforming,” IEEE Transactions on Signal Processing, vol. 73, pp. 2981-2997, 2025.
  • S. Zargari, D. Galappaththige, C. Tellambura, and G. Y. Li, “Downlink beamforming for cell-free ISAC: A fast complex oblique manifold approach,” IEEE Transactions on Wireless Communications, vol. 24, no. 8, pp. 6458-6474, August 2025.
  • S. Sha, S.-L. Zhou, L.-C. Kong, and G. Y. Li, “Sparse decentralized federated learning,” IEEE Transactions on Signal Processing, vol. 73, pp. 3406-3420, 2025.
  • Z.-J. Cao, H. Zhang, L. Liang, H.-T. Wang, S. Jin, and G. Y. Li, “Task-oriented semantic communication for stereo-vision 3D object detection,” IEEE Transactions on Communications, vol. 73, no. 9, pp. 7552-7567, September 2025.
  • K. Huang, L. Liang, X.-P. Yi, H. Ye, S. Jin, and G. Y. Li, “Meta-learning empowered graph neural networks for radio resource management,” IEEE Transactions on Communications, vol. 73, no. 9, pp. 7584-7598, September 2025.
  • O.-Y. Wang, H.-T. He, S.-L. Zhou, Z. Ding, S. Jin, K. B. Letaief, and G. Y. Li, “Fast adaptation for deep learning-based wireless communications,” IEEE Communications Magazine, vol. 63, no. 10, pp. 158-164, October 2025.
  • O.-Y. Wang, S.-L. Zhou, and G. Y. Li, “Frameworks on few-shot learning with applications in wireless communication,” IEEE Transactions on Signal Processing, vol. 73, pp. 3857-3871, 2025.
  • J.-Z. Hu and G. Y. Li, “Distillation-enabled knowledge alignment protocol for semantic communication in AI agent networks,” IEEE Communications Letters, vol. 29, no. 11, pp. 2541-2545, November 2025.
  • H. Zhou, Y.-C. Liang, C. Yuen, and G. Y. Li, “Cooperative modulation for symbiotic radio: design methodology, challenges, and solutions,” IEEE Communications Magazine, early access.
  • S.-X. Wang, W. Dai, and G. Y. Li, “Uncertainty awareness in wireless communications and sensing,” IEEE Communications Magazine, early access.
  • Y.-C. Sheng, L. Liang, H. Ye, S. Jin, and G. Y. Li, “Semantic communication for cooperative perception using HARQ,” IEEE Transactions on Cognitive Communications and Networking, early access.
  • C. Huang, X.-Y Chen, G.-J. Chen, X. Pei, Y. G. Li, and W. Huang, “Deep reinforcement learning-based resource allocation for hybrid bit and generative semantic communications in space-air-ground integrated networks,” IEEE Journal of Selected Areas on Communications, early access.
  • J.-S. Zhou, C. Tellambura, and G. Y. Li, “Hybrid beamforming design for RSMA-enabled near-field integrated sensing and communications,” IEEE Transactions on Communications, early access.
  • Z.-J. Cao, H. Zhang, L. Liang, J.-P. Gan, S. Jin, and G. Y. Li, “Physical-layer secure transmission for semantic communication systems,” IEEE Transactions on Communications, early access.
  • Y.-Z. Huang, X.-D. Wang, and G. Y. Li, “Federated multi-task semantic communications with unified encoder and task-specific decoders,” IEEE Wireless Communications Letters, early access.
  • S.-L. Zhou, O.-Y. Wang, Z.-Y. Luo, Y.-X. Zhu, and G. Y. Li, “Preconditioned inexact stochastic ADMM for deep models,” to appear in Nature Machine Intelligence.
Overview
  • H.-T. He, S. Jin, C.-K. Wen, F.-F. Gao, G. Y. Li, and Z.-B. Xu, “Model-driven deep learning for physical layer communications,” IEEE Wireless Communications, vol. 26, no. 5, pp. 77 - 83, October 2019.  
  • W.-K. Tang, X.-Y. Chen, M.-Z. Chen, J.-Y. Dai, Y. Han, S. Jin, Q. Cheng, G. Y. Li, and T.-J. Cui, “On channel reciprocity in reconfigurable intelligent surface assisted wireless networks,” IEEE Wireless Communications, vol. 28, no. 6, pp. 94 – 101, December 2021.
  • Tong and G. Y. Li “Nine critical issues in AI and wireless communications to ensure successful 6G,” IEEE Wireless Communications, vol. 29, no. 4, pp. 140 – 145, August 2022.
  • M.-Y. Lee, G.-D. Yu, H.-Y. Dai, and G. Y. Li, “Graph neural networks meet wireless communications: motivation, applications, and future directions,” IEEE Wireless Communications, vol. 29, no. 5, pp. 12 – 19, October 2022.
  • J.-J. Guo, C.-K. Wen, S. Jin, and G. Y. Li, “Overview of deep learning-based CSI feedback in massive MIMO systems,” IEEE Transactions on Communications, vol. 70, no. 12, pp. 8017-8045, December 2022.
  • P.-W. Jiang, C.-K. Wen, J. Shi, and G. Y. Li, “Wireless semantic transmission via revising modules in conventional communications,” IEEE Wireless Communications, vol. 30, no. 3, pp 28-34, June 2023.
  • O.-Y. Wang, H.-T. He, S.-L. Zhou, Z. Ding, S. Jin, K. B. Letaief, and G. Y. Li, “Fast adaptation for deep learning-based wireless communications,” IEEE Communications Magazine, vol. 63, no. 10, pp. 158-164, October 2025.
  • S.-X. Wang, W. Dai, and G. Y. Li, “Uncertainty awareness in wireless communications and sensing,” to appear in IEEE Communications Magazine, early access.

 

Deep Learning for Physical Layer Processing in Communications
  • H. Ye, G. Y. Li, and B.-H. F. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems”, IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114 – 117, February 2018.
  • H.-T. He, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based channel estimation for beamspace mmwave massive MIMO systems”, IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 852 – 855, October 2018. 
  • T-Q. Wang, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based CSI feedback approaches for time-varying massive MIMO channels”, IEEE Wireless Communications Letters, vol. 8, No. 2, pp. 416-419, April 2019. 
  • P.-H. Dong, H. Zhang, G. Y. Li, N. Naderializadeh, and I. S. Gaspar, “Deep CNN based channel estimation for mmwave massive MIMO Systems”, IEEE Journal on Selected Topics in Signal Processing, vol. 13, no. 5, pp. 989 - 1000, September 2019. 
  • H.-T. He, C.-K. Wen, S. Jin, and G. Y. Li, “Model-driven deep learning for MIMO detection”, IEEE Transactions on Signal Processing, vol. 68, pp. 1702-1715, March 2020. 
  • H. Ye, L. Liang, G. Y. Li, and B.-H. F. Juang, “Deep learning-based end-to-end wireless communication systems with GAN as unknown channels”, IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 3133-3143, May 2020.
  • Q. Hu, F.-F. Gao, H. Zhang, Shi Jin, and G. Y. Li, “Deep learning for channel estimation: interpretation, performance, and comparison”, IEEE Transactions on Wireless Communications, vol. 20, no. 4, pp. 2398-2412, April 2021. 
  • H.-Q. Xie, Z.-J. Qin, G. Y. Li, and B.-H. F. Juang “Deep learning enabled semantic communication systems,” IEEE Transactions on Signal Processing, vol. 69, pp. 2663-2675, 2021. 
  • H. Ye, L. Liang, G. Y. Li, and B.-H. F. Juang, “Deep learning based end-to-end wireless communication systems without pilots,” IEEE Transactions on Cognitive Communications and Networking, vol. 7, no. 3, pp. 702 – 714, September 2021.
  • J.-B. Gao, M. Hu, C.-J. Zhong, G. Y. Li, and Z.-Y. Zhang, “An attention-aided deep learning framework for massive MIMO channel estimation,” IEEE Transactions on Wireless Communications, vol. 21, no. 3, pp. 1823 – 1835, March 2022.
  • P.-W. Jiang, C.-K. Wen, S. Jin, and G. Y. Li, “Deep source-channel coding for sentence semantic transmission with HARQ,” IEEE Transactions on Communications, vol. 70, no. 8, pp. 5225 – 5240, August 2022.
  • P.-W. Jiang, C.-K. Wen, S. Jin, and G. Y. Li, “Wireless semantic communications for video conferencing,” IEEE Journal on Selected Areas in Communications. vol. 41, no. 1, pp. 230-244, January 2023.
  • O.-Y. Wang, J.-B. Gao, and G. Y. Li, “Learning to adapt to current environment from past experience: Few-shot online learning in wireless communications,” IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 2, pp. 373-385, April 2023.
  • B.-W. Zhang, H. Sifaou, and G. Y. Li, “CSI-fingerprinting indoor localization via attention-augmented residual convolutional neural network,” IEEE Transactions on Wireless Communications, vol. 22, no. 8, pp. 5583-5597, August 2023.
  • Q. Hu, F.-F. Gao, H. Zhang, G. Y. Li, and Z.-B. Xu, “Understanding deep MIMO detection,” IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 9626-9639, December 2023.
  • Y.-Z. Liu Z.-J. Qin, and G. Y. Li, “Energy-efficient distributed spiking neural network for wireless edge intelligence,” IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 10683-10697, September 2024.
  • B. Lin, C.-B. Zhao, F.-F. Gao, G. Y. Li, J. Qian, and H. Wang, “Environment reconstruction based on multi-user selection and multi-modal fusion in ISAC,” IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 15083-15095, October 2024.
  • S.-Z. Hu, Y.-P. Duan, X.-M. Tao, G. Y. Li, J.-H. Lu, G.-Y. Liu, and Z.-M. Zheng, C.-K. Pan, “Brain-inspired image perceptual quality assessment based on EEG: A QoE perspective,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 12, pp. 8424-8441, December 2024.
  • J.-J. Guo, X. Yang, C.-K. Wen, S. Jin, and G. Y. Li, “Deep learning-based CSI feedback for RIS-assisted multi-user systems,” IEEE Transactions on Communications, vol. 73, no. 7, pp. 4974-4989, July 2025.
  • P.-W. Jiang, C.-K. Wen, S. Jin, and G. Y. Li, “Semantic satellite communications based on generative foundation model,” IEEE Journal on Selected Area in Communications, vol. 43, no. 7, pp. 2431-2445, July 2025.
  • Z.-J. Cao, H. Zhang, L. Liang, H.-T. Wang, S. Jin, and G. Y. Li, “Task-oriented semantic communication for stereo-vision 3D object detection,” IEEE Transactions on Communications, vol. 73, no. 9, pp. 7552-7567, September 2025.
  • Y.-C. Sheng, L. Liang, H. Ye, S. Jin, and G. Y. Li, “Semantic communication for cooperative perception using HARQ,” IEEE Transactions on Cognitive Communications and Networking, early access.
Intelligent Wireless Resource Allocation
  • H. Ye, G. Y. Li, B.-H. F. Juang, “Deep reinforcement learning based resource allocation for V2V communications”, IEEE Transactions on Vehicular Technology, vol. 68, no. 4, pp. 3163-3173, April 2019. 
  • L. Liang, H. Ye, and G. Y. Li, “Spectrum sharing in vehicular networks based on multi-agent reinforcement learning”, IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2282 - 2292, October 2019.
  • L. Wang, H. Ye, L. Liang, and G. Y. Li, “Learn to compress CSI and allocate resources in vehicular networks”, IEEE Transactions on Communications, vol. 68, no. 6, pp. 3640 – 3653, June 2020. 
  • M.-Y. Lee, G.-D. Yu, and G. Y. Li, “Graph embedding based wireless link scheduling with few training samples”, IEEE Transactions on Wireless Communications, vol. 20, no. 4, pp. 2282-2294, April 2021.
  • K.-D. Xu, H. Nguyen, and G. Y. Li, “Distributed-training-and-execution multi-agent reinforcement learning for power control in HetNet,” IEEE Transactions on Communications. vol. 71, no. 10, pp. 5893-5903, October 2023.
  • C.-T. Guo, Z.-C. Li, L. Liang, and G. Y. Li, “Reinforcement learning based dynamic power control for reliable wireless transmission,” IEEE Internet of Things Journal, vol. 10, no. 23, pp. 20868-20883, December 2023.
  • K.-D. Xu, S.-L. Zhou, and G. Y. Li, “Federated reinforcement learning for resource allocation in V2X networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 18, no. 7, pp. 1210-1221, October 2024.
  • K.-D. Xu, S.-L. Zhou, and G. Y. Li, “Rescale-invariant federated reinforcement learning for resource allocation in V2X networks,” IEEE Communications Letters, vol. 46, no. 12, pp. 2799-2803, December 2024.
  • K. Huang, L. Liang, X.-P. Yi, H. Ye, S. Jin, and G. Y. Li, “Meta-learning empowered graph neural networks for radio resource management,” IEEE Transactions on Communications, vol. 73, no. 9, pp. 7584-7598, September 2025.
  • C. Huang, X.-Y Chen, G.-J. Chen, X. Pei, Y. G. Li, and W. Huang, “Deep reinforcement learning-based resource allocation for hybrid bit and generative semantic communications in space-air-ground integrated networks,” IEEE Journal of Selected Areas on Communications, early access.
Distributed Learning and Federated Learning
  • H. Ye, L. Liang, and G. Y. Li, “Decentralized learning with unreliable communications,” IEEE Journal on Selected Topics in Signal Processing, vol. 16, no. 3, pp. 487 – 500, April 2022.
  • S.-L. Zhou and G. Y. Li, “FedGiA: an efficient hybrid algorithm for federated learning,” IEEE Transactions on Signal Processing, vol. 71, pp. 1941-1508, 2023.
  • S-L. Zhou and G. Y. Li, “Federated learning via inexact ADMM,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 8, pp. 9699-9780, August 2023.
  • Y.-Y. Guo, Z.-J. Qin, X.-M. Tao, and G. Y. Li, “Federated multi-view synthesizing for metaverse,” IEEE Journal on Selected Areas in Communications, vol. 42, no. 4, pp. 867-879, April 2024.
  • H. Sifaou and G. Y. Li, “Over-the-air federated learning over scalable cell-free massive MIMO,” IEEE Transactions on Wireless Communications, vol. 23, no. 5, pp. 4214-4227, May 2024
  • Z.-X. Chen, W.-Q. Yi, A. Nallanathan, and G. Y. Li, “Efficient wireless federated learning with partial model aggregation,” IEEE Transactions on Communications, vol. 72, no. 10, pp. 6271-6286, October 2024.
  • K.-D. Xu, S.-L. Zhou, and G. Y. Li, “Federated reinforcement learning for resource allocation in V2X networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 18, no. 7, pp. 1210-1221, October 2024.
  • K.-D. Xu, S.-L. Zhou, and G. Y. Li, “Rescale-invariant federated reinforcement learning for resource allocation in V2X networks,” IEEE Communications Letters, vol. 46, no. 12, pp. 2799-2803, December 2024.
  • S. Sha, S.-L. Zhou, L.-C. Kong, and G. Y. Li, “Sparse decentralized federated learning,” IEEE Transactions on Signal Processing, vol. 73, pp. 3406-3420, 2025.
  • Y.-Z. Huang, X.-D. Wang, and G. Y. Li, “Federated multi-task semantic communications with unified encoder and task-specific decoders,” IEEE Wireless Communications Letters, early access.
Few-Short Learning, Fast Adaptation, and Wireless Foundation Model
  • H. He, C.-K. Wen, S. Jin, and G. Y. Li, “Model-driven deep learning for MIMO detection,” IEEE Trans. Signal Process, vol. 68, pp. 1702–1715, Mar. 2020.
  • O.-Y. Wang, J.-B. Gao, and G. Y. Li, “Learning to adapt to current environment from past experience: Few-shot online learning in wireless communications,” IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 2, pp. 373-385, April 2023.
  • X.-Y. Wei, B.-H. F. Juang, O.-Y. Wang, S.-L. Zhou, and G. Y. Li, “Accretionary learning with deep neural networks with application,” IEEE Transactions on Cognitive Communications and Networking, vol. 10, no. 2, pp. 660-673, April 2024.
  • O.-Y. Wang, S.-L. Zhou, and G. Y. Li, “Frameworks on few-shot learning with applications in wireless communication,” IEEE Transactions on Signal Processing, vol. 73, pp. 3857-3871, 2025.
  • Y.-C. Sheng, K. Huang, L. Liang, P. Liu, S. Jin, and G. Y. Li, “Beam prediction based on large language models,” IEEE Wireless Communications Letters, vol. 14, no. 5, pp. 1406-1410, May 2025.
  • O.-Y. Wang, H.-T. He, S.-L. Zhou, Z. Ding, S. Jin, K. B. Letaief, and G. Y. Li, “Fast adaptation for deep learning-based wireless communications,” IEEE Communications Magazine, vol. 63, no. 10, pp. 158-164, October 2025.
  • S.-L. Zhou, O.-Y. Wang, Z.-Y. Luo, Y.-X. Zhu, and G. Y. Li, “Preconditioned inexact stochastic ADMM for deep models,” to appear in Nature Machine Intelligence.
Other Topics (wideband beamforming, ISAC, compressive sensing, etc.)
  • B.-L. Wang, F.-F. Gao, S. Jin, H. Lin, and G. Y. Li, “Spatial- and frequency-wideband effects in massive MIMO”, IEEE Transactions on Signal Processing, vol. 66, no. 13, pp. 3393 – 3406, July 2018. 
  • B.-L. Wang, X. Li, F.-F. Gao, and G. Y. Li, “Power leakage elimination for wideband mmwave massive MIMO: An energy focusing window approach”, IEEE Transactions on Signal Processing, vol. 67, no. 21, pp. 5479 - 5494, November 2019. 
  • B.-L. Wang, M.-N. Jian, F.-F. Gao, G. Y. Li, and H. Lin, “Beam squint and channel estimation for millimeter-wave massive MIMO-OFDM systems”, IEEE Transactions on Signal Processing, vol. 67, no. 23, pp. 5893 – 5908, December 2019.
  • F.-F. Gao, B.-L. Wang, C.-W. Xing, J.-P. An, and G. Y. Li, “Wideband beamforming for hybrid massive MIMO terahertz communications”, IEEE Journal on Selected Areas in Communications, vol. 39, no. 6, pp. 1725-1740, June 2021. 
  • F.-F. Gao, B.-L. Wang, C.-W. Xing, J.-P. An, and G. Y. Li, “Wideband beamforming for hybrid massive MIMO terahertz communications,” IEEE Journal on Selected Areas in Communications, vol. 39, no. 6, pp. 1725-1740, June 2021.
  • S.-L. Zhou, Z.-Y. Luo, N.-H. Xiu, and G. Y. Li, “Computing one-bit compressive sensing via double-sparsity constrained optimization,” IEEE Transactions on Signal Processing, vol. 70, pp. 1593 – 1608, 2022.
  • S.-X. Wang, W. Dai, H.-W. Wang, and G. Y. Li, “Robust waveform design for integrated sensing and communication,” IEEE Transactions on Signal Processing, vol. 72, no. 7, pp. 3122-3138, 2024.
  • S.-X. Wang, W. Dai, and G. Y. Li, “Distributionally robust receive combining,” IEEE Transactions on Signal Processing, vol. 73, pp. 2736-2752, 2025.
  • S.-X. Wang, W. Dai, and G. Y. Li, “Distributionally robust adaptive beamforming,” IEEE Transactions on Signal Processing, vol. 73, pp. 2981-2997, 2025.
  • S.-Z. Hu, Y.-P. Duan, X.-M. Tao, G. Y. Li, J.-H. Lu, G.-Y. Liu, and Z.-M. Zheng, C.-K. Pan, “Brain-inspired image perceptual quality assessment based on EEG: A QoE perspective,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 12, pp. 8424-8441, December 2024.
  • H. Zhang, S.-L. Zhou, G. Y. Li, N. Xiu, and Y. Wang, (2025). “A step function based recursion method for 0/1 deep neural networks,” Applied Mathematics and Computation, 488, 129129.
  • S.-L. Zhou, L. Pan, N. Xiu, and G. Y. Li, (2024). “A 0/1 constrained optimization solving sample average approximation for chance constrained programming,” Mathematics of Operations Research, DOI: https://doi.org/10.1287/moor.2023.0149

Location

Imperial College London
Faculty of Engineering
South Kensington Campus
London SW7 2AZ, UK
White City Campus
London W12 7TA, UK

Key links

Professor Geoffrey Ye Li

Lab Director
geoffrey.li@imperial.ac.uk
Contact us