Selected recent publications

Overview

  • Z.-J. Qin, J.-C. Fan, Y.-W. Liu, Y. Gao, and G. Y. Li, “Sparse representation for wireless communications, a compressive sensing approach”, IEEE Signal Processing Magazine, vol. 35, no. 3, pp. 40-58, May 2018.
  • Y.-L. Cai, Z.-J. Qin, F.-Y. Cui, G. Y. Li, and J. A. McCann, “Modulation and multiple access for 5G networks”, IEEE Communications Surveys and Tutorials, vol. 20, no. 1, pp. 629-646, First Quarter, 2018.
  • B.-L. Wang, F.-F. Gao, S. Jin, G. Y. Li, S. Sun, and T. S. Rappaport, “Spatial-wideband effect in massive MIMO with application to mmWave systems”, IEEE Communications Magazine, vol. 56, no. 12, pp. 134-141, December 2018.
  • 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”, to appear in IEEE Wireless Communications.
  • L. Liang, H.-X. Peng, G. Y. Li, and X. M. Shen, “Vehicular communications: a physical layer perspective”, IEEE Transactions on Vehicular Technology, vol. 66, no. 12, pp. 10647-10659, December 2017.
  • H.-X. Peng, L. Liang, X.-M. Shen, and G. Y. Li, “Vehicular communications: a network layer perspective”, IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1064-1078, February 2019.
  • Y.-W. Liu, Z.-J. Qin, Y.-L. Cai, Y. Gao, G. Y. Li, and A. Nallanathan, “UAV communications based on non-orthogonal multiple access”, IEEE Wireless Communications, vol. 26, no. 1, pp. 52-57, February 2019.
  • S.-Q. Zhang, S.-G. Xu, G. Y. Li, and E. Ayanoglu, “First 20 years of green radios”, IEEE Transactions on Green Communications and Networks, vol. 4, no. 1, pp.1-15, March 2020.
  • Z.-J. Qin, X.-W. Zhou, L. Zhang, Y. Gao, Y.-C. Liang, and G. Y. Li, “20 years of evolution from cognitive to intelligent communications”, IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 6-20, March 2020.
  • Z.-J. Qin, H. Ye, G. Y. Li, and B.-H. Juang, “Deep learning in physical layer communications”, IEEE Wireless Communications, vol. 26, no. 2, pp. 93-99, April 2019.
  • 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.
  • L. Liang, H. Ye, G.-D. Yu, and G. Y. Li, “Deep learning based wireless resource allocation with application in vehicular networks”, the Proceedings of the IEEE, vol. 108, no. 2, pp. 341-356, February 2020.
  • J.-J. Gao, J.-H. Wang, C.-K. Wen, S. Jin, and G. Y. Li, “Compression and acceleration of neural networks for communications”, IEEE Wireless Communications, vol. 27, no. 4, pp. 110-117, August 2020.
  • C.-H. Qi, P.-H. Dong, W.-Y. Ma, H. Zhang, Z.-C. Zhang, and G. Y. Li, “Acquisition of channel state information for mmWave massive MIMO: traditional and machine learning-based approaches”, Science China - Information Science, vol. 64, no. 8, August 2021.
  • Z.-J. Qin, G. Y. Li, and H. Ye, “Federated learning and wireless communications”, to appear in IEEE Wireless Communications.

Fundamental issue

  • S.-L. Zhou, “Sparse SVM for sufficient data reduction”, to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence.
  • S.-L. Zhou, N.-H. Xiu, and H.-D. Qi, “Global and quadratic convergence of Newton hard-thresholding pursuit, Journal of Machine Learning Research”, vol. 22, pp. 1−45, January 2021.
  • S.-L. Zhou, L.-L. Pan, N.-H. Xiu, and H.-D. Qi, Quadratic convergence of smoothing Newton's method for 0/1 loss optimization, to appear in SIAM Journal on Optimization.
  • S.-L. Zhou, Z.-Y. Luo, and N.-H. Xiu, “Computing one-bit compressive sensing via double-sparsity constrained optimization”, at https://arxiv.org/abs/2101.03599.
  • S.-L. Zhou, L.-L. Pan, and N.-H. Xiu, “Newton method for l0-regularized optimization”, to appear in Numerical Algorithms.
  • S.-L. Zhou, N.-H. Xiu and H.-D. Qi, “Robust Euclidean embedding via EDM optimization”, Mathematical Programming Computation, vol. 12, pp. 337-387, August 2019.
  • S.-L. Zhou, N.-H. Xiu and H.-D. Qi, “A fast matrix majorization-projection method for penalized stress minimization with box constraints”, IEEE Transactions on Signal Processing, vol. 66, pp. 4331-4346, June 2018.
  • H. Sifaou, A. Kammoun, L.Sanguinetti, M. Debbah, and M.-S. Alouini, “Max-Min SINR in large-scale single-cell MU-MIMO: asymptotic analysis and low complexity transceivers”, IEEE Transactions on Signal Processing, vol. 65, no. 7, pp. 1841-1854, April 2017.
  • H. Sifaou, A. Kammoun, K.-H. Park, and M.-S. Alouini, “Robust transceivers design for multi-stream multi-user MIMO visible light communication”, IEEE Access, vol. 5, pp. 26387-26399, November 2017.
  • H. Sifaou, A. Kammoun, and M.-S. Alouini, “High-dimensional linear discriminant analysis classifier for spiked covariance model”, Journal of Machine Learning Research, vol. 21, pp. 1–24, May 2020.
  • H. Sifaou, A. Kammoun, and M.-S. Alouini, “A precise performance analysis of support vector regression”, Proceedings of the 38th International Conference on Machine Learning, PMLR 139:9671-9680, July 2021.

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. 
  • J-J. Gao, C.-K. Wen, S. Jin, and G. Y. Li, “Convolutional neural network based multiple-rate compressive sensing for massive MIMO CSI feedback: design, simulation, and analysis”, IEEE Transactions on Wireless Communications, vol. 19, no. 4, pp. 2827-2840, April 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.
  • Y.-F. He, J. Zhang, S. Jin, C.-K. Wen, and G. Y. Li, “Model-driven DNN decoder for turbo codes: Design, simulation and experimental results”, IEEE Transactions on Communications, vol.68, no. 10, pp.6127-6140, October 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”, to appear in IEEE Transactions on Cognitive Communications and Networking. 
  • P.-W. Jiang, S. Jin, C.-K. Wen, and G. Y. Li, “Dual CNN based channel estimation for MIMO-OFDM systems”, to appear in IEEE Transactions on Communications. 
  • P.-W. Jiang, T.-Q. Wang, B. Han, X.-X. Gao, J. Zhang, C.-K. Wen, S. Jin, and G. Y. Li, “AI-aided online adaptive OFDM receiver: Design and experimental results”, to appear in IEEE Transactions on Wireless Communications. 
  • N.-V. Huynh, D.-N. Nguyen, D.-T. Hoang, T.-X. Vu, E. Dutkiewicz, and S. Chatzinotas, “Defeating super-reactive jammers with deception strategy: Modeling, signal detection, and performance analysis”, at https://arxiv.org/abs/2105.01308.

Intelligent wireless resource allocation

  • M.-Y. Lee, Y.-H. Xiong, G.-D. Yu, and Y. G. Li, “Deep neural networks for linear sum assignment problems”, IEEE Wireless Communications Letters, vol. 7, no. 6, pp. 962 – 965, December 2018.
  • 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.
  • M.-Y. Lee, G.-D. Yu, and G. Y. Li, “Learning to branch: Accelerating resource allocation in wireless networks”, IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 958 - 970, January 2020.
  • 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. 
  • J.-C. Shi, W.-N. Wang, X.-P. Yi, J.-H. Wang, X.-Q. Gao, Q. Liu, and G. Y. Li, “Learning to compute ergodic rate for multi-cell scheduling massive MIMO”, IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 785-797, February 2021. 
  • 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.
  • N.-V. Huynh, D.-T. Hoang, D. Nguyen, and E. Dutkiewicz, “Optimal and fast real-time resource slicing with deep dueling neural networks”, IEEE Journal on Selected Areas in Communications, vol. 37, no. 6, pp. 1455-1470, June 2019.

Distributed learning and federated learning

  • N.-V. Huynh, D.-T. Hoang, D.-N. Nguyen, and E. Dutkiewicz, “Joint coding and scheduling optimization for distributed learning over wireless edge networks”, at https://arxiv.org/abs/2103.04303.

Other topics

  • 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. 
  • W.-Q. Wu, X.-Q. Gao, C. Sun, and G. Y. Li, “Shallow underwater acoustic massive MIMO communications”, IEEE Transactions on Signal Processing, vol. 20, no. 2, pp. 1124-1139, 2021.
  • J.-K. Ren, G.-D. Yu, Y.-H. He, and G. Y. Li, “Collaborative cloud and edge computing for latency minimization”, IEEE Transactions on Vehicular Technology, vol. 68, no. 5, pp. 5031-5044, May 2019. 
  • Q.-Y. Hu, Y.-L. Cai, G.-D. Yu, Z.-J. Qin, M.-J. Zhao, and G. Y. Li, “Joint offloading and trajectory design for UAV-enabled mobile edge computing systems”, IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1897-1892, April 2019. 
  • Y.-C. Liang, Q.-Q. Zhang, E. G. Larsson, and G. Y. Li, “Symbiotic radio: Cognitive backscattering communications for future wireless networks”, IEEE Transactions on Cognitive Communications and Networking, vol. 4, no. 6, pp. 1242-1255, December 2020. 
  • N. V. Huynh, D. N. Nguyen, D. T. Hoang, and E. Dutkiewicz, “Jam me if you can: Defeating jammer with deep dueling neural network architecture and ambient backscattering augmented communications”, IEEE Journal on Selected Areas in Communications, vol. 37, no. 11, pp. 2603-2620, November 2019.