Le Liang

For a complete list of publications, please see Google Scholar.
We open source our code on GitHub.

Most recent work

• Beam prediction based on large language models
  Y. Sheng, K. Huang, L. Liang, H. Ye, S. Jin, and G. Y. Li, to appear in IEEE Wireless Communications Letters, 2025.

• On purely data-driven massive MIMO detectors
  H. Ye and L. Liang, arXiv preprint arXiv:2401.07515, 2024.

• Generative Diffusion Models for High Dimensional Channel Estimation
  X. Zhou, L. Liang, J. Zhang, P. Jiang, Y. Li, and S. Jin, arXiv preprint arXiv:2408.10501, 2024.

Magazine and overview papers

• AI Empowered Wireless Communications: From Bits to Semantics
  Z. Qin, L. Liang, Z. Wang, S. Jin, X. Tao, W. Tong, and G. Y. Li, Proceedings of the IEEE, vol. 112, no. 7, pp. 621-652, Jul. 2024.

• Age of information, latency, and reliability in intelligent vehicular networks
  C. Guo, X. Wang, L. Liang, and G. Y. Li, IEEE Network, vol. 37, no. 6, pp. 109-116, Nov. 2023.

• Deep-learning-based wireless resource allocation with application to vehicular networks
  L. Liang, H. Ye, G. Yu, and G. Y. Li, Proceedings of the IEEE, vol. 108, no. 2, pp. 341-356, Feb. 2020.

• Toward intelligent vehicular networks: A machine learning framework
  L. Liang, H. Ye, and G. Y. Li, IEEE Internet of Things Journal, vol. 6, no. 1, pp. 124–135, Feb. 2019.

• Vehicular communications: A network layer perspective
  H. Peng, L. Liang, X. Shen, and G. Y. Li, IEEE Transactions on Vehicular Technology, vol. 68, no. 2, pp. 1064–1078, Feb. 2019.

• Machine learning for vehicular networks: Recent advances and application examples
  H. Ye, L. Liang, G. Y. Li, J. Kim, L. Lu, and M. Wu, IEEE Vehicular Technology Magazine, vol. 13, no. 2, pp. 94–101, Jun. 2018.

• Vehicular communications: A physical layer perspective
  L. Liang, H. Peng, G. Y. Li, and X. Shen, IEEE Transactions on Vehicular Technology, vol. 66, no. 12, pp. 10647–10659, Dec. 2017.

Journal papers

2025

• Near-optimal MIMO detection using gradient-based MCMC in discrete spaces
  X. Zhou, L. Liang, J. Zhang, C.-K. Wen, and S. Jin, IEEE Transactions on Signal Processing, vol. 73, pp. 584-600, 2025.

• ISAC prototype system for multi-domain cooperative communication networks
  J. Yang, H. Que, T. Du, L. Liang, X. Li, C.-K. Wen, and S. Jin, IEEE Wireless Communications Letters, vol. 14, no. 1, pp. 108-112, Jan. 2025.

• Mini-batch gradient-based MCMC for decentralized massive MIMO detection
  X. Zhou, L. Liang, J. Zhang, C.-K. Wen, and S. Jin, IEEE Transactions on Communications, vol. 73, no. 1, pp. 677-692, Jan. 2025.

2024

• Efficient statistical linear precoding for downlink massive MIMO systems
  Z. Wang, L. Liang, S. Lyu, Y. Xia, Y. Huang, D. W. K. Ng, IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 14805-14818, Oct. 2024.

• Multi-agent DRL approach to two-timescale transmission for RIS-aided MU-MISO systems
  Y. Wang, H. Zhang, X. Li, L. Liang, M. Matthaiou, and S. Jin, IEEE Wireless Communications Letters, vol. 13, no. 10, pp. 2697-2701, Oct. 2024.

• Random watermark hopping for enhanced security and communication performance in physical layer authentication
  Y. Ma, H. Fang, L. Liang, and X. Wang, IEEE Internet of Things Journal, vol. 11, no. 20, pp. 32998-33009, Oct. 2024.

• Gradient-based Markov chain Monte Carlo for MIMO detection
  X. Zhou, L. Liang, J. Zhang, C.-K. Wen, and S. Jin, IEEE Transactions on Wireless Communications, vol. 23, no. 7, pp. 7566-7581, Jul. 2024.

• SAFARI: Sparsity-enabled federated learning with limited and unreliable communications
  Y. Mao, Z. Zhao, M. Yang, L. Liang, Y. Liu, W. Ding, T. Lan, and X.-P. Zhang, IEEE Transactions on Mobile Computing, vol. 23, no. 5, pp. 4819-4831, May 2024.

• Universal model-driven deep learning for MIMO transceiver design
  J. Zhang, C.-K. Wen, L. Liang, and S. Jin, IEEE Communications Magazine, vol. 62, no. 4, pp. 74-80, Apr. 2024.

• Semantic communication for cooperative perception based on importance map
  Y. Sheng, H. Ye, L. Liang,S. Jin, and G. Y. Li, Journal of the Franklin Institute, vol. 361, no. 6, pp. 1-16, Apr. 2024.

• AoI-driven power allocation and batch sampling control for V2V status update communications
  C. Guo, S. Liu, B. Liao, Z. Wang, and L. Liang, IEEE Transactions on Industrial Informatics, vol. 20, no. 1, pp. 291-302, Jan. 2024.

2023

• Reinforcement learning based power control for reliable mission-critical wireless transmission
  C. Guo, Z. Li, L. Liang, and G. Y. Li, IEEE Internet of Things Journal, vol. 10, no. 23, pp. 20868-20883, Dec. 2023.

• Mean-field aided multi-agent reinforcement learning for resource allocation in vehicular networks
  H. Zhang, C. Lu, H. Tang, X. Wei, L. Liang, L. Cheng, W. Ding, and Z. Han, IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2667-2679, Feb. 2023.

2022

• Coverage control for UAV swarm communication networks: A distributed learning approach
  N. Gao, L. Liang, D. Cai, X. Li, and S. Jin, IEEE Internet of Things Journal, vol. 9, no. 20, pp. 19854-19867, Oct. 2022.

• Federated edge learning for the wireless physical layer: Opportunities and challenges
  Y. Cui, J. Guo, X. Li, L. Liang, and S. Jin, China Communications, vol. 19, no. 8, pp. 15-30, Aug. 2022.

• Decentralized federated learning with unreliable communications [Code]
  H. Ye, L. Liang, and G. Y. Li, IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 3, pp. 487-500, Apr. 2022.

2021

• A lightweight deep network for efficient CSI feedback in massive MIMO systems
  Y. Sun, W. Xu, L. Liang, N. Wang, and G. Y. Li, IEEE Wireless Communication Letters, vol. 10, no. 8, pp. 1840-1844, Aug. 2021.

• A vector processor for mean field Bayesian channel estimation
  D. Dasalukunte, R. Dorrance, L. Liang, and L. Lu, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 29, no. 7, pp. 1348-1359, July 2021.

2020

• Learn to compress CSI and allocate resources in vehicular networks [Code]
  L. Wang, H. Ye, L. Liang, and G. Y. Li, IEEE Transactions on Communications, vol. 68, no. 6, pp. 3640-3653, Jun. 2020

• Deep learning based end-to-end wireless communication systems with conditional GAN as unknown channel [Code]
  H. Ye, L. Liang, G. Y. Li, and B.-H. Juang, IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 3133-3143, May 2020.

2019

• Spectrum sharing in vehicular networks based on multi-agent reinforcement learning [Code]
  L. Liang, H. Ye, and G. Y. Li, IEEE Journal on Selected Areas in Communications, vol. 37, no. 10, pp. 2282-2292, Oct. 2019.

• Resource allocation for V2X communications: A large deviation theory perspective
  C. Guo, L. Liang, and G. Y. Li, IEEE Wireless Communications Letters, vol. 8, no. 4, pp. 1108–-1111, Aug. 2019.

• Resource allocation for vehicular communications with low latency and high reliability
  C. Guo, L. Liang, and G. Y. Li, IEEE Transactions on Wireless Communications, vol. 18, no. 8, pp. 3887–3902, Aug. 2019.

• Resource allocation for high-reliability low-Latency vehicular communications with packet retransmission
  C. Guo, L. Liang, and G. Y. Li, IEEE Transactions on Vehicular Technology, vol. 68, no. 7, pp. 6219–6230, Jul. 2019.

• Resource allocation for low-latency vehicular communications: An effective capacity perspective
  C. Guo, L. Liang, and G. Y. Li, IEEE Journal on Selected Areas in Communications, vol. 37, no. 4, pp. 905–-917, Apr. 2019.

2018

• Framework of channel estimation for hybrid analog-and-digital processing enabled massive MIMO communications
  L. Pan, L. Liang, W. Xu, and X. Dong, IEEE Transactions on Communications, vol. 66, no. 9, pp. 3902–3915, Sep. 2018.

• Graph-based resource sharing in vehicular communication [Code]
  L. Liang, S. Xie, G. Y. Li, Z. Ding, and X. Yu, IEEE Transactions on Wireless Communications, vol. 17, no. 7, pp. 4579–-4592, Jul. 2018.

2017

• Spectrum and power allocation for vehicular communications with delayed CSI feedback [Code]
  L. Liang, J. JoonBeom, S. C. Jha, K. Sivanesan, and G. Y. Li, IEEE Wireless Communications Letters, vol. 6, no. 4, pp. 458-–461, Aug. 2017.

• Resource allocation for D2D-enabled vehicular communications [Code]
  L. Liang, G. Y. Li, W. Xu, IEEE Transactions on Communications, vol. 65, no. 7, pp. 3186–-3197, Jul. 2017.

2014 and earlier

• Low-complexity hybrid precoding in massive multiuser MIMO systems [Code]
  L. Liang, W. Xu, and X. Dong, IEEE Wireless Communications Letters, vol. 3, no. 6, pp. 653–656, Dec. 2014.

• On coordinated multi-point transmission with partial channel state information via delayed feedback
  W. Xu and L. Liang, Wireless Personal Communications, vol. 75, no. 4, pp. 2103–2119, Apr. 2014.

• Limited feedback-based multi-antenna relay broadcast channels with block diagonalization
  L. Liang, W. Xu, and X. Dong, IEEE Transactions on Wireless Communications, vol. 12, no. 8, pp. 4092–4101, Aug. 2013.

Conference papers

• Deep learning-based LOS and NLOS parameters estimation, tracking, and identification
  K. Chen, J. Wang, J. Yang, L. Liang, C.-K. Wen, and S. Jin, in Proc. IEEE International Conference on Communications in China (ICCC), 2024, pp. 1–6.

• Decentralized massive MIMO detection using Mini-batch gradient-based MCMC
  X. Zhou, L. Liang, J. Zhang, C.-K. Wen, and S. Jin, in Proc. IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2024, pp. 1–5.

• GRLinQ: A Distributed link scheduling mechanism with graph reinforcement learning
  Z. Shan, X. Yi, L. Liang, C.-S. Liao, and S. Jin, in Proc. IEEE International Symposium on Information Theory (ISIT), 2024, pp. 1–7.

• Joint radar-communication beamforming for CRB-based target localization
  T. Mao, J. Yang, L. Liang, and S. Jin, in Proc. IEEE Vehicular Technology Conference (VTC), 2024, pp. 1–7.

• MIMO detection using gradient-based Markov chain Monte Carlo methods
  X. Zhou, L. Liang, J. Zhang, and S. Jin, in Proc. of IEEE Global Communications Conference (GLOBECOM), 2023, pp. 1–6.

• Sparse graph neural networks for two-timescale wireless resource allocation
  K. Wang, H. Ye, L. Liang, and S. Jin, in Proc. of IEEE Global Communications Conference (GLOBECOM) Workshop, 2023, pp. 1-6.

• Semantic communication for cooperative perception based on the importance map
  Y. Sheng, H. Ye, L. Liang, and S. Jin, in Proc. of International Conference on Wireless Communications and Signal Processing (WCSP), 2023, pp. 1–6.

• Pilot-free semantic communication systems for frequency-selective fading channels
  Z. Cao, H. Zhang, L. Liang, and H. Wang, in Proc. of IEEE International Conference on Communications (ICC) Workshop, 2023, pp. 1–6.

• Joint spectrum allocation and power control in vehicular networks based on reinforcement learning
  K. Wang, Y. Feng, L. Liang, and S. Jin, in Proc. of International Symposium on Wireless Communications Systems (ISWCS), 2022, pp. 1-6.

• A multi-task semantic communication system for natural language processing
  Y. Sheng, F. Li, L. Liang, and S. Jin, in Proc. of IEEE Vehicular Technology Conference (VTC), 2022, pp. 1 - 5.

• Fast spectrum sharing in vehicular networks: A meta reinforcement learning approach
  K. Huang, Z. Luo, L. Liang, and S. Jin, in Proc. of IEEE Vehicular Technology Conference (VTC), 2022, pp. 1-5.

• Circular convolutional auto-encoder for channel coding
  H. Ye, L. Liang, and G. Y. Li, in Proc. of IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019, pp. 1–5.

• Multi-agent reinforcement learning for spectrum sharing in vehicular networks
  L. Liang, H. Ye, and G. Y. Li, in Proc. of IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2019, pp. 1–5.

• Resource allocation for low-latency vehicular communications with packet retransmissions
  C. Guo, L. Liang, and G. Y. Li, in Proc. of IEEE Global Communications Conference (GLOBECOM), 2018, pp. 1–-6.

• Graph-based radio resource management for vehicular networks
  L. Liang, S. Xie, G. Y. Li, Z. Ding, and X. Yu, in Proc. of IEEE International Conference on Communications (ICC), 2018, pp. 1–6.

• Meeting different QoS requirements of vehicular networks: A D2D-based approach
  L. Liang, G. Y. Li, W. Xu, in Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 3734–3738.

• How to approach zero-forcing under RF chain limitations in large mmWave multiuser systems?
  L. Liang, Y. Dai, W. Xu, and X. Dong, in Proc. of IEEE International Conference on Communications in China (ICCC), 2014, pp. 518–522.

• Performance enhanced transmission in device-to-device communications: Beamforming or interference cancellation?
  W. Xu, L. Liang, S. Jin, J. Li, and M. Lei, in Proc. of IEEE Global Communications Conference (GLOBECOM), 2012, pp. 4296–4301.

• Adaptive coordinated multi-point transmission based on delayed limited feedback
  L. Liang, W. Xu, and H. Zhang, in Proc. of IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications (PIMRC), 2012, pp. 2335–2340.

• Approaching downlink capacity of multiplexing relay channels with imperfect channel information
  W. Xu and L. Liang, in Proc. of 4th International High Speed Intelligent Communication Forum (HSIC), 2012, pp. 136–138.

Theses

• Resource allocation for vehicular communications (Ph.D. Dissertation, Georgia Institute of Technology, 2018)

• Practical precoding design for modern multiuser MIMO communications (M.A.Sc Thesis, University of Victoria, 2015)

• Cooperative communications in MIMO systems (in Chinese) (B.E. Thesis, Southeast University, 2012)