You can also find my articles on my Google Scholar profile
Overview
[1] J. Guo, C.-K. Wen, S. Jin*, and G. Y. Li, “Overview of Deep Learning-Based CSI Feedback in Massive MIMO Systems,” IEEE Trans. Commun., vol. 70, no. 12, pp. 8017–8045, Dec. 2022. (This paper gives a comprehensive overview of state-of-the-art research on deep learning-based CSI feedback.)
[2] J. Guo, C.-K. Wen, S. Jin, and X. Li, “AI for CSI feedback enhancement in 5G-Advanced,” IEEE Wireless Commun., vol. 31, no. 3, pp. 169–176, Jun. 2024. (This paper gives a comprehensive overview of the recent work about AI-based CSI feedback in 3GPP standardization.)
[3] Q. Xue, J. Guo*, B. Zhou, Y. Xu, Z. Li and S. Ma, “AI/ML for Beam Management in 5G-Advanced: A Standardization Perspective,” IEEE Vehicular Technology Magazine, Early access, 2024. (This paper gives a comprehensive overview of the recent work about AI-based beam management in 3GPP standardization.)
[4] J. Guo, C.-K. Wen, and S. Jin*,”AI-Native Air Interface.” Fundamentals of 6G Communications and Networking. Cham: Springer International Publishing, 2023. 143-163. (This book chapter gives a comprehensive overview of AI for end-to-end communications and single-module enhancement.)
Technical paper
Integrated Sensing and Communications (ISAC)
[1] J. Guo, Y. Lv, C.-K. Wen, X. Li, and S. Jin*, “Learning-based Integrated CSI Feedback and Localization in Massive MIMO,” IEEE Transactions on Wireless Communications, Early access, 2024. (This paper introduces an integrated learning framework for CSI feedback and localization designed to synergistically improve both tasks.)
Deployment of AI-based CSI feedback
[1] J. Guo, S. Ma, C.-K. Wen, and S. Jin*, “Performance Monitoring-enabled Reliable AI-based CSI Feedback,”IEEE Trans. Wireless Commun., 2024. (This paper introduces performance monitoring for reliable AI-based CSI feedback.)
[2] J. Guo, J. Wang, C.-K. Wen, S. Jin*, and G. Y. Li, “Compression and acceleration of neural networks for communications,” IEEE Wireless Commun., vol. 27, no. 4, pp. 110–117, Aug. 2020. (This paper introduces some novel neural network compression techniques to reduce the complexity of AI-based air interface.)
[3] J. Guo, C.-K. Wen, M. Chen, and S. Jin*, “Environment knowledge-aided massive MIMO feedback codebook enhancement using artificial intelligence,” IEEE Trans. Commun., vol. 70, no. 7, pp. 4527–4542, Jul. 2022. (This paper introduces an one-sided AI-based CSI feedback with no requirements in changing the current feedback standard.)
[4] J. Guo, 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 Trans. Wireless Commun., vol. 19, no. 4, pp. 2827-2840, Apr. 2020. (This paper introduces a novel quantization framework and two variable-rate feedback approaches.)
Introducing correlations to AI-based CSI feedback
[1] J. Guo, C.-K. Wen and S. Jin*, “CAnet: Uplink-aided downlink channel acquisition in FDD massive MIMO using deep learning”, IEEE Trans. Commun., vol. 70, no. 1, pp. 199-214, Jan. 2022. (This paper proposes a uplink-aided downlink channel acquisition framework.)
[2] J. Guo, Y. Zuo, C. -K. Wen and S. Jin*, “User-Centric Online Gossip Training for Autoencoder-Based CSI Feedback,” in IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 3, pp. 559-572, April 2022. (This paper utilizes the spatial correlation to improve feedback accuracy by user-centric online learning.)
Multi-module joint design supported by AI
[1] J. Guo, C. -K. Wen and S. Jin*, “Deep Learning-Based CSI Feedback for Beamforming in Single- and Multi-Cell Massive MIMO Systems,” in IEEE Journal on Selected Areas in Communications, vol. 39, no. 7, pp. 1872-1884, July 2021. (This paper proposes a DL-based CSI feedback framework for beamforming design, called CsiFBnet. The key idea of the CsiFBnet is to maximize the beamforming performance gain rather than the feedback accuracy. )
[2] J. Guo, T. Chen, S. Jin*, G. Y. Li, X. Wang, and X. Hou “Deep learning for joint channel estimation and feedback in massive MIMO systems,” Digital Communications and Networks, vol. 10, no. 1, pp. 83-93, Jan. 2024. (This paper proposes a deep learning based joint channel estimation and feedback framework, which comprehensively realizes the estimation, compression, and reconstruction of downlink channels in FDD massive MIMO systems.)
Some new scenarios
[1] J. Guo, C. -K. Wen and S. Jin*, “Eliminating CSI Feedback Overhead via Deep Learning-Based Data Hiding,” in IEEE Journal on Selected Areas in Communications, vol. 40, no. 8, pp. 2267-2281, Aug. 2022.(The key idea of this paper is to hide/superimpose CSI in transmitted messages (e.g., images) with no transmission resource occupation and few effects on message semantics.)
[2] J. Guo, W. Chen, C. -K. Wen and S. Jin*, “Deep Learning-Based Two-Timescale CSI Feedback for Beamforming Design in RIS-Assisted Communications,” in IEEE Transactions on Vehicular Technology, vol. 72, no. 4, pp. 5452-5457, April 2023.(This letter proposes a deep learning-based two-timescale CSI feedback framework called RIS-CsiNet for beamforming design and discrete phase shift design in RIS-assisted systems.)