Deep CSI Compression and Feedback for Massive MIMO: A Survey

doi:10.12142/ZTECOM.202601003

ZTE Communications ›› 2026, Vol. 24 ›› Issue (1): 4-15.DOI: 10.12142/ZTECOM.202601003

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Deep CSI Compression and Feedback for Massive MIMO: A Survey

Lu Zhaohua¹^,², Yi Chenyang³, Wu Jie³, Shao Bo³, Xu Wei³^,⁴()

^1.State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen 518055, China
^2.ZTE Corporation, Shenzhen 518057, China
^3.National Mobile Communications Research Laboratory, Southeast University, Nanjing 211189, China
^4.Purple Mountain Laboratories, Nanjing 211111, China

Received:2024-09-20 Online:2026-03-17 Published:2026-03-17
About author:Lu Zhaohua received his BS degree in electrical engineering and PhD degree in signal processing from Tianjin University, China in 2001 and 2006, respectively. Since 2006, he has been engaged in mobile communication physical layer technology at ZTE Corporation, including MIMO, interference control, artificial intelligence, etc. He has published more than 30 papers and held over 200 authorized patents.
Yi Chenyang received her BS degree in electrical engineering from Southeast University, China in 2020. She is currently working toward her PhD degree with the School of Information Science and Engineering, National Mobile Communications Research Laboratory, Southeast University. Her current research interests include massive MIMO, mmWave communications, and artificial intelligence for wireless communications.
Wu Jie received his BS degree in electrical engineering from Southeast University, China in 2022, where he is currently pursuing his MS degree in communication and information engineering. His recent research interests include deep learning for CSI compression and feedback in wireless communications.
Shao Bo received his BS degree in electrical engineering from Xidian University, China in 2023. He is currently pursuing his MS degree in communication and information engineering at Southeast University, China. His recent research interests include deep learning for CSI compression and feedback in wireless communications and massive MlMO systems.
Xu Wei (wxu@seu.edu.cn) received his BS degree in electrical engineering and his MS and PhD degrees in communication and information engineering from Southeast University, China in 2003, 2006, and 2009, respectively. Between 2009 and 2010, he was a post-doctoral research fellow with the Department of Electrical and Computer Engineering, University of Victoria, Canada. He is currently a professor at the National Mobile Communications Research Laboratory, Southeast University. He was an adjunct professor of the University of Victoria, Canada from 2017 to 2020, and a distinguished visiting fellow of the Royal Academy of Engineering, UK in 2019. He has co-authored over 100 refereed journal papers in addition to 36 domestic patents and four US patents granted. His research interests include information theory, signal processing and machine learning for wireless communications. He is currently an editor of IEEE Transactions on Communications and a senior editor of IEEE Communications Letters. He received the Best Paper Awards from a number of prestigious IEEE conferences including IEEE Globecom/ICCC, etc. He received the Science and Technology Award for Young Scholars of the Chinese Institute of Electronics in 2018. He is an IEEE Fellow and IET Fellow.
Supported by:
ZTE Industry?University?Institute Cooperation Funds(IA20240319003);the NSFC(62571112)

Abstract

Abstract:

To achieve the potential performance gain of massive multiple-input multiple-output (MIMO) systems, base stations (BS) require downlink channel state information (CSI) fed back by users to execute beamforming design, especially in the frequency division duplex (FDD) systems. However, due to the enormous number of antennas in massive MIMO systems, the feedback overhead of downlink CSI acquisition is extremely large. To address this issue, deep learning (DL) techniques have been introduced to develop high-accuracy feedback strategies under limited backhaul constraints. In this paper, we provide an overview of DL-based CSI compression and feedback approaches in massive MIMO systems. Specifically, we introduce the conventional CSI compression and feedback schemes and the existing problems. Besides, we elaborate on various DL techniques employed in CSI compression from the perspective of network architecture and analyze the advantages of different techniques. We also enumerate the applications of DL-based methods for solving practical challenges in CSI compression and feedback. In addition, we brief the remaining issues in deep CSI compression and indicate potential directions in future wireless networks.

Key words: deep learning, MIMO, CSI compression, limited feedback, FDD system

Lu Zhaohua, Yi Chenyang, Wu Jie, Shao Bo, Xu Wei. Deep CSI Compression and Feedback for Massive MIMO: A Survey[J]. ZTE Communications, 2026, 24(1): 4-15.

Figures/Tables 5

References 59

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Complexity		1/4	1/8	1/16	1/32
Time complexity	Ref. [17]	21 659 648	5 668 864	3 571 712	2 523 136
	Ref. [20]	121 708 544	97 591 296	86 319 104	80 879 616
	Ref. [21]	-	-	-	-
Space complexity	Ref. [17]	2 103 904	1 055 072	530 656	268 448
	Ref. [20]	28 326 904	22 296 312	19 477 624	18 117 432
	Ref. [21]	10 247 148	-	7 484 688	7 024 272

Complexity		1/4	1/8	1/16	1/32
Time complexity	Ref. [17]	21 659 648	5 668 864	3 571 712	2 523 136
	Ref. [20]	121 708 544	97 591 296	86 319 104	80 879 616
	Ref. [21]	-	-	-	-
Space complexity	Ref. [17]	2 103 904	1 055 072	530 656	268 448
	Ref. [20]	28 326 904	22 296 312	19 477 624	18 117 432
	Ref. [21]	10 247 148	-	7 484 688	7 024 272

Advantages	Key Techniques	References
Network lightweighting	Design an innovative structure of multi-branch convolutions	Ref. [26]
Network lightweighting	Exploit the similarity of real and imaginary parts of CSI	Refs. [27–29]
Performance improvement	Exploit the sparse characteristics of CSI	Refs. [30–31]
	Exploit the image characteristics of the CSI matrix	Refs. [32–33]
	Extract CSI features based on physical propagation environment	Refs. [34–35]
Generalization enhancement	Adopt model-agnostic meta-learning approaches	Refs. [36–38]
	Adopt deep transfer learning techniques	Refs. [39–40]
	Adopt interactive federated and transfer learning	Ref. [41]
End-to-end design	Joint framework of pilot design, CSI feedback, and precoding	Refs. [42–43]
	Consider adaptive pilot length for mm-wave MIMO systems	Ref. [44]
	Design a dual-timescale network to reduce signaling overhead	Ref. [45]

Advantages	Key Techniques	References
Network lightweighting	Design an innovative structure of multi-branch convolutions	Ref. [26]
Network lightweighting	Exploit the similarity of real and imaginary parts of CSI	Refs. [27–29]
Performance improvement	Exploit the sparse characteristics of CSI	Refs. [30–31]
	Exploit the image characteristics of the CSI matrix	Refs. [32–33]
	Extract CSI features based on physical propagation environment	Refs. [34–35]
Generalization enhancement	Adopt model-agnostic meta-learning approaches	Refs. [36–38]
	Adopt deep transfer learning techniques	Refs. [39–40]
	Adopt interactive federated and transfer learning	Ref. [41]
End-to-end design	Joint framework of pilot design, CSI feedback, and precoding	Refs. [42–43]
	Consider adaptive pilot length for mm-wave MIMO systems	Ref. [44]
	Design a dual-timescale network to reduce signaling overhead	Ref. [45]

Deep CSI Compression and Feedback for Massive MIMO: A Survey

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