Residual Cross-Attention Transformer-Based Multi-User CSI Feedback With Deep Joint Source-Channel Coding

This letter proposes a deep-learning (DL)-based multi-user channel state information (CSI) feedback framework for massive multiple-input multiple-output systems, where the deep joint source-channel coding (DJSCC) is utilized to improve the CSI reconstruction accuracy. Specifically, we design a multi...

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Bibliographic Details
Published in:IEEE wireless communications letters Vol. 14; no. 8; pp. 2481 - 2485
Main Authors: Zhang, Hengwei, Wu, Minghui, Qiao, Li, Liu, Ling, Han, Ziqi, Gao, Zhen
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Coding
Correlation
Decoding
deep joint source-channel coding (DJSCC)
Delays
Downlink
Encoding
Feature extraction
Feedback
massive multiple-input multiple-output (MIMO)
multi-user CSI feedback
Noise levels
Residual cross-attention
Training
Transformers
Uplink
Vectors
ISSN:2162-2337, 2162-2345
Online Access:Get full text
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Summary:This letter proposes a deep-learning (DL)-based multi-user channel state information (CSI) feedback framework for massive multiple-input multiple-output systems, where the deep joint source-channel coding (DJSCC) is utilized to improve the CSI reconstruction accuracy. Specifically, we design a multi-user joint CSI feedback framework, whereby the CSI correlation of nearby users is utilized to reduce the feedback overhead. Under the framework, we propose a new residual cross-attention transformer architecture, which is deployed at the base station to further improve the CSI feedback performance. Moreover, to tackle the "cliff-effect" of conventional bit-level CSI feedback approaches, we integrated DJSCC into the multi-user CSI feedback, together with utilizing a two-stage training scheme to adapt to varying uplink noise levels. Experimental results demonstrate the superiority of our methods in CSI feedback performance, with low network complexity and better scalability.
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content type line 14
ISSN:2162-2337
2162-2345
DOI:10.1109/LWC.2025.3574011