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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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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Abstract 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.
AbstractList 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.
Author Wu, Minghui
Han, Ziqi
Zhang, Hengwei
Qiao, Li
Liu, Ling
Gao, Zhen
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Snippet This letter proposes a deep-learning (DL)-based multi-user channel state information (CSI) feedback framework for massive multiple-input multiple-output...
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SubjectTerms 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
Title Residual Cross-Attention Transformer-Based Multi-User CSI Feedback With Deep Joint Source-Channel Coding
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https://www.proquest.com/docview/3246349286
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