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

• URL: http://arxiv.org/abs/2505.19465v1
• Date: Mon, 26 May 2025 03:38:08 GMT
• Title: Residual Cross-Attention Transformer-Based Multi-User CSI Feedback with Deep Joint Source-Channel Coding
• Authors: Hengwei Zhang, Minghui Wu, Li Qiao, Ling Liu, Ziqi Han, Zhen Gao,
• Abstract summary: This letter proposes a deep-learning (DL)-based multi-user channel state information (CSI) feedback framework for massive multiple-input multiple-output systems.<n>We design a multi-user joint CSI feedback framework, whereby the CSI correlation of nearby users is utilized to reduce the feedback overhead.<n> Experimental results demonstrate the superiority of our methods in CSI feedback performance, with low network complexity and better scalability.
• Score: 11.692049253618546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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