用深度联合编码提升多用户信道反馈精度，降低开销。
Residual Cross-Attention Transformer-Based Multi-User CSI Feedback with Deep Joint Source-Channel Coding
- 基于残差交叉注意力的Transformer架构，利用用户间相关性压缩反馈数据。
- 结合双阶段训练与深耦合编解码，在不同噪声下实现更稳定重建。
- 适合大规模MIMO系统，对实际部署友好且可扩展性强。
本文提出一种面向大规模多输入多输出系统的多用户信道状态信息（CSI）反馈框架，采用深度联合源信道编码（DJSCC）提升重建精度。设计了多用户联合反馈机制，利用邻近用户间的CSI相关性降低反馈开销。在此框架下，提出一种新型残差交叉注意力Transformer架构，部署于基站以进一步优化反馈性能。为克服传统比特级反馈的‘悬崖效应’，将DJSCC融入多用户反馈，并采用两阶段训练策略以适应不同上行噪声水平。实验结果表明，该方法在保持低网络复杂度的同时，显著提升了CSI反馈性能，具备更强的可扩展性。
原文摘要 · Abstract (English)
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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