Residual Cross-Attention Transformer-Based Multi-User CSI Feedback with Deep Joint Source-Channel Coding
This work addresses feedback efficiency for wireless communication systems, but it is incremental as it builds on existing deep-learning methods for CSI feedback.
The paper tackles the problem of multi-user channel state information (CSI) feedback in massive MIMO systems by proposing a deep-learning framework that uses deep joint source-channel coding and a residual cross-attention transformer to improve reconstruction accuracy and reduce feedback overhead, achieving superior performance with low complexity and better scalability.
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.