Enhancing Deep Learning-Based CSI Feedback in Noisy Channels with a Soft Variational Approach
Shuojun Lyu, Zhenyu Liu, Zhuohang Han, Jincheng Dai · 2024
Deep learning (DL)-based channel state information (CSI) feedback holds substantial promise for boosting spectrum efficiency in massive MIMO systems. However, prevailing studies often treat compressed CSI bits uniformly, assuming their accurate transmission over noisy channels. Such assumptions falter when confronted with bandwidth limitations or low signal-to-noise ratios (SNRs), significantly impairing CSI reconstruction quality. In this paper, we introduce a novel soft variational approach to implement deep joint source-channel coding for CSI feedback within noisy environments, conceptualizing the system as an end-to-end rate-distortion (RD) optimization framework. Specifically, our model utilizes a nonlinear transform to extract the latent representation of CSI and employs an entropy model as a prior to guide the variable-rate transmission of latent features across noisy channels. Experimental results demonstrate that our approach achieves substantial improvements over benchmark schemes, saving 40% in feedback bandwidth under normalized mean squared error (NMSE) and ensuring the robustness to varying wireless channels.