Despite significant advancements in deep learning based CSI compression, some key limitations remain unaddressed. Current approaches predominantly treat CSI compression as a source-coding problem, thereby neglecting transmission errors. Conventional separate source and channel coding suffers from the cliff effect, leading to significant deterioration in reconstruction performance under challenging channel conditions. While existing autoencoder-based compression schemes can be readily extended to support joint source-channel coding, they struggle to capture complex channel distributions and exhibit poor scalability with increasing parameter count. To overcome these inherent limitations of autoencoder-based approaches, we propose Residual-Diffusion Joint Source-Channel Coding (RD- JSCC), a novel framework that integrates a lightweight autoencoder with a residual diffusion module to iteratively refine CSI reconstruction. Our flexible decoding strategy balances computational efficiency and performance by dynamically switching between low-complexity autoencoder decoding and sophisticated diffusion-based refinement based on channel conditions. Comprehensive simulations demonstrate that RD-JSCC significantly outperforms existing autoencoder-based approaches in challenging wireless environments. Furthermore, RD-JSCC offers several practical features, including a low-latency 2-step diffusion during inference, support for multiple compression rates with a single model, robustness to fixed-bit quantization, and adaptability to imperfect channel estimation.

翻译：尽管基于深度学习的CSI压缩已取得显著进展，但一些关键局限性仍未得到解决。现有方法主要将CSI压缩视为信源编码问题，从而忽略了传输误差。传统的分离式信源信道编码存在悬崖效应，导致在恶劣信道条件下重建性能急剧恶化。虽然现有基于自编码器的压缩方案可轻松扩展以支持联合信源信道编码，但其难以捕捉复杂信道分布，且随着参数数量增加表现出较差的可扩展性。为克服基于自编码器方法的这些固有局限，我们提出残差扩散联合信源信道编码（RD-JSCC）——一种将轻量级自编码器与残差扩散模块相结合的新型框架，通过迭代优化CSI重建。我们的灵活解码策略通过在低复杂度自编码器解码与基于信道条件的精细化扩散优化之间动态切换，实现了计算效率与性能的平衡。综合仿真表明，在挑战性无线环境中，RD-JSCC显著优于现有基于自编码器的方法。此外，RD-JSCC具备多项实用特性：推理期间的低延迟两步扩散、单模型支持多压缩率、对固定比特量化的鲁棒性，以及对非理想信道估计的适应性。