Residual Diffusion Models for Variable-Rate Joint Source Channel Coding of MIMO CSI

TL;DR

This paper introduces RD-JSCC, a novel joint source-channel coding framework combining autoencoders and residual diffusion to improve MIMO CSI transmission robustness and scalability in challenging wireless environments.

Contribution

The paper proposes Residual-Diffusion Joint Source-Channel Coding (RD-JSCC), integrating autoencoders with residual diffusion for enhanced CSI compression and transmission under variable channel conditions.

Findings

RD-JSCC outperforms existing autoencoder-based methods in challenging environments.

Supports multiple compression rates with a single model.

Offers robustness to quantization and imperfect channel estimation.

Abstract

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.