CDDM: Channel Denoising Diffusion Models for Wireless Semantic Communications

TL;DR

This paper introduces channel denoising diffusion models (CDDM) as a novel physical layer module in wireless semantic communications, effectively reducing noise and improving image transmission quality.

Contribution

The paper proposes CDDM, a new diffusion-based noise mitigation method for wireless channels, with tailored training algorithms and theoretical validation for semantic communication systems.

Findings

CDDM reduces the mean square error (MSE) after equalization.

The joint CDDM and JSCC system outperforms traditional methods.

CDDM effectively decreases the conditional entropy of received signals.

Abstract

Diffusion models (DM) can gradually learn to remove noise, which have been widely used in artificial intelligence generated content (AIGC) in recent years. The property of DM for eliminating noise leads us to wonder whether DM can be applied to wireless communications to help the receiver mitigate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for semantic communications over wireless channels in this paper. CDDM can be applied as a new physical layer module after the channel equalization to learn the distribution of the channel input signal, and then utilizes this learned knowledge to remove the channel noise. We derive corresponding training and sampling algorithms of CDDM according to the forward diffusion process specially designed to adapt the channel models and theoretically prove that the well-trained CDDM can effectively reduce the conditional entropy of the received signal under small sampling steps. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC) for image transmission. Extensive experimental results demonstrate that CDDM can further reduce the mean square error (MSE) after minimum mean square error (MMSE) equalizer, and the joint CDDM and JSCC system achieves better performance than the JSCC system and the traditional JPEG2000 with low-density parity-check (LDPC) code approach.