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 removing noise leads us to wonder whether DM can be applied to wireless communications to help the receiver eliminate the channel noise. To address this, we propose channel denoising diffusion models (CDDM) for wireless communications 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 design corresponding training and sampling algorithms for the forward diffusion process and the reverse sampling process of CDDM. Moreover, we apply CDDM to a semantic communications system based on joint source-channel coding (JSCC). 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.

翻译：扩散模型（DM）能够逐步学会去除噪声，近年来已被广泛应用于人工智能生成内容（AIGC）领域。DM的去噪特性使我们思考能否将其应用于无线通信，帮助接收端消除信道噪声。针对此问题，本文提出面向无线通信的信道去噪扩散模型（CDDM）。CDDM可作为物理层新模块部署在信道均衡之后，通过学习信道输入信号的分布，并利用该学习知识移除信道噪声。我们为CDDM的前向扩散过程与反向采样过程设计了相应的训练与采样算法。此外，我们将CDDM应用于基于联合信源信道编码（JSCC）的语义通信系统。实验结果表明，CDDM能在最小均方误差（MMSE）均衡器之后进一步降低均方误差（MSE），且CDDM与JSCC的联合系统在性能上优于单独JSCC系统以及采用低密度奇偶校验（LDPC）码的传统JPEG2000方案。