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.

翻译：扩散模型（DM）能够逐步学习去除噪声，近年来在人工智能生成内容（AIGC）领域得到广泛应用。DM的去噪特性启发我们思考：能否将其应用于无线通信中，帮助接收器缓解信道噪声？为此，本文提出面向无线信道语义通信的信道去噪扩散模型（CDDM）。CDDM可作为信道均衡后的新型物理层模块，通过学习信道输入信号的分布，利用该知识消除信道噪声。我们根据适配信道模型的特殊前向扩散过程，推导了CDDM的相应训练与采样算法，并从理论上证明：在较小采样步数下，训练完备的CDDM能有效降低接收信号的条件熵。此外，我们将CDDM应用于基于联合信源信道编码（JSCC）的图像传输语义通信系统。大量实验结果表明：CDDM能在最小均方误差（MMSE）均衡器后进一步降低均方误差（MSE），且CDDM与JSCC的联合系统性能优于单独JSCC系统及采用低密度奇偶校验（LDPC）码的传统JPEG2000方案。