In this paper, conditional denoising diffusion probabilistic models (DDPMs) are proposed to enhance the data transmission and reconstruction over wireless channels. The underlying mechanism of DDPM is to decompose the data generation process over the so-called "denoising" steps. Inspired by this, the key idea is to leverage the generative prior of diffusion models in learning a "noisy-to-clean" transformation of the information signal to help enhance data reconstruction. The proposed scheme could be beneficial for communication scenarios in which a prior knowledge of the information content is available, e.g., in multimedia transmission. Hence, instead of employing complicated channel codes that reduce the information rate, one can exploit diffusion priors for reliable data reconstruction, especially under extreme channel conditions due to low signal-to-noise ratio (SNR), or hardware-impaired communications. The proposed DDPM-assisted receiver is tailored for the scenario of wireless image transmission using MNIST dataset. Our numerical results highlight the reconstruction performance of our scheme compared to the conventional digital communication, as well as the deep neural network (DNN)-based benchmark. It is also shown that more than 10 dB improvement in the reconstruction could be achieved in low SNR regimes, without the need to reduce the information rate for error correction.

翻译：本文提出采用条件去噪扩散概率模型（DDPM）来增强无线信道上的数据传输与重构能力。DDPM的基本机制是通过一系列“去噪”步骤分解数据生成过程。受此启发，本研究核心思想在于利用扩散模型的生成先验，学习信息信号从“含噪到洁净”的变换过程，从而提升数据重构性能。该方案特别适用于具备信息内容先验知识的通信场景（例如多媒体传输）。因此，无需采用会降低信息率的复杂信道编码，即可借助扩散先验实现可靠数据重构，尤其在低信噪比（SNR）或硬件受损通信等极端信道条件下效果显著。本文提出的DDPM辅助接收机专为基于MNIST数据集的无线图像传输场景设计。数值实验结果表明：相较于传统数字通信方案及基于深度神经网络（DNN）的基准方法，本方案在重构性能上具有显著优势。研究同时证实，在低信噪比环境下无需通过降低信息率进行纠错，即可实现超过10 dB的重构性能提升。