Thanks to the outstanding achievements from state-of-the-art generative models like ChatGPT and diffusion models, generative AI has gained substantial attention across various industrial and academic domains. In this paper, denoising diffusion probabilistic models (DDPMs) are proposed for a practical finite-precision wireless communication system with hardware-impaired transceivers. The intuition behind DDPM is to decompose the data generation process over the so-called "denoising" steps. Inspired by this, a DDPM-based receiver is proposed for a practical wireless communication scheme that faces realistic non-idealities, including hardware impairments (HWI), channel distortions, and quantization errors. It is shown that our approach provides network resilience under low-SNR regimes, near-invariant reconstruction performance with respect to different HWI levels and quantization errors, and robust out-of-distribution performance against non-Gaussian noise. Moreover, the reconstruction performance of our scheme is evaluated in terms of cosine similarity and mean-squared error (MSE), highlighting more than 25 dB improvement compared to the conventional deep neural network (DNN)-based receivers.

翻译：得益于ChatGPT和扩散模型等先进生成模型的卓越成就，生成式人工智能已在众多工业和学术领域获得广泛关注。本文针对配备硬件损伤收发器的实际有限精度无线通信系统，提出了去噪扩散概率模型。DDPM的核心思路是将数据生成过程分解为所谓的"去噪"步骤。受此启发，我们提出了一种基于DDPM的接收机，适用于面临实际非理想特性（包括硬件损伤、信道失真和量化误差）的无线通信方案。研究表明，该方法在低信噪比条件下具有网络鲁棒性，针对不同硬件损伤水平和量化误差展现出近乎恒定的重构性能，并且对非高斯噪声具有稳健的分布外泛化能力。此外，我们通过余弦相似度和均方误差评估了方案的重构性能，结果显示相较于传统深度神经网络接收机，性能提升超过25分贝。