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.

本文提出了基于去噪扩散概率模型的无线通信方案，旨在解决实际应用中的硬件损伤、信道失真和量化误差等非理想因素，提供低信噪比下的网络韧性、对不同硬件损伤水平和量化误差的近不变重建性能，以及抵抗非高斯噪声的强大分布外表现，并通过余弦相似度和均方误差（MSE）评估与传统深度神经网络（DNN）接收机相比的超过 25 dB 改进的重建性能。