Generative AI has received significant attention among a spectrum of diverse industrial and academic domains, thanks to the magnificent results achieved from deep generative models such as generative pre-trained transformers (GPT) and diffusion models. In this paper, we explore the applications of denoising diffusion probabilistic models (DDPMs) in wireless communication systems under practical assumptions such as hardware impairments (HWI), low-SNR regime, and quantization error. Diffusion models are a new class of state-of-the-art generative models that have already showcased notable success with some of the popular examples by OpenAI1 and Google Brain2. The intuition behind DDPM is to decompose the data generation process over small ``denoising'' steps. Inspired by this, we propose using denoising diffusion model-based receiver for a practical wireless communication scheme, while providing network resilience in low-SNR regimes, non-Gaussian noise, different HWI levels, and quantization error. We evaluate the reconstruction performance of our scheme in terms of mean-squared error (MSE) metric. Our results show that more than 25 dB improvement in MSE is achieved compared to deep neural network (DNN)-based receivers. We also highlight robust out-of-distribution performance under non-Gaussian noise.

翻译：生成式人工智能在工业与学术等多个领域备受关注，这得益于生成式预训练变换器（GPT）和扩散模型等深度生成模型所取得的卓越成果。本文探讨了去噪扩散概率模型（DDPMs）在无线通信系统中的应用，考虑了硬件损伤（HWI）、低信噪比（SNR）场景及量化误差等实际条件。扩散模型是一类前沿生成模型，已通过OpenAI和Google Brain的典型例子展现出显著成功。DDPM的核心思想是将数据生成过程分解为一系列小的"去噪"步骤。受此启发，我们提出将基于去噪扩散模型的接收器用于实际无线通信方案，从而在低信噪比区域、非高斯噪声、不同硬件损伤水平及量化误差下提供网络弹性。我们通过均方误差（MSE）指标评估方案的恢复性能。结果表明，与基于深度神经网络（DNN）的接收器相比，我们的方案在MSE上实现了超过25分贝的改善。我们还突出了在非高斯噪声下稳健的分布外性能。