Denoising Diffusion Probabilistic Models for Hardware-Impaired
  Communication Systems: Towards Wireless Generative AI
Mehdi Letafati Samad Ali Matti Latva-aho
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.