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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.
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