Denoising Diffusion Probabilistic Models for Hardware-Impaired Communications

TL;DR

This paper introduces a denoising diffusion probabilistic model-based receiver for wireless communications, demonstrating significant improvements in reconstruction accuracy and robustness under hardware impairments, low-SNR, and non-Gaussian noise conditions.

Contribution

It is the first to apply DDPMs to wireless communication receivers, enhancing resilience and performance in practical hardware-impaired scenarios.

Findings

Over 25 dB MSE improvement over DNN-based receivers

Robust performance under non-Gaussian noise

Effective in low-SNR regimes

Abstract

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.