Diffusion Models for Wireless Communications

TL;DR

This paper explores the use of denoising diffusion models in wireless communications, demonstrating their ability to improve data reconstruction and signal modeling in digital and semantic systems, with significant performance gains.

Contribution

It introduces the application of diffusion models to wireless systems, including novel conditional diffusion models for data reconstruction and semantic communication, showing substantial performance improvements.

Findings

10 dB improvement in low-SNR data reconstruction

Diffusion autoencoders outperform legacy autoencoders and VAEs

Mitigation of redundant error correction bits in digital systems

Abstract

A comprehensive study on the applications of denoising diffusion models for wireless systems is provided. The article highlights the capabilities of diffusion models in learning complicated signal distributions, modeling wireless channels, and denoising and reconstructing distorted signals. First, fundamental working mechanism of diffusion models is introduced. Then the recent advances in applying diffusion models to wireless systems are reviewed. Next, two case studies are provided, where conditional diffusion models (CDiff) are proposed for data reconstruction enhancement, covering both the conventional digital communication systems, as well as the semantic communication (SemCom) setups. The first case study highlights about 10 dB improvement in data reconstruction under low-SNR regimes, while mitigating the need to transmit redundant bits for error correction codes in digital systems. The second study further extends the case to a SemCom setup, where diffusion autoencoders showcase superior performance compared to legacy autoencoders and variational autoencoder (VAE) architectures. Finally, future directions and existing challenges are discussed.