扩散模型在无线通信中的应用 (Diffusion Models for Wireless Communications)

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.

翻译：本文对去噪扩散模型在无线系统中的应用进行了全面研究。文章重点阐述了扩散模型在学习复杂信号分布、建模无线信道以及去噪和重构失真信号方面的能力。首先，介绍了扩散模型的基本工作机制。随后，综述了扩散模型应用于无线系统的最新进展。接着，提供了两个案例研究，其中提出了条件扩散模型（CDiff）用于数据重构增强，涵盖传统的数字通信系统以及语义通信（SemCom）场景。第一个案例研究表明，在低信噪比条件下，数据重构性能提升了约10 dB，同时减少了数字系统中为纠错码传输冗余比特的需求。第二个研究进一步将该案例扩展至语义通信场景，其中扩散自编码器相较于传统自编码器和变分自编码器（VAE）架构展现出更优越的性能。最后，讨论了未来的研究方向及现有挑战。