Innovative foundation models, such as GPT-4 and stable diffusion models, have made a paradigm shift in the realm of artificial intelligence (AI) towards generative AI-based systems. AI and machine learning (AI/ML) algorithms are envisioned to be pervasively incorporated into the future wireless communications systems. In this article, we outline the applications of diffusion models in wireless communication systems, which are a new family of probabilistic generative models that have showcased state-of-the-art performance. The key idea is to decompose data generation process over "denoising" steps, gradually generating samples out of noise. Based on two case studies presented, we show how diffusion models can be employed for the development of resilient AI-native communication systems. Specifically, we propose denoising diffusion probabilistic models (DDPM) for a wireless communication scheme with non-ideal transceivers, where 30% improvement is achieved in terms of bit error rate. In the other example, DDPM is employed at the transmitter to shape the constellation symbols, highlighting a robust out-of-distribution performance.

翻译：创新基础模型（如GPT-4和稳定扩散模型）已推动人工智能领域向生成式AI系统范式转变。未来无线通信系统将全面融入AI与机器学习算法。本文概述了扩散模型在无线通信系统中的应用——这类新型概率生成模型已展现出最先进的性能。其核心思想是将数据生成过程分解为多个"去噪"步骤，逐步从噪声中生成样本。通过两个案例研究，我们展示了如何利用扩散模型构建具有韧性的AI原生通信系统。具体而言，针对非理想收发机场景，我们提出基于去噪扩散概率模型（DDPM）的无线通信方案，在误码率指标上实现30%的提升。另一个案例中，DDPM被部署于发射端以优化星座符号映射，凸显出卓越的分布外鲁棒性能。