Innovative foundation models, such as GPT-3 and stable diffusion models, have made a paradigm shift in the realm of artificial intelligence (AI) towards generative AI-based systems. In unison, from data communication and networking perspective, AI and machine learning (AI/ML) algorithms are envisioned to be pervasively incorporated into the future generations of wireless communications systems, highlighting the need for novel AI-native solutions for the emergent communication scenarios. In this article, we outline the applications of generative AI in wireless communication systems to lay the foundations for research in this field. Diffusion-based generative models, as the new state-of-the-art paradigm of generative models, are introduced, and their applications in wireless communication systems are discussed. Two case studies are also presented to showcase how diffusion models can be exploited 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. As the second application, DDPMs are employed at the transmitter to shape the constellation symbols, highlighting a robust out-of-distribution performance. Finally, future directions and open issues for the development of generative AI-based wireless systems are discussed to promote future research endeavors towards wireless generative AI (WiGenAI).

翻译：创新基础模型（如GPT-3和稳定扩散模型）已在人工智能领域引发范式转变，推动系统向生成式AI方向演进。与此同时，从数据通信与网络视角来看，人工智能与机器学习算法预计将被广泛集成至未来几代无线通信系统中，这凸显了为新兴通信场景开发新型AI原生解决方案的迫切需求。本文概述了生成式AI在无线通信系统中的应用，为该领域研究奠定基础。作为生成模型领域的最新前沿范式，本文介绍了扩散生成模型并探讨其在无线通信系统中的具体应用。我们通过两个案例分析，展示了如何利用扩散模型开发具有鲁棒性的AI原生通信系统。首先，针对非理想收发信机场景，提出了一种基于去噪扩散概率模型的无线通信方案，在误码率指标上实现了30%的性能提升。其次，将该模型应用于发射端的星座符号整形，展现出卓越的分布外鲁棒性。最后，讨论了基于生成式AI的无线系统未来发展方向与开放性问题，以推动无线生成式AI（WiGenAI）的后续研究。