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）已推动人工智能领域向生成式AI系统实现范式转变。与此同时，从数据通信与网络角度出发，人工智能与机器学习算法被预期将全面融入未来无线通信系统，凸显了新兴通信场景对原生AI解决方案的需求。本文概述了生成式AI在无线通信系统中的应用，旨在为该领域研究奠定基础。作为生成式模型的最新前沿范式，我们介绍了基于扩散的生成模型，并探讨了其在无线通信系统中的潜在应用。通过两项案例研究，展示了如何利用扩散模型开发具有韧性的原生AI通信系统：首先提出将去噪扩散概率模型应用于非理想收发器无线通信方案，实现了误码率30%的性能提升；其次，在发射端应用DDPM进行星座符号整形，展现了鲁棒的分布外性能。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，以促进无线生成式AI（WiGenAI）领域的后续研究。