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).

使用生成式人工智能 (generative AI)、扩散模型 (diffusion models) 和去噪扩散概率模型 (DDPM) 研究了其在无线通信系统中的应用，并提出了具有鲁棒性的 AI 原生通信系统的发展方向与开放问题。