The recent evolution of generative artificial intelligence (GAI) leads to the emergence of groundbreaking applications such as ChatGPT, which not only enhances the efficiency of digital content production, such as text, audio, video, or even network traffic data, but also enriches its diversity. Beyond digital content creation, GAI's capability in analyzing complex data distributions offers great potential for wireless communications, particularly amidst a rapid expansion of new physical layer communication technologies. For example, the diffusion model can learn input signal distributions and use them to improve the channel estimation accuracy, while the variational autoencoder can model channel distribution and infer latent variables for blind channel equalization. Therefore, this paper presents a comprehensive investigation of GAI's applications for communications at the physical layer, ranging from traditional issues, including signal classification, channel estimation, and equalization, to emerging topics, such as intelligent reflecting surfaces and joint source channel coding. We also compare GAI-enabled physical layer communications with those supported by traditional AI, highlighting GAI's inherent capabilities and unique contributions in these areas. Finally, the paper discusses open issues and proposes several future research directions, laying a foundation for further exploration and advancement of GAI in physical layer communications.

翻译：近年来，生成式人工智能（GAI）的演进催生了诸如ChatGPT等突破性应用，不仅提升了文本、音频、视频乃至网络流量数据等数字内容的生产效率，还丰富了其多样性。除数字内容生成外，GAI在分析复杂数据分布方面的能力为无线通信带来了巨大潜力，尤其是在物理层通信新技术快速扩展的背景下。例如，扩散模型可学习输入信号分布，并用于提升信道估计精度；变分自编码器可对信道分布建模，并推导隐变量用于盲信道均衡。因此，本文全面探讨了GAI在物理层通信中的应用，涵盖从信号分类、信道估计与均衡等传统问题，到智能反射面、联合信源信道编码等新兴主题。我们还比较了基于GAI的物理层通信与传统AI支持的方法，凸显了GAI在这些领域的固有能力和独特贡献。最后，本文讨论了开放性问题并提出了若干未来研究方向，为GAI在物理层通信中的进一步探索与发展奠定基础。