  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.
