  As generative artificial intelligence (GAI) models continue to evolve, their
generative capabilities are increasingly enhanced and being used extensively in
content generation. Beyond this, GAI also excels in data modeling and analysis,
benefitting wireless communication systems. In this article, we investigate
applications of GAI in the physical layer and analyze its support for
integrated sensing and communications (ISAC) systems. Specifically, we first
provide an overview of GAI and ISAC, touching on GAI's potential support across
multiple layers of ISAC. We then concentrate on the physical layer,
investigating GAI's applications from various perspectives thoroughly, such as
channel estimation, and demonstrate the value of these GAI-enhanced physical
layer technologies for ISAC systems. In the case study, the proposed diffusion
model-based method effectively estimates the signal direction of arrival under
the near-field condition based on the uniform linear array, when antenna
spacing surpassing half the wavelength. With a mean square error of 1.03
degrees, it confirms GAI's support for the physical layer in near-field sensing
and communications.
