Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspective

TL;DR

This paper explores how generative AI can enhance the physical layer of integrated sensing and communication systems, demonstrating its effectiveness in tasks like near-field signal direction estimation with high accuracy.

Contribution

It provides a comprehensive analysis of GAI applications at the physical layer of ISAC systems and introduces a diffusion model-based method for near-field sensing.

Findings

GAI supports physical layer functions in ISAC systems.

Diffusion model achieves 1.03° mean square error in direction estimation.

GAI enhances near-field sensing capabilities.

Abstract

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.