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

arXiv: https://arxiv.org/abs/2310.01036 · HTML: https://mlsanity.com/paper/2310.01036/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2023.

## Built on by (most cited first, 17 of 17)

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- [Generative Artificial Intelligence (GAI) for Mobile Communications: A Diffusion Model Perspective](https://mlsanity.com/paper/2410.06389/) (2024)
- [Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Design](https://mlsanity.com/paper/2505.12664/) (2025)
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- [Echo-Conditioned Denoising Diffusion Probabilistic Models for Multi-Target Tracking in RF Sensing](https://mlsanity.com/paper/2510.25464/) (2025)

## Abstract (arXiv)

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.