As generative artificial intelligence (GAl) models continue to evolve, their generative capabilities are increasingly enhanced, and being used exten-sively in content generation. Furthermore, GAl also excels in data modeling and analysis, benefiting 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 GAl's potential support across multi-ple layers of ISAC. We then thoroughly investigate GAl's applications in the physical layer, such as channel estimation, which demonstrates the benefits that GAl-enhanced physical layer technologies bring to ISAC systems. Finally, in the case study, we present a diffusion model-based method for estimating signal direction of arrival in near-field scenarios using uniform linear arrays with antenna spacing over half the wavelength. With a mean square error of 1.03 degrees, the method confirms GAl's support for the physical layer in near-field sensing and communications.

中文翻译：

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用于集成传感和通信的生成式 AI：从物理层角度获得见解


随着生成式人工智能 （GAl） 模型的不断发展，其生成能力越来越强大，并被广泛用于内容生成。此外，GAl 还擅长数据建模和分析，使无线通信系统受益。在本文中，我们研究了 GAI 在物理层中的应用，并分析了它对集成传感和通信 （ISAC） 系统的支持。具体来说，我们首先概述了 GAI 和 ISAC，并谈到了 GAl 在 ISAC 多层中的潜在支持。然后，我们彻底研究了 GAl 在物理层的应用，例如信道估计，这展示了 GAl 增强物理层技术为 ISAC 系统带来的好处。最后，在案例研究中，我们提出了一种基于扩散模型的方法，用于使用天线间距超过波长一半的均匀线性阵列来估计近场场景中的信号到达方向。该方法的均方误差为 1.03 度，证实了 GAl 在近场传感和通信中对物理层的支持。