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.

中文翻译：

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物理层通信的生成式人工智能：一项调查


生成式人工智能（GAI）的最新发展导致了ChatGPT等突破性应用的出现，它不仅提高了文本、音频、视频甚至网络流量数据等数字内容生产的效率，还丰富了其内容多样性。除了数字内容创建之外，GAI 分析复杂数据分布的能力为无线通信提供了巨大潜力，特别是在新物理层通信技术快速扩展的情况下。例如，扩散模型可以学习输入信号分布并使用它们来提高信道估计精度，而变分自动编码器可以对信道分布进行建模并推断盲信道均衡的潜在变量。因此，本文对 GAI 在物理层通信的应用进行了全面的研究，范围从信号分类、信道估计和均衡等传统问题到智能反射面和联合源信道编码等新兴主题。我们还将 GAI 支持的物理层通信与传统 AI 支持的物理层通信进行了比较，突出了 GAI 的固有功能和在这些领域的独特贡献。最后，论文讨论了一些悬而未决的问题，并提出了未来的几个研究方向，为GAI在物理层通信方面的进一步探索和进步奠定了基础。