Generative AI for Physical Layer Communications: A Survey

• URL: http://arxiv.org/abs/2312.05594v1
• Date: Sat, 9 Dec 2023 15:20:56 GMT
• Title: Generative AI for Physical Layer Communications: A Survey
• Authors: Nguyen Van Huynh, Jiacheng Wang, Hongyang Du, Dinh Thai Hoang, Dusit Niyato, Diep N. Nguyen, Dong In Kim, and Khaled B. Letaief
• Abstract summary: generative artificial intelligence (GAI) has the potential to enhance the efficiency of digital content production. GAI's capability in analyzing complex data distributions offers great potential for wireless communications. 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.
• Score: 76.61956357178295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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