Generative AI for Physical Layer Communications: A Survey

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. Bey...

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Bibliographic Details
Published in:IEEE transactions on cognitive communications and networking 2024-06, Vol.10 (3), p.706-728
Main Authors: Van Huynh, Nguyen, Wang, Jiacheng, Du, Hongyang, Hoang, Dinh Thai, Niyato, Dusit, Nguyen, Diep N., Kim, Dong In, Letaief, Khaled B.
Format: Article
Language:English
Subjects:
Artificial intelligence
Audio data
beamforming
Channel estimation
channel estimation and equalization
Communications traffic
Diffusion layers
Equalization
Generative adversarial networks
Generative AI
Generative artificial intelligence
IRS
joint source channel coding
Physical layer
physical layer communications
physical layer security
Reconfigurable intelligent surfaces
Signal classification
Surveys
Wireless communication
Wireless communications
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Summary: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.
ISSN:2332-7731
2332-7731
DOI:10.1109/TCCN.2024.3384500