mlsanityData to 14 Oct 2026

Generative AI for Physical Layer Communications: A Survey

arXiv 2312.05594 ↗First posted 9 Dec 2023cs.NI cs.AIcited by 17

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Abstract (arXiv)

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.

Key facts. Cited by 17 papers in the map (data through 14 Oct 2026). Builds on 64 of its 157 references that are in the map. First posted on arXiv 9 Dec 2023.

Builds on

64 of 157 references.

References · 64

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38 more
  1. 2017An Introduction to Deep Learning for the Physical Layercited by 233
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 9; 2026: 5.

17 papers.

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  1. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Artcited by 14
  2. 2024Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environmentscited by 10
  3. 2024Applications of Generative AI (GAI) for Mobile and Wireless Networking: A Surveycited by 9
  4. 2025SQ-GAN: Semantic Image Communications Using Masked Vector Quantizationcited by 4
  5. 2025Agentic Satellite-Augmented Low-Altitude Economy and Terrestrial Networks: A Survey on Generative Approachescited by 2
  6. 2025Enhancements in Score-based Channel Estimation for Real-Time Wireless Systemscited by 2
  7. 2025Generative AI Meets Wireless Sensing: Towards Wireless Foundation Modelcited by 1
  8. 2025SecDiff: Diffusion-Aided Secure Deep Joint Source-Channel Coding Against Adversarial Attackscited by 1
  9. 2024Strategic Demand-Planning in Wireless Networks: Can Generative-AI Save Spectrum and Energy?cited by 0
  10. 2025CoDiPhy: A General Framework for Applying Denoising Diffusion Models to the Physical Layer of Wireless Communication Systemscited by 0
  11. 2025Conditional Denoising Diffusion Autoencoders for Wireless Semantic Communicationscited by 0
  12. 2025Sensing and Understanding the World over Air: A Large Multimodal Model for Mobile Networkscited by 0
5 more
  1. 2026A Perception vs. Distortion Perspective on Score-Based Generative Channel Estimationcited by 0
  2. 2026Semantic-Aware Generative Image Transmission for Resource-Constrained Visual IoT Systemscited by 0
  3. 2026Generative Models for Modeling and Synthesizing MIMO Channels in Adverse Weather Conditionscited by 0
  4. 2026Wireless Foundation Models: State-of-the-Art and Open Challengescited by 0
  5. 2026Representing Clinical Conditions on Vital Signs from Healthy Individuals using Latent Modelingcited by 0

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