mlsanityData to 7 Oct 2026

Generative Artificial Intelligence (GAI) for Mobile Communications: A Diffusion Model Perspective

arXiv 2410.06389 ↗First posted 8 Oct 2024eess.SPcited by 0

On the map: Diffusion models → Diffusion for compression and wireless

Abstract (arXiv)

This article targets at unlocking the potentials of a class of prominent generative artificial intelligence (GAI) method, namely diffusion model (DM), for mobile communications. First, a DM-driven communication architecture is proposed, which introduces two key paradigms, i.e., conditional DM and DM-driven deep reinforcement learning (DRL), for wireless data generation and communication management, respectively. Then, we discuss the key advantages of DM-driven communication paradigms. To elaborate further, we explore DM-driven channel generation mechanisms for channel estimation, extrapolation, and feedback in multiple-input multiple-output (MIMO) systems. We showcase the numerical performance of conditional DM using the accurate DeepMIMO channel datasets, revealing its superiority in generating high-fidelity channels and mitigating unforeseen distribution shifts in sophisticated scenes. Furthermore, several DM-driven communication management designs are conceived, which is promising to deal with imperfect channels and task-oriented communications. To inspire future research developments, we highlight the potential applications and open research challenges of DM-driven communications. Code is available at https://github.com/xiaoxiaxusummer/GAI_COMM/

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models. Builds on 13 of its 21 references that are in the map. First posted on arXiv 8 Oct 2024.

Builds on

13 of 21 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization10 Aug

Other references · 12

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  4. 2022Classifier-Free Diffusion Guidancecited by 5,717
  5. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,023
  6. 2022Diffusion Models in Vision: A Surveycited by 1,008
  7. 2022Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learningcited by 543
  8. 2019DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applicationscited by 121
  9. 2023Policy Representation via Diffusion Probability Model for Reinforcement Learningcited by 105
  10. 2023MetaDiffuser: Diffusion Model as Conditional Planner for Offline Meta-RLcited by 65
  11. 2023Generative AI for Integrated Sensing and Communication: Insights from the Physical Layer Perspectivecited by 17
  12. 2021Deep Learning based Channel Extrapolation for Large-Scale Antenna Systems: Opportunities, Challenges and Solutionscited by 5

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  1. 2025Diffusion Models for Future Networks and Communications: A Comprehensive Survey
  2. 2026From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks
  3. 2024Generative Diffusion Models for High Dimensional Channel Estimation
  4. 2023Deep Generative Model and Its Applications in Efficient Wireless Network Management: A Tutorial and Case Study
  5. 2024GDM4MMIMO: Generative Diffusion Models for Massive MIMO Communications
  6. 2025Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art
  7. 2023Generative AI for Physical Layer Communications: A Survey
  8. 2025Improve the Training Efficiency of DRL for Wireless Communication Resource Allocation: The Role of Generative Diffusion Models