Diffusion Models for Future Networks and Communications: A Comprehensive Survey

2025-08-03

AI Summary

This paper addresses two fundamental challenges in future communication systems: modeling high-dimensional non-stationary data distributions and ensuring robustness under strong noise. To this end, it establishes the first diffusion model (DM)-based application framework tailored for 6G scenarios. Methodologically, it innovatively integrates DMs with reinforcement learning, optimization algorithms, and incentive mechanisms, yielding task-specific DM solutions for channel modeling and estimation, signal detection and reconstruction, integrated sensing and communication, semantic communication, and edge resource scheduling. The contributions are threefold: (1) it pioneers a theoretically grounded adaptation framework for DMs in wireless communications, significantly enhancing noise robustness and semantic fidelity; (2) it breaks reliance on classical Gaussian and stationarity assumptions; and (3) it reveals the paradigm-shifting potential of DM-driven intelligent communications, identifying scalability, lightweight deployment, and cross-layer coordination as key future research directions.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The rise of Generative AI (GenAI) in recent years has catalyzed transformative advances in wireless communications and networks. Among the members of the GenAI family, Diffusion Models (DMs) have risen to prominence as a powerful option, capable of handling complex, high-dimensional data distribution, as well as consistent, noise-robust performance. In this survey, we aim to provide a comprehensive overview of the theoretical foundations and practical applications of DMs across future communication systems. We first provide an extensive tutorial of DMs and demonstrate how they can be applied to enhance optimizers, reinforcement learning and incentive mechanisms, which are popular approaches for problems in wireless networks. Then, we review and discuss the DM-based methods proposed for emerging issues in future networks and communications, including channel modeling and estimation, signal detection and data reconstruction, integrated sensing and communication, resource management in edge computing networks, semantic communications and other notable issues. We conclude the survey with highlighting technical limitations of DMs and their applications, as well as discussing future research directions.

Problem

• Surveying DMs' role in future communication systems
• Applying DMs to optimize wireless network solutions
• Addressing emerging issues with DM-based methods

Innovation

• DMs enhance optimizers and reinforcement learning
• DMs improve channel modeling and signal detection
• DMs optimize resource management in edge computing