扩散模型赋能未来通信，提升信道建模与资源管理效率
Diffusion Models for Future Networks and Communications: A Comprehensive Survey
- 将扩散模型用于无线网络优化、强化学习和激励机制设计
- 在信道估计、信号检测、语义通信等场景表现优异
- 适合研究智能通信系统与生成式AI融合的学者
近年来，生成式人工智能（GenAI）的兴起推动了无线通信与网络的变革。在各类GenAI方法中，扩散模型（DMs）因其处理高维复杂数据分布的能力及对噪声的鲁棒性，成为重要工具。本文全面综述了扩散模型在下一代通信系统中的理论基础与实际应用。首先，系统介绍扩散模型原理，并展示其在优化器、强化学习和激励机制设计中的应用，这些是无线网络中常见问题的主流解决方案。随后，梳理并讨论基于扩散模型应对未来网络新挑战的方法，包括信道建模与估计、信号检测与数据重建、通感一体化、边缘计算网络中的资源管理、语义通信等关键问题。最后，指出扩散模型当前的技术局限及其应用瓶颈，并展望未来研究方向。
原文摘要 · Abstract (English)
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.
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