标题： 生成扩散模型在无线网络中的应用：基础、架构与最新进展 显示英文标题

标题： Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art

摘要： 随着生成式人工智能（GAI）技术的快速发展，生成式扩散模型（GDMs）由于具有噪声抗性、训练稳定性、可控性和多模态生成等优势，在无线网络领域展现出显著的赋能潜力。 尽管已有多个研究聚焦于无线网络中的GDMs，但对其技术演进仍缺乏全面的综述。 受此启发，我们系统地探索了GDMs在无线网络中的应用。 首先，从数学原理出发，我们分析了GDMs的技术优势，并介绍了六个代表性模型。 此外，我们提出了包含感知层、传输层、应用层和安全平面的多层无线网络架构。 我们还介绍了各层中GDM的核心机制。 随后，我们对现有的基于GDM的方案进行了严谨的综述，重点分析了它们的创新点、GDM的作用、优势和不足。 最终，我们提取了关键挑战并提供了潜在解决方案，旨在为该领域的未来研究提供方向性指导。 显示英文摘要

摘要： With the rapid development of Generative Artificial Intelligence (GAI) technology, Generative Diffusion Models (GDMs) have shown significant empowerment potential in the field of wireless networks due to advantages, such as noise resistance, training stability, controllability, and multimodal generation. Although there have been multiple studies focusing on GDMs for wireless networks, there is still a lack of comprehensive reviews on their technological evolution. Motivated by this, we systematically explore the application of GDMs in wireless networks. Firstly, starting from mathematical principles, we analyze technical advantages of GDMs and present six representative models. Furthermore, we propose the multi-layer wireless network architecture including sensing layer, transmission layer, application layer, and security plane. We also introduce the core mechanisms of GDM at each of the layers. Subsequently, we conduct a rigorous review on existing GDM-based schemes, with a focus on analyzing their innovative points, the role of GDMs, strengths, and weaknesses. Ultimately, we extract key challenges and provide potential solutions, with the aim of providing directional guidance for future research in this field.