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

TL;DR

This paper provides a comprehensive review of Generative Diffusion Models (GDMs) in wireless networks, highlighting their advantages, technological evolution, and potential for addressing core challenges across sensing, transmission, and security.

Contribution

It offers a structured taxonomy and detailed analysis of GDM-based schemes in wireless networks, filling the gap in comprehensive reviews of their development and applications.

Findings

GDMs enhance noise resistance and training stability in wireless networks.

They enable multimodal generation and controllability for network applications.

Key challenges include security and scalability, with proposed solutions outlined.

Abstract

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, we identify the core challenges of wireless networks and argue why GDMs are uniquely suited to address them. We then introduce the mathematical principles of GDMs and representative models. Furthermore, we organize our comprehensive review through a structured taxonomy that categorizes GDM-based schemes into the sensing, transmission, and Applications, complemented by a security plane. For each representative scheme, we analyze its 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.