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Generative Diffusion Models (GDMs) have emerged as a transformative force inthe realm of Generative Artificial Intelligence (GAI), demonstrating theirversatility and efficacy across a variety of applications. The ability to modelcomplex data distributions and generate high-quality samples has made GDMsparticularly effective in tasks such as image generation and reinforcementlearning. Furthermore, their iterative nature, which involves a series of noiseaddition and denoising steps, is a powerful and unique approach to learning andgenerating data. This paper serves as a comprehensive tutorial on applying GDMsin network optimization tasks. We delve into the strengths of GDMs, emphasizingtheir wide applicability across various domains, such as vision, text, andaudio generation.We detail how GDMs can be effectively harnessed to solvecomplex optimization problems inherent in networks. The paper first provides abasic background of GDMs and their applications in network optimization. Thisis followed by a series of case studies, showcasing the integration of GDMswith Deep Reinforcement Learning (DRL), incentive mechanism design, SemanticCommunications (SemCom), Internet of Vehicles (IoV) networks, etc. These casestudies underscore the practicality and efficacy of GDMs in real-worldscenarios, offering insights into network design. We conclude with a discussionon potential future directions for GDM research and applications, providingmajor insights into how they can continue to shape the future of networkoptimization.

Beyond Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization