Biography: Dusit Niyato is a Chair professor in the College of Computing and Data Science, at Nanyang Technological University, Singapore. Dusit is currently serving as the Editor in Chief of IEEE Communications Surveys and Tutorials and the Area Editor of IEEE Transactions on Vehicular Technologies. His research interests are in the areas of sustainability, edge intelligence, decentralized machine learning, and incentive mechanism design. Dusit was named the 2017-2023 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET.
Talk Title:
Generative AI for Game Theory-based Mobile Networkin
Talk Abstract :
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GAI), demonstrating their versatility and efficacy across a variety of applications. The ability to model complex data distributions and generate high-quality samples has made GDMs particularly effective in tasks such as image generation and reinforcement learning. Furthermore, their iterative nature, which involves a series of noise addition and denoising steps, is a powerful and unique approach to learning and generating data. This presentation gives an introduction on applying GDMs in network optimization tasks. We delve into the strengths of GDMs, emphasizing their wide applicability across various domains. The presentation first provides a basic background of GDMs and their applications in network optimization. This is followed by a series of case studies, showcasing the integration of GDMs with Deep Reinforcement Learning (DRL), Semantic Communications (SemCom), and Internet of Vehicles (IoV) networks. These case studies underscore the practicality and efficacy of GDMs in real-world scenarios, offering insights into network design.
