219 citations · 546 across the 100 of their papers we have counts for
5 papers · 1 filter
Reinforcement Learning With LLMs Interaction For Distributed Diffusion Model Services
Hongyang Du, Ruichen Zhang, Dusit Niyato +5
Distributed Artificial Intelligence-Generated Content (AIGC) has attracted significant attention, but two key challenges remain: maximizing subjective Quality of Experience (QoE) a…
Generative AI for Space-Air-Ground Integrated Networks
Ruichen Zhang, Hongyang Du, Dusit Niyato +5
Recently, generative AI technologies have emerged as a significant advancement in artificial intelligence field, renowned for their language and image generation capabilities. Mean…
Enhancing Deep Reinforcement Learning: A Tutorial on Generative Diffusion Models in Network Optimization
Hongyang Du, Ruichen Zhang, Yinqiu Liu +10
Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy a…
Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study
Ruichen Zhang, Ke Xiong, Hongyang Du +4
Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative A…
Exploring Collaborative Distributed Diffusion-Based AI-Generated Content (AIGC) in Wireless Networks
Hongyang Du, Ruichen Zhang, Dusit Niyato +6
Driven by advances in generative artificial intelligence (AI) techniques and algorithms, the widespread adoption of AI-generated content (AIGC) has emerged, allowing for the genera…