29 papers
Multi-objective Low-altitude IRS-assisted ISAC Optimization via Generative AI-enhanced Deep Reinforcement Learning
Wenwen Xie, Geng Sun, Chuang Zhang +3
Integrated sensing and communication (ISAC) has garnered substantial research interest owing to its pivotal role in advancing the development of next-generation (6G) wireless netwo…
Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power Systems: A Graph Diffusion Approach
Changyuan Zhao, Guangyuan Liu, Bin Xiang +2
With advancements in physical power systems and network technologies, integrated Cyber-Physical Power Systems (CPPS) have significantly enhanced system monitoring and control effic…
Optimizing Split Federated Learning with Unstable Client Participation
Wei Wei, Zheng Lin, Xihui Liu +3
To enable training of large artificial intelligence (AI) models at the network edge, split federated learning (SFL) has emerged as a promising approach by distributing computation…
Large Language Model (LLM)-enabled Reinforcement Learning for Wireless Network Optimization
Jie Zheng, Ruichen Zhang, Dusit Niyato +5
Enhancing future wireless networks presents a significant challenge for networking systems due to diverse user demands and the emergence of 6G technology. While reinforcement learn…
LLM-Empowered Agentic AI for QoE-Aware Network Slicing Management in Industrial IoT
Xudong Wang, Lei Feng, Ruichen Zhang +6
The Industrial Internet of Things (IIoT) requires networks that deliver ultra-low latency, high reliability, and cost efficiency, which traditional optimization methods and deep re…
Large Language Model Empowered Next-Generation MIMO Networks: Fundamentals, Challenges, and Visions
Zhe Wang, Jiayi Zhang, Hongyang Du +4
Next-generation Multiple-Input Multiple-Output (MIMO) is expected to be intelligent and scalable. In this paper, we study Large Language Model (LLM)-enabled next-generation MIMO ne…