4 papers
LLM-Empowered Agentic MAC Protocols: A Dynamic Stackelberg Game Approach
Renxuan Tan, Rongpeng Li, Fei Wang +4
Medium Access Control (MAC) protocols, essential for wireless networks, are typically manually configured. While deep reinforcement learning (DRL)-based protocols enhance task-spec…
Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM Alignment
Renxuan Tan, Rongpeng Li, Zhifeng Zhao +1
Transcending the single-preference paradigm, aligning LLMs with diverse human values is pivotal for robust deployment. Contemporary Multi-Objective Preference Alignment (MPA) appro…
Pareto Actor-Critic for Communication and Computation Co-Optimization in Non-Cooperative Federated Learning Services
Renxuan Tan, Rongpeng Li, Xiaoxue Yu +3
Federated learning (FL) in multi-service provider (SP) ecosystems is fundamentally hampered by non-cooperative dynamics, where privacy constraints and competing interests preclude…
LLM4MAC: An LLM-Driven Reinforcement Learning Framework for MAC Protocol Emergence
Renxuan Tan, Rongpeng Li, Zhifeng Zhao
With the advent of 6G systems, emerging hyper-connected ecosystems necessitate agile and adaptive medium access control (MAC) protocols to contend with network dynamics and diverse…