6 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…
Adapting Diffusion Language Models for Lossless Pixel-Level Image Transmission
Tianqi Ren, Rongpeng Li, Xianfu Chen +2
Lossless pixel-level image transmission is a fundamental regime beyond semantic communications, because exact recovery requires both accurate symbol probability modeling and reliab…
Multi-Agent Conditional Diffusion Model with Mean Field Communication as Wireless Resource Allocation Planner
Kechen Meng, Sinuo Zhang, Rongpeng Li +5
In wireless communication systems, efficient and adaptive resource allocation plays a crucial role in enhancing overall Quality of Service (QoS). Compared to the conventional Model…
Physics-Gated Visual Prediction of MARFE on the HL-3 Tokamak
Qianyun Dong, Rongpeng Li, Zongyu Yang +5
The Multifaceted Asymmetric Radiation From the Edge (MARFE) is a critical plasma instability that often precedes density-limit disruptions in tokamaks, posing a significant risk to…
Plasma Shape Control via Zero-shot Generative Reinforcement Learning
Niannian Wu, Rongpeng Li, Zongyu Yang +6
Traditional PID controllers have limited adaptability for plasma shape control, and task-specific reinforcement learning (RL) methods suffer from limited generalization and the nee…
High-Fidelity Data-Driven Dynamics Model for Reinforcement Learning-based Control in HL-3 Tokamak
Niannian Wu, Zongyu Yang, Rongpeng Li +11
The success of reinforcement learning (RL)-based control in tokamaks, an emerging technique for controlled nuclear fusion with improved flexibility, typically requires substantial…