LLM-Aided Joint Secrecy Precoding and Trajectory for RSMA-Based Heterogeneous UAV Networks

The paper presents a novel hierarchical optimization framework for secure communications in RSMA-enabled heterogeneous UAV networks, addressing a multi-objective problem that includes UAV trajectory design and secrecy precoding. It introduces a Large Language Model-guided heuristic multi-agent reinforcement learning approach (LLM-HeMARL) that leverages LLM-generated expert policies to optimize UAV trajectories, achieving improved secrecy rates and energy efficiency compared to existing methods. This work is significant for practitioners as it demonstrates how LLMs can enhance decision-making in complex, non-convex optimization scenarios within UAV communication systems.