This paper investigates secure communications in rate-splitting multiple access (RSMA) enabled heterogeneous UAV networks, where multiple UAVs collaboratively serve ground terminals in the presence of eavesdroppers. By jointly considering secrecy rate maximization and propulsion energy consumption minimization, we formulate a multi-objective optimization problem involving UAV trajectory design, service association, power allocation, and secrecy precoding under mobility, collision-avoidance, service-capacity, and communication constraints. The formulated problem is highly non-convex due to the coupling among UAV trajectories, RSMA transmission variables, and secrecy constraints.To address the resulting non-convex and highly coupled optimization problem, we propose a hierarchical optimization framework. The inner layer uses a semidefinite relaxation (SDR)-based S2DC algorithm combining penalty functions and difference-of-convex (D.C.) programming to solve the secrecy precoding problem with fixed UAV positions. The outer layer introduces a Large Language Model (LLM)-guided heuristic multi-agent reinforcement learning approach (LLM-HeMARL) for trajectory optimization. LLM-HeMARL efficiently incorporates LLM-generated expert heuristic policy, enabling UAVs to learn energy-aware, security-driven trajectories without the inference overhead of real-time LLM calls. The simulation results show that our method outperforms existing baselines in secrecy rate and energy efficiency, with consistent robustness across varying UAV swarm sizes and random seeds.

翻译：本文研究了速率分裂多址接入（RSMA）异构无人机网络中的安全通信问题，其中多架无人机在存在窃听者的环境下协同服务地面终端。通过联合考虑保密速率最大化与推进能耗最小化，我们构建了一个多目标优化问题，涉及无人机轨迹设计、服务关联、功率分配及安全预编码，并受到移动性、避碰、服务容量与通信约束的限制。由于无人机轨迹、RSMA传输变量与安全约束之间存在耦合，该优化问题高度非凸。为解决这一非凸且高耦合的优化问题，我们提出了一种分层优化框架。内层采用基于半定松弛（SDR）的S2DC算法，结合惩罚函数与凸差（D.C.）规划，在固定无人机位置下求解安全预编码问题。外层引入了一种基于大语言模型（LLM）引导的启发式多智能体强化学习方法（LLM-HeMARL）进行轨迹优化。LLM-HeMARL高效融合了LLM生成的专家启发式策略，使无人机能够学习能量感知与安全驱动的轨迹，而无需实时调用LLM带来的推理开销。仿真结果表明，与现有基线方法相比，本方法在保密速率与能效方面均表现更优，并在不同无人机群规模与随机种子下保持一致的鲁棒性。