标题： LLM 遇到天空：用于安全异构无人机网络的启发式多智能体强化学习 显示英文标题

标题： LLM Meets the Sky: Heuristic Multi-Agent Reinforcement Learning for Secure Heterogeneous UAV Networks

摘要： 这项工作解决了在推进能量约束下异构无人机网络（HetUAVNs）中最大化保密率的物理层安全（PLS）问题。 与之前假设无人机能力一致或忽略能量-安全权衡的研究不同，我们考虑了一个现实场景，其中具有不同有效载荷和计算资源的无人机协作在存在窃听者的情况下为地面终端提供服务。 为了管理无人机运动与通信之间的复杂耦合，我们提出了一种分层优化框架。 内层使用基于半定松弛（SDR）的S2DC算法，结合惩罚函数和凸差（d.c.）规划，以解决固定无人机位置下的保密预编码问题。 外层引入了一种由大型语言模型（LLM）引导的启发式多智能体强化学习方法（LLM-HeMARL）用于轨迹优化。 LLM-HeMARL高效地结合了由LLM生成的专家启发策略，使无人机能够在不产生实时LLM调用推理开销的情况下，学习节能且安全驱动的轨迹。 仿真结果表明，我们的方法在保密率和能效方面优于现有基线方法，并在不同无人机群规模和随机种子下表现出一致的鲁棒性。 显示英文摘要

摘要： This work tackles the physical layer security (PLS) problem of maximizing the secrecy rate in heterogeneous UAV networks (HetUAVNs) under propulsion energy constraints. Unlike prior studies that assume uniform UAV capabilities or overlook energy-security trade-offs, we consider a realistic scenario where UAVs with diverse payloads and computation resources collaborate to serve ground terminals in the presence of eavesdroppers. To manage the complex coupling between UAV motion and communication, 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 expert heuristics policy generated by the LLM, 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.