面向SAGIN中带移动天线的无人机集群的安全通信：基于CKM的多智能体强化学习框架
Toward Secure Communications for a UAV Swarm with Movable Antennas in SAGIN: CKM-Enabled Multi-Agent Reinforcement Learning Framework
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中文总结 AI 辅助
针对SAGIN中无人机集群通信易受窃听问题，提出CKM辅助的多智能体强化学习框架，联合优化MA位置、无人机轨迹与链路选择以提升保密能效。
中文摘要 AI 辅助
空天地一体化网络（SAGIN）可为无人机（UAV）提供无处不在的可靠连接，但空对地链路通常以视距（LoS）传播为主，因无线信道的广播特性易受被动窃听。为增强物理层安全，本文研究一种SAGIN支持的安全下行通信系统，其中无人机在卫星、空中和地面网络中选择服务链路，同时调整移动天线（MA）阵列的位置，以充分利用连接性和空间自由度提升保密通信性能。具体而言，我们通过联合优化MA位置、无人机轨迹和链路选择，在无人机移动性、MA移动和链路连接性约束下最大化无人机集群的保密能效（SEE）。为降低实时信道状态信息（CSI）获取开销，我们提出一种信道知识地图（CKM）辅助的多智能体强化学习框架：首先通过克里金插值从稀疏信道测量中构建CKM，再结合卫星星历信息实现CSI的高效存储与检索；为降低动作空间维度和计算复杂度，我们采用刚体运动学对MA阵列建模，通过全局刚体平移调整其位置，从而为联合优化决策构建低维混合动作空间；为使局部决策在系统约束下与全系统性能对齐，我们设计个体-团队协作奖励机制，并引入动作掩码以强制执行无人机移动性、避障、MA区域和连接容量的约束。
英文摘要
Space-air-ground integrated networks (SAGINs) can provide ubiquitous and reliable connectivity for unmanned aerial vehicles (UAVs). However, air-to-ground links, which are typically dominated by line-of-sight (LoS) propagation, are vulnerable to passive eavesdropping due to the broadcast nature of wireless channels. To enhance physical-layer security, we investigate a SAGIN-enabled secure downlink communication system in which UAVs select service links among satellite, aerial, and terrestrial networks while adjusting the positions of the movable antenna (MA) array to fully exploit connectivity and spatial degrees of freedom for improved secrecy communication performance. Specifically, we maximize the secrecy energy efficiency (SEE) of a UAV swarm by jointly optimizing the MA positions, UAV trajectories, and link selections, subject to UAV mobility, MA movement, and link connectivity constraints. To reduce the real-time channel state information (CSI) acquisition overhead, we propose a channel knowledge map (CKM)-assisted multi-agent reinforcement learning framework. Specifically, the CKM is first constructed from sparse channel measurements via Kriging interpolation and is then leveraged together with satellite ephemeris information to enable efficient storage and retrieval of CSI. To reduce the action-space dimensionality and computational complexity, we model the MA array using rigid-body kinematics and adjust its position through global rigid-body translation, thereby constructing a low-dimensional hybrid action space for the joint optimization decisions. To align local decisions with system-wide performance under system constraints, we design an individual-team collaborative reward mechanism and introduce action masks to enforce constraints on UAV mobility, collision avoidance, MA regions, and connectivity capacity.
