Unmanned aerial vehicles (UAVs) play an essential role in future wireless communication networks due to their high mobility, low cost, and on-demand deployment. In air-to-ground links, UAVs are widely used to enhance the performance of wireless communication systems due to the presence of high-probability line-of-sight (LoS) links. However, the high probability of LoS links also increases the risk of being eavesdropped, posing a significant challenge to the security of wireless communications. In this work, the secure communication problem in a multi-UAV-assisted communication system is investigated in a moving airborne eavesdropping scenario. To improve the secrecy performance of the considered communication system, aerial eavesdropping capability is suppressed by sending jamming signals from a friendly UAV. An optimization problem under flight conditions, fairness, and limited energy consumption constraints of multiple UAVs is formulated to maximize the fair sum secrecy throughput. Given the complexity and non-convex nature of the problem, we propose a two-step-based optimization approach. The first step employs the $K$-means algorithm to cluster users and associate them with multiple communication UAVs. Then, a multi-agent deep deterministic policy gradient-based algorithm is introduced to solve this optimization problem. The effectiveness of this proposed algorithm is not only theoretically but also rigorously verified by simulation results.

翻译：无人机因其高机动性、低成本及按需部署特性，在未来无线通信网络中扮演着至关重要的角色。在空对地链路中，由于存在高概率的视距链路，无人机被广泛用于提升无线通信系统的性能。然而，高概率的视距链路也增加了被窃听的风险，对无线通信的安全性构成了重大挑战。本文研究了移动空中窃听场景下多无人机辅助通信系统中的安全通信问题。为提升所考虑通信系统的保密性能，通过从友好无人机发送干扰信号来抑制空中窃听能力。本文构建了一个在多个无人机的飞行条件、公平性及有限能耗约束下的优化问题，旨在最大化公平总保密吞吐量。鉴于该问题的复杂性和非凸特性，我们提出了一种基于两步法的优化方案：第一步采用$K$-means算法对用户进行聚类并将其与多个通信无人机关联；随后，引入一种基于多智能体深度确定性策略梯度的算法来求解该优化问题。所提算法的有效性不仅从理论上得到论证，更通过仿真结果进行了严格验证。