Unmanned aerial vehicles (UAVs) are becoming increasingly attractive for the ambitious expectations for 5G and beyond networks due to their several benefits. Indeed, UAV-assisted communications introduce a new range of challenges and opportunities regarding the security of these networks. Thus, in this paper we explore the opportunities that UAVs can provide for physical layer security solutions. Particularly, we analyse the secrecy performance of a ground wireless communication network assisted by N friendly UAV jammers in the presence of an eavesdropper. To tackle the secrecy performance of this system, we introduce a new area-based metric, the weighted secrecy coverage (WSC), that measures the improvement on the secrecy performance of a system over a certain physical area given by the introduction of friendly jamming. Herein, the optimal 3D positioning of the UAVs and the power allocation is addressed in order to maximise the WSC. For that purpose, we provide a reinforcement learning-based solution by modelling the positioning problem as a multi-armed bandit problem over three positioning variables for the UAVs: angle, height and orbit radius. Our results show that the proposed algorithm improves the secrecy of the system over time in terms of the WSC, and it converges into a stable state close to the exhaustive search solution for discretised actions, where there is a trade-off between expediency of the positioning of the UAVs to positions of better secrecy outcome and energy consumption.

无人机 (UAV) 因其多项优势而对 5G 及其他网络的雄心勃勃的期望越来越有吸引力。实际上，无人机辅助通信为这些网络的安全带来了一系列新的挑战和机遇。因此，在本文中，我们探讨了无人机可以为物理层安全解决方案提供的机会。特别是，我们分析了由N辅助的地面无线通信网络的保密性能。在存在窃听者的情况下友好的无人机干扰器。为了解决该系统的保密性能，我们引入了一种新的基于区域的度量，即加权保密覆盖率（WSC），它衡量了由于引入友好干扰而在特定物理区域内系统保密性能的改进。在此，解决了无人机的最佳 3D 定位和功率分配问题，以最大化 WSC。为此，我们通过将定位问题建模为无人机的三个定位变量（角度、高度和轨道半径）上的多臂老虎机问题来提供基于强化学习的解决方案。我们的结果表明，所提出的算法在 WSC 方面随着时间的推移提高了系统的保密性，