Unmanned aerial vehicles (UAVs) have been widely employed to enhance the end-to-end performance of wireless communications since the links between UAVs and terrestrial nodes are line-of-sight (LoS) with high probability. However, the broadcast characteristics of signal propagation in LoS links make it vulnerable to being wiretapped by malicious eavesdroppers, which poses a considerable challenge to the security of wireless communications. This paper investigates the security of aerial cognitive radio networks (CRNs). An airborne base station transmits confidential messages to secondary users utilizing the same spectrum as the primary network. An aerial base station transmits jamming signals to suppress the eavesdropper to enhance secrecy performance. The uncertainty of eavesdropping node locations is considered, and the average secrecy rate of the cognitive user is maximized by optimizing multiple users' scheduling, the UAVs' trajectory, and transmit power. To solve the non-convex optimization problem with mixed multiple integers variable problem, we propose an iterative algorithm based on block coordinate descent and successive convex approximation. Numerical results verify the effectiveness of our proposed algorithm and demonstrate that our scheme is beneficial to improving the secrecy performance of aerial CRNs.
翻译:无人驾驶航空飞行器(UAVs)被广泛用来提高无线通信的端到端性能,因为无人驾驶飞行器和地面节点之间的联系是视距线,概率很高;然而,LOS链接信号传播的广播特点使其容易被恶意窃听器窃听器窃听器窃听器窃听,这对无线通信的安全构成相当大的挑战。本文调查了空中认知无线电网络的安全性。一个空降基地台利用与主网络相同的频谱向二级用户传送机密信息。一个空基站传送干扰信号,以压制窃听器,加强保密性能。考虑到窃听节点位置的不确定性,通过优化多用户的日程安排、无人驾驶飞行器的轨迹和传输能量,使认知用户的平均保密率最大化。为了解决非convex优化问题,同时解决混合多个整数变量的混合问题,我们提议基于块协调下行和连续连接的连接率的迭代算法。NUMERCR结果验证了我们拟议空中算法的效益性能提升我们的空中算法。