论文标题
陆地认知无线电网络中无人用的安全通信:联合电源控制和3D轨迹优化
UAV-Assisted Secure Communications in Terrestrial Cognitive Radio Networks: Joint Power Control and 3D Trajectory Optimization
论文作者
论文摘要
本文考虑了在外部窃听器(EVE)存在下的底层认知无线网络(CRN)的安全通信。 CRN的保密性能通常受主要接收器的干扰功率约束的限制。为了克服这个问题,我们建议使用无人驾驶汽车(UAV)作为友好的干扰器,以干扰夏娃从二级发射机(ST)中解码机密信息。我们的目标是在三维(3D)空间中共同优化发射功率和无人机的轨迹,以最大化二级系统的平均可实现的保密率。公式的优化问题是非凸的,这是由于约束的目标和非范围的非概念性,这是非常具有挑战性的。为了获得对问题的次优但有效的解决方案,我们首先将原始问题转换为更具处理形式的形式,并通过利用内部近似框架来开发其解决方案的迭代算法。我们进一步将提出的算法扩展到EVE不完美的位置信息的情况,在该情况下,平均最差的策略率被视为目标函数。提供了广泛的数值结果,以证明在现有方法上提出的算法的优点。
This paper considers secure communications for an underlay cognitive radio network (CRN) in the presence of an external eavesdropper (Eve). The secrecy performance of CRNs is usually limited by the primary receiver's interference power constraint. To overcome this issue, we propose to use an unmanned aerial vehicle (UAV) as a friendly jammer to interfere with Eve in decoding the confidential message from the secondary transmitter (ST). Our goal is to jointly optimize the transmit power and UAV's trajectory in the three-dimensional (3D) space to maximize the average achievable secrecy rate of the secondary system. The formulated optimization problem is nonconvex due to the nonconvexity of the objective and nonconvexity of constraints, which is very challenging to solve. To obtain a suboptimal but efficient solution to the problem, we first transform the original problem into a more tractable form and develop an iterative algorithm for its solution by leveraging the inner approximation framework. We further extend the proposed algorithm to the case of imperfect location information of Eve, where the average worst-case secrecy rate is considered as the objective function. Extensive numerical results are provided to demonstrate the merits of the proposed algorithms over existing approaches.