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UAV-Assisted Secure Communications in Terrestrial Cognitive Radio Networks: Joint Power Control and 3D Trajectory Optimization

机译:陆地认知无线电网络中的无人机辅助安全通信:联合功率控制和3D轨迹优化

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摘要

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 Eve in decoding the confidential message from the secondary transmitter. Our goal is to jointly optimize the transmit power and UAV's trajectory in the three-dimensional space to maximize the average achievable secrecy rate of the secondary system. The formulated optimization problem is nonconvex due to the non-convexity of the objective and non-convexity of constraints, which is very challenging to solve. To obtain a suboptimal but efficient solution to the design problem, we first transform it into a more tractable form and then develop an iterative algorithm for its solution by leveraging the inner approximation (IA) framework. Combining tools from IA framework and S-procedure, we further extend the proposed algorithm to a more realistic scenario, where the imperfect location information of ground nodes (including Eve, secondary receiver and primary receiver) is considered, resulting in the average worst-case secrecy rate. Extensive numerical results are provided to demonstrate the merits of the proposed algorithms over existing approaches.
机译:本文认为在外部窃听器(EVE)存在下为底层认知无线电网络(CRN)的安全通信。 CRN的保密性能通常受到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飞行器(UAV)作为友好的干扰器来干扰来自辅助发射机的机密信息的夏娃。我们的目标是共同优化三维空间中的传输功率和UAV的轨迹,以最大限度地提高二级系统的平均可实现的保密率。由于限制的目标和非凸起的非凸起,所配制的优化问题是非凸起的,这是解决的非常具有挑战性。为了获得设计问题的次优,但有效的解决方案,我们首先将其转换为更易拔的形式,然后通过利用内部近似(IA)框架来开发其解决方案的迭代算法。将工具与IA框架和S-Program一起结合,我们进一步将所提出的算法扩展到更现实的场景,其中考虑了接地节点(包括EVE,辅助接收器和主接收器)的不完美位置信息,从而导致平均最坏情况保密率。提供了广泛的数值结果,以展示所提出的算法的优点。

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