The cognitive unmanned aerial vehicle (UAV) communication system has emerged as a pivotal technology in addressing the scarcity of spectral resources for UAV communications, but the jamming and eavesdropping attacks are severe due to the high-quality air-to-ground communication links. Consequently, this paper introduces a UAV-enabled cooperative jammer to disrupt the eavesdropping activities of active eavesdroppers by emitting artificial noise. Our objective is to jointly optimize the three-dimensional UAV trajectory and transmit power to maximize the secrecy communication rate under quality of service (QoS) requirement. To tackle the non-convex problem, the block coordinate descent (BCD) and successive convex approximation (SCA) methods are utilized to transform it into an approximate convex problem, and then we design an alternative optimization iterative algorithm to achieve suboptimal but efficient solution. Moreover, we extend the developed algorithm into an imperfect channel state information (CSI) scenario to maximize the worst-case secrecy rate by jointly optimizing the robust UAV's trajectory and transmit power, where the location uncertainties of ground primary, secondary, and eavesdropping devices are considered. Simulation results demonstrate that the proposed joint optimization algorithm significantly enhances system secrecy performance under different real-world settings compared to existing state-of-the-art algorithms.

中文翻译：

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基于鲁棒联合轨迹和功率控制优化的认知无人机辅助安全可靠通信

认知无人机 （UAV） 通信系统已成为解决无人机通信频谱资源稀缺问题的关键技术，但由于高质量的空对地通信链路，干扰和窃听攻击非常严重。因此，本文引入了一种基于无人机的协作干扰器，通过发出人工噪声来破坏主动窃听者的窃听活动。我们的目标是在服务质量 （QoS） 要求下共同优化三维无人机轨迹和发射功率，以最大限度地提高保密通信速率。为了解决非凸问题，利用块坐标下降 （BCD） 和连续凸近似 （SCA） 方法将其转换为近似凸问题，然后我们设计了一种替代优化迭代算法来实现次优但高效的解决方案。此外，该文将开发的算法扩展到不完美信道状态信息（CSI）场景中，通过联合优化鲁棒无人机的轨迹和发射功率，最大限度地提高最坏情况的保密率，其中考虑了地面主、次和窃听设备的位置不确定性。仿真结果表明，与现有的最先进算法相比，所提出的联合优化算法在不同实际设置下显著提高了系统保密性能。