Unmanned aerial vehicles (UAVs) can provide wireless access services to terrestrial users without geographical limitations and will become an essential part of the future communication system. However, the openness of wireless channels and the mobility of UAVs make the security of UAV-based communication systems particularly challenging. This work investigates the security of aerial cognitive radio networks (CRNs) with multiple uncertainties colluding eavesdroppers. A cognitive aerial base station transmits messages to cognitive terrestrial users using the spectrum resource of the primary users. All secondary terrestrial users and illegitimate receivers jointly decode the received message. The average secrecy rate of the aerial CRNs is maximized by jointly optimizing the UAV's trajectory and transmission power. An iterative algorithm based on block coordinate descent and successive convex approximation is proposed to solve the non-convex mixed-variable optimization problem. Numerical results verify the effectiveness of our proposed algorithm and show that our scheme improves the secrecy performance of airborne CRNs.

翻译：无人机可为地面用户提供不受地理限制的无线接入服务，将成为未来通信系统的重要组成部分。然而，无线信道的开放性和无人机的移动性使得基于无人机的通信系统安全性面临严峻挑战。本研究针对存在多不确定性协同窃听者的空中认知无线电网络安全性展开研究。认知空中基站利用主用户频谱资源向认知地面用户传输信息，所有次级地面用户与非法接收者共同对接收信号进行解码。通过联合优化无人机轨迹与传输功率，实现空中认知无线电网络的平均保密速率最大化。针对该非凸混合变量优化问题，提出基于块坐标下降与逐次凸逼近的迭代算法。数值结果验证了所提算法的有效性，并表明所提方案能有效提升空中认知无线电网络的保密性能。