This paper investigates the robust and secure task transmission and computation scheme in multi-antenna unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where the UAV is dual-function, i.e., aerial MEC and aerial relay. The channel uncertainty is considered during information offloading and downloading. An energy consumption minimization problem is formulated under some constraints including users' quality of service and information security requirements and the UAV's trajectory's causality, by jointly optimizing the CPU frequency, the offloading time, the beamforming vectors, the artificial noise and the trajectory of the UAV, as well as the CPU frequency, the offloading time and the transmission power of each user. To solve the non-convex problem, a reformulated problem is first derived by a series of convex reformation methods, i.e., semi-definite relaxation, S-Procedure and first-order approximation, and then, solved by a proposed successive convex approximation (SCA)-based algorithm. The convergence performance and computational complexity of the proposed algorithm are analyzed. Numerical results demonstrate that the proposed scheme outperform existing benchmark schemes. Besides, the proposed SCA-based algorithm is superior to traditional alternative optimization-based algorithm.

翻译：本文研究了多天线无人机（UAV）辅助移动边缘计算（MEC）网络中鲁棒且安全的任务传输与计算方案，其中无人机具有双重功能，即空中MEC与空中中继。考虑信息上传与下载过程中存在的信道不确定性。在包括用户服务质量与信息安全需求，以及无人机轨迹因果性的约束条件下，通过联合优化无人机的CPU频率、卸载时间、波束赋形向量、人工噪声与轨迹，以及各用户的CPU频率、卸载时间与传输功率，构建了一个能耗最小化问题。为解决该非凸问题，首先通过一系列凸重构方法（即半定松弛、S-过程与一阶近似）推导出重构问题，进而提出一种基于逐次凸近似（SCA）的算法进行求解。分析了所提算法的收敛性能与计算复杂度。数值结果表明，所提方案优于现有基准方案。此外，基于SCA的算法优于传统的交替优化算法。