This article investigates the unmanned-aerial-vehicle (UAV)-enabled wireless powered cooperative mobile edge computing (MEC) system, where a UAV installed with an energy transmitter (ET) and an MEC server provides both energy and computing services to sensor devices (SDs). The active SDs desire to complete their computing tasks with the assistance of the UAV and their neighboring idle SDs that have no computing task. An optimization problem is formulated to minimize the total required energy of UAV by jointly optimizing the CPU frequencies, the offloading amount, the transmit power, and the UAV’s trajectory. To tackle the nonconvex problem, a successive convex approximation (SCA)-based algorithm is designed. Since it may be with relatively high computational complexity, as an alternative, a decomposition and iteration (DAI)-based algorithm is also proposed. The simulation results show that both proposed algorithms converge within several iterations, and the DAI-based algorithm achieve the similar minimal required energy and optimized trajectory with the SCA-based one. Moreover, for a relatively large amount of data, the SCA-based algorithm should be adopted to find an optimal solution, while for a relatively small amount of data, the DAI-based algorithm is a better choice to achieve smaller computing energy consumption. It also shows that the trajectory optimization plays a dominant factor in minimizing the total required energy of the system and optimizing acceleration has a great effect on the required energy of the UAV. Additionally, by jointly optimizing the UAV’s CPU frequencies and the amount of bits offloaded to UAV, the minimal required energy for computing can be greatly reduced compared to other schemes and by leveraging the computing resources of idle SDs, the UAV’s computing energy can also be greatly reduced.

中文翻译：

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无人机辅助的无线协作移动边缘计算：联合卸载，CPU控制和轨迹优化

本文研究了启用了无人机的无线协作式移动边缘计算（MEC）系统，其中装有能量发射器（ET）和MEC服务器的无人机为传感器设备提供能量和计算服务（ SDs）。主动SD希望借助UAV及其附近没有计算任务的空闲SD来完成其计算任务。通过联合优化CPU频率，卸载量，发射功率和UAV的轨迹，提出了一个优化问题，以使UAV所需的总能量最小化。为了解决非凸问题，设计了一种基于连续凸逼近法的算法。由于它可能具有较高的计算复杂度，因此，还提出了一种基于分解和迭代（DAI）的算法。仿真结果表明，两种算法都在多次迭代中收敛，并且基于DAI的算法与基于SCA的算法具有相似的最小所需能量和最佳轨迹。此外，对于相对大量的数据，应采用基于SCA的算法来找到最佳解决方案，而对于相对少量的数据，基于DAI的算法是实现较小计算能耗的更好选择。这也表明，轨迹优化在最小化系统总能量需求中起着主导作用，而优化加速度对无人机的能量需求有很大影响。此外，通过共同优化无人机的CPU频率和卸载到无人机的位数，