UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization
UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization
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DOI：
10.1109/twc.2019.2928539
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发表时间：
2019-10-01
影响因子：
10.4
通讯作者：
Zheng, Zhongbin
中科院分区：
文献类型：
--
作者：
Hu, Xiaoyan;Wong, Kai-Kit;Zheng, Zhongbin
在本文中，我们研究了一种无人机（UAV）辅助的移动边缘计算（MEC）架构，其中在区域内漫游的无人机可以充当计算服务器来帮助用户设备（UE）计算其任务，或者充当中继以进一步将其计算任务卸载到接入点（AP）。我们的目标是在任务约束、信息因果关系约束、带宽分配约束和无人机轨迹约束下，最小化无人机和用户设备的加权和能耗。所需的优化是非凸的，提出了一种交替优化算法，以迭代方式联合优化计算资源调度、带宽分配和无人机轨迹。数值结果表明，与传统方法相比，获得了显着的性能增益。此外，在处理计算密集型延迟关键任务时，所提出的算法的优势更加突出。
In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture, in which a UAV roaming around the area may serve as a computing server to help user equipment (UEs) compute their tasks or act as a relay for further offloading their computation tasks to the access point (AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints and the UAV's trajectory constraints. The required optimization is nonconvex, and an alternating optimization algorithm is proposed to jointly optimize the computation resource scheduling, bandwidth allocation, and the UAV's trajectory in an iterative fashion. The numerical results demonstrate that significant performance gain is obtained over conventional methods. Also, the advantages of the proposed algorithm are more prominent when handling computation-intensive latency-critical tasks.