Huang Jinming, Xu Sijie, Zhang Jun, Wu Yi
Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, Fujian Normal University, Fuzhou 350007, China.
Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Sensors (Basel). 2022 Mar 28;22(7):2590. doi: 10.3390/s22072590.
Equipping an unmanned aerial vehicle (UAV) with a mobile edge computing (MEC) server is an interesting technique for assisting terminal devices (TDs) to complete their delay sensitive computing tasks. In this paper, we investigate a UAV-assisted MEC network with air-ground cooperation, where both UAV and ground access point (GAP) have a direct link with TDs and undertake computing tasks cooperatively. We set out to minimize the maximum delay among TDs by optimizing the resource allocation of the system and by three-dimensional (3D) deployment of UAVs. Specifically, we propose an iterative algorithm by jointly optimizing UAV-TD association, UAV horizontal location, UAV vertical location, bandwidth allocation, and task split ratio. However, the overall optimization problem will be a mixed-integer nonlinear programming (MINLP) problem, which is hard to deal with. Thus, we adopt successive convex approximation (SCA) and block coordinate descent (BCD) methods to obtain a solution. The simulation results have shown that our proposed algorithm is efficient and has a great performance compared to other benchmark schemes.
为无人机（UAV）配备移动边缘计算（MEC）服务器是一种用于协助终端设备（TD）完成其对延迟敏感的计算任务的有趣技术。在本文中，我们研究了一种具有空地协作的无人机辅助MEC网络，其中无人机和地面接入点（GAP）都与终端设备有直接链路，并协同承担计算任务。我们旨在通过优化系统的资源分配和无人机的三维（3D）部署来最小化终端设备之间的最大延迟。具体而言，我们提出了一种迭代算法，通过联合优化无人机与终端设备的关联、无人机的水平位置、无人机的垂直位置、带宽分配和任务分割比例。然而，整体优化问题将是一个混合整数非线性规划（MINLP）问题，难以处理。因此，我们采用逐次凸逼近（SCA）和块坐标下降（BCD）方法来获得解决方案。仿真结果表明，我们提出的算法是高效的，并且与其他基准方案相比具有优异的性能。
