空地协同移动边缘计算系统的资源分配和轨迹优化  

Resource allocation and trajectory optimization for air-groundcooperative mobile edge computing systems

作　　者：李智灏 李俊杰 崔苗[1] 张广驰[1] Li Zhihao;Li Junjie;Cui Miao;Zhang Guangchi(School of Information Engineering,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学信息工程学院,广州510006

出　　处：《计算机应用研究》2024年第12期3807-3813,共7页Application Research of Computers

基　　金：广东省海洋经济发展项目(粤自然资合[2023]24号);广东省科技计划资助项目(2023A0505050127,2022A0505050023,2022A0505020008);广东省基础与应用基础研究基金资助项目(2023A1515011980)。

摘　　要：随着车联网的普及应用,车辆需要完成大量的实时计算任务,为了增强车辆的无线连接与计算能力,引入具有高机动性和按需部署优点的无人机进行辅助是一种有效方法。因此,研究了一个面向车联网的空地协同移动边缘计算系统,该系统由分别部署在无人机和地面基站的移动边缘计算服务器组成,协作为车辆提供通信和计算服务。为了最小化车辆的最大平均通信计算时延,研究了一个联合优化无人机和地面基站的通信带宽分配、计算任务卸载比例分配、无人机轨迹和计算资源分配的问题。为求解这个非凸优化问题,提出一种基于块坐标下降法和连续凸优化方法的高效交替优化算法,将原问题分解为带宽分配、计算任务卸载比例分配、无人机轨迹优化和计算资源分配四个子问题,并引入松弛变量和利用一阶泰勒展开的方法对子问题进行交替迭代求解。仿真结果表明,与多种基准方案相比,该算法能够有效地降低车辆的平均通信计算时延。这证明了无人机和地面基站的空地协作对车联网的通信与计算能力提升的重要性。With the popularity and application of Internet-of-Vehicles(IoV),vehicles need to accomplish a large number of real-time computational tasks.To enhance the wireless connectivity and computational capability of the vehicles,the introduction of unmanned aerial vehicle(UAV)with the advantages of high mobility and on-demand deployment for assistance is an effective method.Therefore,this paper studied an air-ground collaborative mobile edge computing system for the IoV,which consisted of mobile edge computing servers deployed at a UAV and ground base stations,respectively,to collaborate in providing communication and computation services for vehicles.In order to minimize the maximum average communication and computational delay of the vehicles,this paper studied a problem of jointly optimizing communication bandwidth allocation between the UAV and ground base stations,computational task offload ratio allocation,UAV trajectory and computational resource allocation.To solve this non-convex optimization problem,this paper proposed an efficient alternating optimization algorithm based on block coordinate descent and successive convex optimization methods,which was decomposed into four sub-problems:bandwidth allocation,computational task offload ratio allocation,UAV trajectory optimization and computational resource allocation,and introduced slack variables and utilized a first-order Taylor expansion to solve the sub-problems alternately and itera-tively.Simulation results show that the proposed algorithm can effectively reduce the average communication and computa-tional delay of vehicles compared to the multiple benchmark schemes.This conclusion demonstrates the importance of air-ground collaboration between the UAV and ground base stations to enhance the communication and computation capabilities of the IoV.

关 键 词：无人机通信 移动边缘计算 带宽分配 飞行轨迹设计 车联网通信 

分 类 号：TN929.5[电子电信—通信与信息系统]