摘要
使用灵活性机动性俱佳的无人机与地面基站组成空地协同移动边缘计算(MEC)系统,能有效保障具有高移动性用户的车联网的MEC服务质量。为了适应车联网用户位置的实时随机变化特性,研究在线式的空地协同MEC系统的通信与计算资源分配优化,研究将无人机的飞行时间等分为多个时隙。在每个当前时隙,使用基于卡尔曼滤波的方法预测下一个时隙的车辆用户的位置。接着,根据当前时隙的资源分配情况,研究一个联合优化系统的通信带宽、计算任务卸载比例、无人机轨迹和计算资源的问题,最小化车辆用户的在下一个时隙的通信计算时延。为高效求解该非凸优化难题,提出一种高效交替优化算法,将原问题分解为带宽分配子问题、计算任务卸载比例分配子问题、无人机轨迹优化子问题和计算资源分配子问题,并对这些子问题进行交替迭代求解。仿真结果验证了所提在线优化算法具有良好的实时性表现,能够明显降低车辆任务所需的通信计算时延,显示了实时优化通信与计算资源在车联网中的重要性和可行性。
An air-ground collaborative mobile edge computing(MEC)system composed of flexible and maneuverable unmanned aerial vehicles(UAV)and ground base stations can effectively guarantee the MEC service quality of the Internet of Vehicles for highly mobile users.In order to adapt to the real-time and random variation characteristics of the positions of IoV users,this paper studied the optimization of communication and computing resource allocation in an online air-ground collaborative MEC system.This research divided the flight time of UAV into multiple equal-length time slots.At each current time slot,this paper employed a Kalman filter-based method to predict the positions of vehicle users in the next time slot.Subsequently,based on the resource allocation in the current time slot,it studied a problem of jointly optimizing the communication bandwidth,computing task offloading ratio,UAV trajectory,and computing resources of the system to minimize the communication-computation delay of vehicle users in the next future time slot.To efficiently solve this non-convex optimization problem,this paper proposed an efficient alternating optimization algorithm.It decomposed the original problem into sub-problems of bandwidth allocation,computing task offloading ratio allocation,UAV trajectory optimization,and computing resource allocation,and solved these sub-problems by alternating iteration.The simulation results verify that the proposed online optimization algorithm exhibits excellent real-time performance.It can significantly reduce the communication and computation delay required for vehicle tasks,demonstrating the importance and feasibility of real-time optimization of communication and computing resources in the connected vehicle network.
作者
付珍
李智灏
郑涛
骆博雅
崔苗
张广驰
Fu Zhen;Li Zhihao;Zheng Tao;Luo Boya;Cui Miao;Zhang Guangchi(Jiangxi Institute of Civil Military Integration,Nanchang 300114,China;School of Information Engineering,Guangdong University of Technology,Guangzhou 510006,China)
出处
《计算机应用研究》
北大核心
2025年第8期2474-2481,共8页
Application Research of Computers
基金
广东省科技计划资助项目(2023A0505050127,2022A0505050023)
广东省基础与应用基础研究基金资助项目(2023A1515011980)
江西省军民融合研究院“北斗+”项目(2024JXRH0Y02)。
关键词
无人机通信
空地协同
移动边缘计算
在线优化
资源信息预测
UAV communication
air-ground collaboration
mobile edge computing
online optimization
resource information prediction
