无人机辅助MEC系统中面向用户公平性的三维部署和卸载优化  

User Fairness Oriented 3D Deployment and Unloading Optimization in UAV-assisted MEC System

作　　者：林诚章 吴涛[1] 周启钊 陈曦[2] LIN Cheng-Zhang;WU Tao;ZHOU Qi-Zhao;CHEN Xi(School of Computer Science,Chengdu University of Information Technology,Chengdu 610225,China;School of Computer Science and Technology,Southwest Minzu University,Chengdu 610225,China)

机构地区：[1]成都信息工程大学计算机学院,成都610225 [2]西南民族大学计算机科学与工程学院,成都610225

出　　处：《计算机系统应用》2024年第1期157-166,共10页Computer Systems & Applications

基　　金：四川重点研发计划(23ZDYF0171,23RKX0645);成都信息工程大学引进人才科研启动项目(KYTZ202269)。

摘　　要：针对无人机辅助移动边缘计算系统存在的用户公平性不足问题,本文提出了一种面向用户公平性的三维部署和卸载优化算法.该算法综合考虑用户匹配、无人机三维部署、计算资源分配、卸载因子对系统总时延及用户公平性的影响,建立了一个最小化系统总时延的多元优化问题,并针对该问题提出了一种两阶段联合优化算法,其中第1阶段使用带有平衡约束的聚类算法解决用户匹配和无人机的水平部署问题,第2阶段使用凸优化算法迭代求解无人机高度部署,资源分配和卸载因子优化问题.实验结果表明,与4种基准算法相比,所提算法在系统总时延和用户公平性两方面具有更好的性能.Aiming at insufficient user fairness in UAV-assisted mobile edge computing systems,this study proposes a user fairness-oriented 3D deployment and unloading optimization algorithm.The algorithm comprehensively considers the effects of user matching,3D UAV deployment,computing resource allocation,and unloading factors on the total system delay and user fairness.Meanwhile,a multivariate optimization problem is established to minimize the total system delay,and a two-stage joint optimization algorithm is put forward for this problem.In the first stage,a clustering algorithm with balanced constraints is adopted to solve the problem of user matching and horizontal UAV deployment.In the second stage,the convex optimization algorithm is utilized to iteratively solve the UAV altitude deployment,resource allocation,and optimization problems of unloading factors.The experimental results show that the proposed algorithm has better performance than the four benchmark algorithms in both total system latency and user fairness.

关 键 词：无人机 移动边缘计算 计算卸载 三维部署 凸优化 

分 类 号：V279[航空宇航科学与技术—飞行器设计] TN929.5[电子电信—通信与信息系统]