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基于无人机辅助边缘计算系统的节能卸载策略
引用本文:余雪勇,朱烨,邱礼翔,朱洪波. 基于无人机辅助边缘计算系统的节能卸载策略[J]. 系统工程与电子技术, 2022, 44(3): 1022-1029. DOI: 10.12305/j.issn.1001-506X.2022.03.35
作者姓名:余雪勇  朱烨  邱礼翔  朱洪波
作者单位:1. 南京邮电大学江苏省无线通信重点实验室, 江苏 南京 2100032. 南京邮电大学教育部泛在网络健康服务系统工程研究中心, 江苏 南京 210003
基金项目:国家重大研究计划重点项目(92067201);国家自然科学基金(61871446);江苏省重点研发计划(BE2020084-4);江苏省无线通信重点实验室开放研究基金(710020017002);南京邮电大学自然科学基金(NY220047)资助课题。
摘    要:针对复杂地形中地面基础设施无法有效提供可靠通信和密集算力的问题,首先提出一种基于无人机(unmanned aerial vehicle, UAV)托管计算资源的卸载方案。考虑用户终端的计算需求,计算任务的时延约束,以及UAV的能量约束,构建了一种以最小化用户终端计算和卸载能耗为目标的UAV辅助边缘计算模型。其次,通过将原非凸的问题分解为两个凸优化子问题,采用了基于块坐标下降的两步迭代优化算法,联合优化了用户终端本地任务的数据量、卸载任务的数据量以及UAV的轨迹,实现约定时间内用户终端能耗的最小化。仿真结果表明,所提策略适用于优劣不同的信道条件,能够在保证用户终端完成任务的同时,使得用户终端能耗方面优于其他基准方案。

关 键 词:移动边缘计算  无人机通信  资源分配  轨迹优化
收稿时间:2021-07-12

Energy efficient offloading strategy for UAV aided edge computing systems
YU Xueyong,ZHU Ye,QIU Lixiang,ZHU Hongbo. Energy efficient offloading strategy for UAV aided edge computing systems[J]. System Engineering and Electronics, 2022, 44(3): 1022-1029. DOI: 10.12305/j.issn.1001-506X.2022.03.35
Authors:YU Xueyong  ZHU Ye  QIU Lixiang  ZHU Hongbo
Affiliation:1. Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China2. Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks, Ministry of Education, Nan University of Posts Telecommunications, Nanjing 210003, China
Abstract:Aiming at the problem that the ground infrastructure can not effectively provide reliable communication and intensive computing power in complex terrain, an unloading scheme based on unmanned aerial vehicle(UAV) managed computing resources is proposed firstly. Considering the computing requirements of user terminals, the delay constraints of computing tasks, and the energy constraints of UAVs, a UAV assisted edge computing model is constructed to minimize the energy consumption of user terminals. Secondly, by decomposing the original nonconvex problem into two convex optimization subproblems, a two-step iterative optimization algorithm based on block coordinate descent is adopted to jointly optimize the amount of data of the local task of the user terminal, the amount of data of the unloading task and the trajectory of the UAV, so as to minimize the energy consumption of the user terminal within the agreed time. The simulation results show that the proposed strategy is suitable for different channel conditions, and can ensure the user terminal to complete the task while making the user terminal energy consumption better than other benchmark schemes.
Keywords:mobile-edge computing  unmanned aerial vehicle(UAV)communication  resource allocation  trajectory optimizing
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