Multi-UAV Collaborative Edge Computing Algorithm for Joint Task Offloading and Channel Resource Allocation

作     者：Yuting Wei Sheng Wu Zhe Ji Zhigang Yu Chunxiao Jiang Linling Kuang Yuting Wei;Sheng Wu;Zhe Ji;Zhigang Yu;Chunxiao Jiang;Linling Kuang

作者机构：School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China China Academy of Electronics and Information TechnologyBeijing 100041China Beijing National Research Center for Information Science and TechnologyTsinghua Space CenterTsinghua UniversityBeijing 100084China 

出 版 物：《Journal of Communications and Information Networks》 (通信与信息网络学报（英文）)

年 卷 期：2024年第9卷第2期

页      面：137-150页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

基　　金：supported in part by the National Natural Science Foundation of China under Grants 62341104 62201085 62325108 and 62341131 

主　　题：UAV-based edge computing multi-UAV collaboration joint task offloading and wireless channel allocation simulated annealing(SA)algorithm multi-agent deep deterministic policy gradient(MADDPG) 

摘      要：Unmanned aerial vehicle (UAV)-based edge computing is an emerging technology that provides fast task processing for a wider area. To address the issues of limited computation resource of a single UAV and finite communication resource in multi-UAV networks, this paper joints consideration of task offloading and wireless channel allocation on a collaborative multi-UAV computing network, where a high altitude platform station (HAPS)is adopted as the relay device for communication between UAV clusters consisting of UAV cluster heads (ch-UAVs) and mission UAVs (m-UAVs). We propose an algorithm, jointing task offloading and wireless channel allocation to maximize the average service success rate (ASSR)of a period time. In particular,the simulated annealing(SA)algorithm with random perturbations is used for optimal channel allocation,aiming to reduce interference and minimize transmission delay.A multi-agent deep deterministic policy gradient (MADDPG) is proposed to get the best task offloading strategy. Simulation results demonstrate the effectiveness of the SA algorithm in channel allocation. Meanwhile,when jointly considering computation and channel resources,the proposed scheme effectively enhances the ASSR in comparison to other benchmark algorithms.