Cost-Effective Task Offloading and Resource Scheduling for Mobile Edge Computing in 6G Space-Air-Ground Integrated Network

作     者：Zhu, Wenwu Deng, Xiaoheng Gui, Jinsong Zhang, Honggang Min, Geyong 

作者机构：Central South University School of Electronic Information Changsha410075 China School of Electronic Information Shenzhen Research Institute Central South University Changsha410075 China Umass Boston Engineering Department BostonMA02125 United States University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom 

出 版 物：《IEEE Internet of Things Journal》 (IEEE Internet Things J.)

年 卷 期：2025年第12卷第12期

页      面：19428-19442页

核心收录：

学科分类：0810[工学-信息与通信工程] 1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 0808[工学-电气工程] 1001[医学-基础医学(可授医学、理学学位)] 08[工学] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported in part by the National Natural Science Foundation of China Project under Grant 62172441 and Grant 62172449 in part by the Joint Funds for Railway Fundamental Research of National Natural Science Foundation of China under Grant U2368201 in part by the Special Fund of National Key Laboratory of Ni & Co Associated Minerals Resources Development and Comprehensive Utilization under Grant GZSYSKY-2022-018 and Grant GZSYSKY-2022-024 in part by the Key Project of Shenzhen City Special Fund for Fundamental Research under Grant JCYJ20220818103200002 in part by the Postgraduate Innovative Project of Central South University under Grant 2023XQLH008 in part by the National Natural Science Foundation of Hunan Province under Grant 2023JJ30696 and in part by the High Performance Computing Center of Central South University 

主　　题：Unmanned aerial vehicles (UAV) 

摘      要：With the advent of the sixth-generation (6G) wireless communications, transmission speeds are projected to exceed tenfold those of 5G, reaching theoretical peak download speeds of up to 1 Tbps. Data transmission capacity and speed will be significantly enhanced, enabling emerging applications, such as mixed reality, federated learning, and digital twins, driving exponential data traffic growth. To address this, the space-air-ground integrated network (SAGIN) combines satellite, aerial, and ground communication technologies, offering seamless global coverage and high-speed connectivity. In this article, we proposes an SAGIN framework integrated with mobile edge computing (MEC) to jointly optimize system energy consumption and delay costs. Specifically, we decompose the optimization problem into three subproblems: 1) uncrewed aerial vehicle (UAV) computational resource allocation;2) satellite computational resource allocation;and 3) task offloading and channel allocation. The subproblems are then transformed and addressed using Newton s interior point method and the deep reinforcement learning DQN algorithm to derive optimal allocation strategies for UAV and satellite computing resources, along with task offloading and channel resources, that our proposed algorithm effectively reduces system energy consumption and delay costs compared to other algorithms. © 2025 IEEE.