ShanghaiTech University Knowledge Management System
Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Satellite-Terrestrial Integrated Networks | |
2022 | |
会议录名称 | IEEE VEHICULAR TECHNOLOGY CONFERENCE |
ISSN | 1550-2252 |
卷号 | 2022-June |
发表状态 | 已发表 |
DOI | 10.1109/VTC2022-Spring54318.2022.9860361 |
摘要 | Satellite mobile edge computing (SMEC) enhanced satellite-terrestrial integrated networks (STIN) have attracted intensive attention to obtain seamless coverage and provide on-demand computation services. However, the cooperative task execution among low earth orbit (LEO) satellites is largely ignored in the SMEC-STIN. In this paper, we explore a hybrid cloud and edge computing architecture of the SMEC-STIN with coordinated task processing among neighboring LEO satellites. We investigate the computation offloading and resource allocation strategies to minimize the long-term cost in terms of a trade-off between task execution latency and energy consumption. We formulate the optimization problem as a Markov decision process and design a proximal policy optimization based deep reinforcement learning method to approximate the optimal solution with robust training stability and low storage demand. Simulation results validate the effectiveness of our proposed method. © 2022 IEEE. |
会议录编者/会议主办者 | Huawei ; Nokia ; pix moving ; Samsung ; Technology Innovation Institute (TII) |
关键词 | computation offloading Deep learning Economic and social effects Energy utilization Markov processes Mobile edge computing Orbits Resource allocation Satellites Computation offloading Computation resources Deep reinforcement learning Integrated networks Low earth orbit satellites On-demand computations Reinforcement learnings Resources allocation Satellite-terrestial integrated network Task executions |
会议名称 | 95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring |
出版地 | 345 E 47TH ST, NEW YORK, NY 10017 USA |
会议地点 | Helsinki, Finland |
会议日期 | June 19, 2022 - June 22, 2022 |
URL | 查看原文 |
收录类别 | EI ; CPCI ; CPCI-S |
语种 | 英语 |
资助项目 | Shanghai Pujiang Program[2020PJD081] ; Shanghai Technical Standard Project[21DZ2200200] |
WOS研究方向 | Engineering ; Transportation |
WOS类目 | Engineering, Electrical & Electronic ; Transportation Science & Technology |
WOS记录号 | WOS:000861825800011 |
出版者 | Institute of Electrical and Electronics Engineers Inc. |
EI入藏号 | 20223712740901 |
EI主题词 | Reinforcement learning |
EI分类号 | 461.4 Ergonomics and Human Factors Engineering ; 525.3 Energy Utilization ; 655.2 Satellites ; 722.4 Digital Computers and Systems ; 723 Computer Software, Data Handling and Applications ; 723.4 Artificial Intelligence ; 912.2 Management ; 922.1 Probability Theory ; 971 Social Sciences |
原始文献类型 | Conference article (CA) |
来源库 | IEEE |
引用统计 | 正在获取...
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文献类型 | 会议论文 |
条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/232010 |
专题 | 信息科学与技术学院_硕士生 信息科学与技术学院_特聘教授组_卜智勇组 |
通讯作者 | Wu, Haonan |
作者单位 | 1.Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Shanghai, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.ShanghaiTech Univ, Shanghai, Peoples R China 4.Chinese Acad Sci, Key Lab Wireless Sensor Network & Commun, Shanghai, Peoples R China |
第一作者单位 | 上海科技大学 |
通讯作者单位 | 上海科技大学 |
推荐引用方式 GB/T 7714 | Wu, Haonan,Yang, Xiumei,Bu, Zhiyong. Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Satellite-Terrestrial Integrated Networks[C]//Huawei, Nokia, pix moving, Samsung, Technology Innovation Institute (TII). 345 E 47TH ST, NEW YORK, NY 10017 USA:Institute of Electrical and Electronics Engineers Inc.,2022. |
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