Deep Reinforcement Learning for Computation Offloading and Resource Allocation in Satellite-Terrestrial Integrated Networks
2022
会议录名称IEEE VEHICULAR TECHNOLOGY CONFERENCE
ISSN1550-2252
卷号2022-June
发表状态已发表
DOI10.1109/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
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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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