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DOI	10.1109/WCNC49053.2021.9417127
	Deep Reinforcement Learning-based Task Offloading in Satellite-Terrestrial Edge Computing Networks
	Zhu, Dali; Liu, Haitao; Li, Ting; Sun, Jiyan; Liang, Jie; Zhang, Hangsheng; Geng, Liru; Liu, Yudong
通讯作者	Liu, YD (corresponding author), Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China. ; Liu, YD (corresponding author), Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China.
会议名称	IEEE Wireless Communications and Networking Conference (WCNC)
会议日期	MAR 29-APR 01, 2021
会议地点	Nanjing, PEOPLES R CHINA
英文摘要	In remote regions (e.g., mountain and desert), cellular networks are usually sparsely deployed or unavailable. With the appearance of new applications (e.g., industrial automation and environment monitoring) in remote regions, resource-constrained terminals become unable to meet the latency requirements. Meanwhile, offloading tasks to urban terrestrial cloud (TC) via satellite link will lead to high delay. To tackle above issues, Satellite Edge Computing architecture is proposed, i.e., users can offload computing tasks to visible satellites for executing. However, existing works are usually limited to offload tasks in pure satellite networks, and make offloading decisions based on the predefined models of users. Besides, the runtime consumption of existing algorithms is rather high. In this paper, we study the task offloading problem in satellite-terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. The proposed Deep Reinforcement learning-based Task Offloading (DRTO) algorithm can accelerate learning process by adjusting the number of candidate locations. In addition, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that DRTO achieves near-optimal offloading cost performance with much less runtime consumption, which is more suitable for satellite-terrestrial network with fast fading channel.
英文关键词	Satellite-terrestrial networks Edge computing Deep reinforcement learning Computation offloading Mixed-integer programming
来源出版物	2021 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE (WCNC)
ISSN	1525-3511
出版年	2021
ISBN	978-1-7281-9505-6
出版者	IEEE
类型	Proceedings Paper
语种	英语
开放获取类型	Green Submitted
收录类别	CPCI-S
WOS记录号	WOS:000704226500014
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/365654
作者单位	[Zhu, Dali; Liu, Haitao; Li, Ting; Sun, Jiyan; Liang, Jie; Zhang, Hangsheng; Geng, Liru; Liu, Yudong] Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China; [Zhu, Dali; Liu, Haitao; Li, Ting; Liang, Jie; Zhang, Hangsheng; Liu, Yudong] Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zhu, Dali,Liu, Haitao,Li, Ting,et al. Deep Reinforcement Learning-based Task Offloading in Satellite-Terrestrial Edge Computing Networks[C]:IEEE,2021.