Reinforcement Learning-Based Mobile Offloading for Edge Computing Against Jamming and Interference

doi:10.1109/TCOMM.2020.3007742

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	Reinforcement Learning-Based Mobile Offloading for Edge Computing Against Jamming and Interference
	Xiao, Liang 1; Lu, Xiaozhen 1; Xu, Tangwei 1; Wan, Xiaoyue 1; Ji, Wen 2,3; Zhang, Yanyong 4
刊名	IEEE TRANSACTIONS ON COMMUNICATIONS
	2020-10-01
卷号	68 期号:10 页码:6114-6126
关键词	Task analysis Mobile handsets Edge computing Computational modeling Interference Energy consumption Jamming Mobile offloading edge computing interference jamming reinforcement learning
ISSN号	0090-6778
DOI	10.1109/TCOMM.2020.3007742
英文摘要	Mobile edge computing systems help improve the performance of computational-intensive applications on mobile devices and have to resist jamming attacks and heavy interference. In this paper, we present a reinforcement learning based mobile offloading scheme for edge computing against jamming attacks and interference, which uses safe reinforcement learning to avoid choosing the risky offloading policy that fails to meet the computational latency requirements of the tasks. This scheme enables the mobile device to choose the edge device, the transmit power and the offloading rate to improve its utility including the sharing gain, the computational latency, the energy consumption and the signal-to-interference-plus-noise ratio of the offloading signals without knowing the task generation model, the edge computing model, and the jamming/interference model. We also design a deep reinforcement learning based mobile offloading for edge computing that uses an actor network to choose the offloading policy and a critic network to update the actor network weights to improve the computational performance. We discuss the computational complexity and provide the performance bound that consists of the computational latency and the energy consumption based on the Nash equilibrium of the mobile offloading game. Simulation results show that this scheme can reduce the computational latency and save energy consumption.
资助项目	Natural Science Foundation of China[61971366] ; National Key R&D Program of China[2017YFB1400100] ; Beijing Natural Science Foundation[4202072] ; Key Research Program of Frontier Sciences, CAS[ZDBS-LY-JSC001]
WOS研究方向	Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000579344400013
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/15698]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Xiao, Liang
作者单位	1.Xiamen Univ, Dept Informat & Commun Engn, Xiamen 361005, Fujian, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Peng Cheng Lab, Shenzhen 518055, Guangdong, Peoples R China 4.Univ Sci & Technol China, Sch Comp Sci & Technol, Hefei 230027, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Xiao, Liang,Lu, Xiaozhen,Xu, Tangwei,et al. Reinforcement Learning-Based Mobile Offloading for Edge Computing Against Jamming and Interference[J]. IEEE TRANSACTIONS ON COMMUNICATIONS,2020,68(10):6114-6126.
APA	Xiao, Liang,Lu, Xiaozhen,Xu, Tangwei,Wan, Xiaoyue,Ji, Wen,&Zhang, Yanyong.(2020).Reinforcement Learning-Based Mobile Offloading for Edge Computing Against Jamming and Interference.IEEE TRANSACTIONS ON COMMUNICATIONS,68(10),6114-6126.
MLA	Xiao, Liang,et al."Reinforcement Learning-Based Mobile Offloading for Edge Computing Against Jamming and Interference".IEEE TRANSACTIONS ON COMMUNICATIONS 68.10(2020):6114-6126.