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Mobility-Aware Partial Computation Offloading in Vehicular Networks: A Deep Reinforcement Learning Based Scheme

查看全文 作  者:Jianfei [1]Wang;Tiejun [1]Lv;Pingmu [1]Huang;P.Takis [2]Mathiopoulos 高影响力作者 机构地区:[1]School of Information and Communication Engineering,Beijing University of Posts and Telecommunications(BUPT),Beijing 100876,China;[2]Department of Informatics and Telecommunications,National and Kapodistrian University of Athens,Athens 15784,Greece高影响力机构 出  处:《China Communications》索引2020年第17卷第10期,共19页高影响力期刊 基  金:the National Natural Science Foundation of China(NSFC)(Grant No.61671072). 摘  要:Encouraged by next-generation networks and autonomous vehicle systems,vehicular networks must employ advanced technologies to guarantee personal safety,reduce traffic accidents and ease traffic jams.By leveraging the computing ability at the network edge,multi-access edge computing(MEC)is a promising technique to tackle such challenges.Compared to traditional full offloading,partial offloading offers more flexibility in the perspective of application as well as deployment of such systems.Hence,in this paper,we investigate the application of partial computing offloading in-vehicle networks.In particular,by analyzing the structure of many emerging applications,e.g.,AR and online games,we convert the application structure into a sequential multi-component model.Focusing on shortening the application execution delay,we extend the optimization problem from the single-vehicle computing offloading(SVCOP)scenario to the multi-vehicle computing offloading(MVCOP)by taking multiple constraints into account.A deep reinforcement learning(DRL)based algorithm is proposed as a solution to this problem.Various performance evaluation results have shown that the proposed algorithm achieves superior performance as compared to existing offloading mechanisms in deducing application execution delay. 关 键 词:partial offloading MEC fog computing vehicular networks D2D AR
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