Deep Reinforcement Learning-Based Computation Offloading for 5G Vehicle-Aware Multi-Access Edge Computing Network

作     者：Ziying Wu Danfeng Yan Ziying Wu;Danfeng Yan

作者机构：State Key Laboratory of Network and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing 100876China 

出 版 物：《China Communications》 (中国通信（英文版）)

年 卷 期：2021年第18卷第11期

页      面：26-41页

核心收录：

学科分类：080904[工学-电磁场与微波技术] 0810[工学-信息与通信工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 0838[工学-公安技术] 081001[工学-通信与信息系统] 

基　　金：supported in part by the National Key R&D Program of China under Grant 2018YFC0831502 

主　　题：multi-access edge computing computation offloading 5G vehicle-aware deep reinforcement learning deep q-network 

摘      要：Multi-access Edge Computing(MEC)is one of the key technologies of the future 5G *** deploying edge computing centers at the edge of wireless access network,the computation tasks can be offloaded to edge servers rather than the remote cloud server to meet the requirements of 5G low-latency and high-reliability application ***,with the development of IOV(Internet of Vehicles)technology,various delay-sensitive and compute-intensive in-vehicle applications continue to *** with traditional Internet business,these computation tasks have higher processing priority and lower delay *** this paper,we design a 5G-based vehicle-aware Multi-access Edge Computing network(VAMECN)and propose a joint optimization problem of minimizing total system *** view of the problem,a deep reinforcement learningbased joint computation offloading and task migration optimization(JCOTM)algorithm is proposed,considering the influences of multiple factors such as concurrent multiple computation tasks,system computing resources distribution,and network communication ***,the mixed integer nonlinear programming problem is described as a Markov Decision *** show that our proposed algorithm can effectively reduce task processing delay and equipment energy consumption,optimize computing offloading and resource allocation schemes,and improve system resource utilization,compared with other computing offloading policies.