文章基本信息

标题：Computing resource allocation scheme of IOV using deep reinforcement learning in edge computing environment
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作者：Yiwei Zhang ; Min Zhang ; Caixia Fan 等
期刊名称：EURASIP Journal on Advances in Signal Processing
印刷版ISSN：1687-6172
电子版ISSN：1687-6180
出版年度：2021
卷号：2021
期号：1
页码：1
DOI：10.1186/s13634-021-00750-6
出版社：Hindawi Publishing Corporation
摘要：With the emergence and development of 5G technology, Mobile Edge Computing (MEC) has been closely integrated with Internet of Vehicles (IoV) technology, which can effectively support and improve network performance in IoV. However, the high-speed mobility of vehicles and diversity of communication quality make computing task offloading strategies more complex. To solve the problem, this paper proposes a computing resource allocation scheme based on deep reinforcement learning network for mobile edge computing scenarios in IoV. Firstly, the task resource allocation model for IoV in corresponding edge computing scenario is determined regarding the computing capacity of service nodes and vehicle moving speed as constraints. Besides, the mathematical model for task offloading and resource allocation is established with the minimum total computing cost as objective function. Then, deep Q-learning network based on deep reinforcement learning network is proposed to solve the mathematical model of resource allocation. Moreover, experience replay method is used to solve the instability of nonlinear approximate function neural network, which can avoid falling into dimension disaster and ensure the low-overhead and low-latency operation requirements of resource allocation. Finally, simulation results show that proposed scheme can effectively allocate the computing resources of IoV in edge computing environment. When the number of user uploaded data is 10K bits and the number of terminals is 15, it still shows the excellent network performance of low-overhead and low-latency.
关键词：Internet of Vehicles ; Mobile edge computing ; Reinforcement learning ; Experience replay method ; Resource allocation ; Offloading strategy