QoE-Driven Edge Caching in Vehicle Networks Based on Deep Reinforcement Learning | |
Song CH(宋纯贺)1; Xu WX(徐文想)1; Wu TT(武婷婷)1; Yu SM(于诗矛)1; Zeng P(曾鹏)1; Zhang N(张宁)2 | |
刊名 | IEEE Transactions on Vehicular Technology |
2021 | |
卷号 | 70期号:6页码:5286-5295 |
关键词 | Internet of vehicles roadside units cache update quality of experience deep reinforcement learning |
ISSN号 | 0018-9545 |
产权排序 | 1 |
英文摘要 | The Internet of vehicles (IoV) is a large information interaction network that collects information on vehicles, roads and pedestrians. One of the important uses of vehicle networks is to meet the entertainment needs of driving users through communication between vehicles and roadside units (RSUs). Due to the limited storage space of RSUs, determining the content cached in each RSU is a key challenge. With the development of 5G and video editing technology, short video systems have become increasingly popular. Current widely used cache update methods, such as partial file precaching and content popularity- and user interest-based determination, are inefficient for such systems. To solve this problem, this paper proposes a QoE-driven edge caching method for the IoV based on deep reinforcement learning. First, a class-based user interest model is established. Compared with the traditional file popularity- and user interest distribution-based cache update methods, the proposed method is more suitable for systems with a large number of small files. Second, a quality of experience (QoE)-driven RSU cache model is established based on the proposed class-based user interest model. Third, a deep reinforcement learning method is designed to address the QoE-driven RSU cache update issue effectively. The experimental results verify the effectiveness of the proposed algorithm. |
资助项目 | National Key R&D Program of China[2018YFB1700200] ; National Nature Science Foundation of China[U1908212] ; National Nature Science Foundation of China[61773368] ; Shenzhen Science and Technology Innovation Committee[JCYJ20190809145407809] ; Revitalizing Liaoning Outstanding Talents[XLYC1907057] ; State Grid Corporation Science and Technology[SG2NK00DWJS1800123] ; Industrial Internet Innovation Development Project Edge computing test bed |
WOS关键词 | ALLOCATION ; DELIVERY ; SERVICES |
WOS研究方向 | Engineering ; Telecommunications ; Transportation |
语种 | 英语 |
WOS记录号 | WOS:000671544000010 |
资助机构 | National Key R and D Program of China under Grant 2018YFB1700200 ; National Nature Science Foundation of China under Grants U1908212 and 61773368 ; Industrial Internet innovation development project |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/28908] |
专题 | 沈阳自动化研究所_工业控制网络与系统研究室 |
通讯作者 | Zeng P(曾鹏) |
作者单位 | 1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, China 2.Department of Electrical and Computer Engineering University of Windsor, Windsor, Ontario, Canada, N9B 3P4 |
推荐引用方式 GB/T 7714 | Song CH,Xu WX,Wu TT,et al. QoE-Driven Edge Caching in Vehicle Networks Based on Deep Reinforcement Learning[J]. IEEE Transactions on Vehicular Technology,2021,70(6):5286-5295. |
APA | Song CH,Xu WX,Wu TT,Yu SM,Zeng P,&Zhang N.(2021).QoE-Driven Edge Caching in Vehicle Networks Based on Deep Reinforcement Learning.IEEE Transactions on Vehicular Technology,70(6),5286-5295. |
MLA | Song CH,et al."QoE-Driven Edge Caching in Vehicle Networks Based on Deep Reinforcement Learning".IEEE Transactions on Vehicular Technology 70.6(2021):5286-5295. |
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