Title
Deep Reinforcement Learning for Edge Caching and Content Delivery in Internet of Vehicles
Abstract
To enable the emerging vehicular applications and multimedia services in an Internet of Vehicles (IoV) framework, edge caching is a promising paradigm which can cache content in proximity to vehicles, and thus alleviate heavy load on backhaul links and contribute in reducing transmission latency. However, in a multi-access vehicular network, complex content delivery and high mobility of vehicles introduce new challenges to support edge caching in a dynamic environment. Deep Reinforcement Learning (DRL) is an emerging technique to solve the issue with time-varying feature. In this paper, we utilize DRL to design an optimal vehicular edge caching and content delivery strategy for minimizing content delivery latency. We first propose a multiaccess edge caching and content delivery framework in vehicular networks. Then, we formulate the vehicular edge caching and content delivery problem and propose a novel DRL algorithm to solve it. Numerical results demonstrate the effectiveness of proposed DRL-based algorithm, compared to two benchmark solutions.
Year
DOI
Venue
2019
10.1109/ICCChina.2019.8855951
2019 IEEE/CIC International Conference on Communications in China (ICCC)
Keywords
Field
DocType
content delivery strategy,multiaccess edge caching,content delivery framework,vehicular networks,content delivery problem,multiaccess vehicular network,complex content delivery,optimal vehicular edge caching,vehicular applications,Deep Reinforcement Learning,Internet of Vehicles framework,content delivery latency,multimedia services
Content delivery,Backhaul (telecommunications),Computer science,Cache,Latency (engineering),Computer network,Transmission latency,Vehicular ad hoc network,The Internet,Reinforcement learning
Conference
ISSN
ISBN
Citations 
2377-8644
978-1-7281-0733-2
0
PageRank 
References 
Authors
0.34
10
5
Name
Order
Citations
PageRank
Yueyue Dai111.36
Xu Du23715.92
Yunlong Lu300.34
Sabita Maharjan4107852.89
Yan Zhang55818354.13