Deep Reinforcement Learning for Cooperative Content Caching in Vehicular Edge Computing and Networks

In this article, we propose a cooperative edge caching scheme, a new paradigm to jointly optimize the content placement and content delivery in the vehicular edge computing and networks, with the aid of the flexible trilateral cooperations among a macro-cell station, roadside units, and smart vehicl...

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Veröffentlicht in:	IEEE internet of things journal Jg. 7; H. 1; S. 247 - 257
Hauptverfasser:	Qiao, Guanhua, Leng, Supeng, Maharjan, Sabita, Zhang, Yan, Ansari, Nirwan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Base stations Caching Computational modeling Computer simulation Content delivery content placement Cooperative caching cooperative edge caching deep deterministic policy gradient (DDPG) double time-scale Markov decision process (DTS-MDP) Edge computing Indexes Integer programming Intelligent vehicles Internet of Things Linear programming Machine learning Markov processes Mixed integer Optimization Placement Roadsides vehicular edge computing and networks
ISSN:	2327-4662, 2327-4662
Online-Zugang:	Volltext
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