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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Vydané v:	IEEE internet of things journal Ročník 7; číslo 1; s. 247 - 257
Hlavní autori:	Qiao, Guanhua, Leng, Supeng, Maharjan, Sabita, Zhang, Yan, Ansari, Nirwan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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