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 2020-01, Vol.7 (1), p.247-257
Hauptverfasser:	Qiao, Guanhua, Leng, Supeng, Maharjan, Sabita, Zhang, Yan, Ansari, Nirwan
Format:	Artikel
Sprache:	eng
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
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