Flexible Resource Block Allocation to Multiple Slices for Radio Access Network Slicing Using Deep Reinforcement Learning

In the fifth-generation of mobile communications, network slicing is used to provide an optimal network for various services as a slice. In this paper, we propose a radio access network (RAN) slicing method that flexibly allocates RAN resources using deep reinforcement learning (DRL). In RANs, the n...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE access 2020-01, Vol.8, p.1-1
Hauptverfasser:	Abiko, Yu, Saito, Takato, Ikeda, Daizo, Ohta, Ken, Mizuno, Tadanori, Mineno, Hiroshi
Format:	Artikel
Sprache:	eng
Schlagworte:	5G mobile communication Deep learning Deep reinforcement learning Egress Environment models Machine learning Mobile communication systems Network slicing Optimization Radio access networks RAN slicing Resource allocation Resource management Scalability Wireless networks
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!