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...

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Bibliographic Details

Published in:	IEEE access Vol. 8; p. 1
Main Authors:	Abiko, Yu, Saito, Takato, Ikeda, Daizo, Ohta, Ken, Mizuno, Tadanori, Mineno, Hiroshi
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01-01-2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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 Access:	Get full text
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