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

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
Main Authors: Yu Abiko, Takato Saito, Daizo Ikeda, Ken Ohta, Tadanori Mizuno, Hiroshi Mineno
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Deep reinforcement learning
network slicing
RAN slicing
resource management
Online Access:https://ieeexplore.ieee.org/document/9057705/

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