Multi-Agent Reinforcement Learning-Based Resource Management for End-to-End Network Slicing

Multi-Agent Reinforcement Learning-Based Resource Management for End-to-End Network Slicing

To meet the explosive growth of mobile traffic, the 5G network is designed to be flexible and support multi-access edge computing (MEC), thereby improving the end-to-end quality of service (QoS). In particular, 5G network slicing, which allows a physical infrastructure to split into multiple logical...

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Bibliographic Details
Main Authors: Yohan Kim, Hyuk Lim
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
5G
network slicing
multi-access edge computing
network resource management
multi-agent reinforcement learning
Online Access:https://ieeexplore.ieee.org/document/9400356/

Internet

https://ieeexplore.ieee.org/document/9400356/

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