移动通信2023，Vol.47Issue(12)：66-70,5.DOI:10.3969/j.issn.1006-1010.20230827-0001
基于强化学习的5G无线资源管理方法研究
Research on 5G Wireless Resource Management Method Based on Reinforcement Learning
张伟1
作者信息
- 1. 中国联合网络通信集团有限公司广东省分公司,广东广州 510630
- 折叠
摘要
Abstract
A 5G wireless resource management technology based on reinforcement learning is proposed to address the issue of inflexibility in current network slicing resource allocation schemes.This method aims to achieve adaptive network slicing dynamic optimization and end-to-end service reliability.Firstly,calculate different user priorities to realize the allocation strategy based on user priority differentiation.Then,consider the QoE of user service as a mapping between subjective views on service quality and specific network indicators.Finally,with the goal of maximizing the user QoS requirements and system throughput,a network slicing resource allocation scheme is implemented.Simulation shows that the proposed method effectively improves the throughput and fairness of the system and provides a reference for 5G wireless resource management.关键词
切片资源分配/用户分组/QoE/强化学习Key words
slice resource allocation/user grouping/QoE/reinforcement learning分类
信息技术与安全科学引用本文复制引用
张伟..基于强化学习的5G无线资源管理方法研究[J].移动通信,2023,47(12):66-70,5.
