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Received:November 24, 2020 Revised:December 22, 2020
Received:November 24, 2020 Revised:December 22, 2020
中文摘要: 随着智能电网的不断发展, 电力服务种类的多样化引出了不同的服务需求. 5G中的网络切片技术, 可以为智能电网提供虚拟化无线专用网络, 以应对智能电网安全性、可靠性、时延性等方面的诸多挑战. 考虑到智能电网的差异化服务特性, 本文旨在使用深度强化学习(DRL)来解决智能电网的无线接入网(RAN)切片的资源分配问题. 文章首先回顾了智能电网的背景以及网络切片技术的相关研究, 随后分析了智能电网的RAN切片模型, 并且提出了一种基于DRL的切片分配策略. 仿真表明, 本文所提出的算法能够在降低成本的同时, 最大限度地满足智能电网在RAN侧的资源分配需求.
Abstract:With the continuous development of smart grids, diversified power service types lead to different service demands. The 5G network slicing technology can provide virtual wireless private networks for smart grids in response to challenges in security, reliability, and time delay. Considering the differentiated service characteristics of smart grids, this study aims to use Deep Reinforcement Learning (DRL) to solve the resource allocation of the Radio Access Network (RAN) slices of smart grids. This study reviews the background of smart grids and the related research on network slicing technology, then analyzes the RAN slicing model of smart grids, and proposes a slice allocation strategy based on DRL. Simulation results show that the proposed algorithm can reduce the cost and meet the resource allocation requirements of smart grids on the RAN side to the maximum extent .
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基金项目:江苏省2019年度第二批省级工业和信息产业转型升级专项资金（5246DR180077）
引用文本：
张影,龚亮亮,胡阳,丁仪,姬昊.基于深度强化学习的智能电网RAN切片策略.计算机系统应用,2021,30(8):293-299
ZHANG Ying,GONG Liang-Liang,HU Yang,DING Yi,JI Hao.RAN Slicing Strategy for Smart Grid Based on Deep Reinforcement Learning.COMPUTER SYSTEMS APPLICATIONS,2021,30(8):293-299
张影,龚亮亮,胡阳,丁仪,姬昊.基于深度强化学习的智能电网RAN切片策略.计算机系统应用,2021,30(8):293-299
ZHANG Ying,GONG Liang-Liang,HU Yang,DING Yi,JI Hao.RAN Slicing Strategy for Smart Grid Based on Deep Reinforcement Learning.COMPUTER SYSTEMS APPLICATIONS,2021,30(8):293-299
