无线电工程2024，Vol.54Issue(6)：1380-1387,8.DOI:10.3969/j.issn.1003-3106.2024.06.006
基于深度强化学习的电力物联网动态切片策略研究
Dynamic Slicing Strategy for Power Internet of Things Based on Deep Reinforcement Learning
摘要
Abstract
Software-defined power internet of things supports the construction of Network Slice(NS)that can carry different business requirements.By deploying NS,end-to-end services can be provided to internet of things devices with specific business demands.The deployment of business NS involves two interrelated issues,namely the deployment of Virtual Network Function(VNF)and the determination of business transmission routings.In dynamic network scenario with massive business requirements,NS deployment solutions need to achieve intelligent and dynamically flexible deployment based on the network status.To address the aforementioned problems,the slicing strategy in the dynamic network scenario is explored and the complex joint optimization problem of VNF deployment and business transmission routing determination is solved based on deep reinforcement learning algorithm.The experimental findings demonstrate that the proposed strategy effectively adjusts the deployment plan based on the current state,controls the average energy loss,average reliability,and average remaining bandwidth occupation of the service path,and improves the overall transmission performance of the network.关键词
软件定义电力物联网/切片/虚拟网络功能/路由/深度强化学习Key words
software-defined power internet of things/slice/VNF/routing/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
辛锐,吴军英,薛冰,张鹏飞,李艳军,柴守亮,王佳楠..基于深度强化学习的电力物联网动态切片策略研究[J].无线电工程,2024,54(6):1380-1387,8.基金项目
河北省省级科技计划资助(22310302D)S&T Program of Hebei Province(22310302D) （22310302D）
