工业工程 ›› 2022, Vol. 25 ›› Issue (1): 129-135.doi: 10.3969/j.issn.1007-7375.2022.01.016

• 实践与应用 • 上一篇    下一篇

面向电力物联网URLLC业务的智能网络切片管理方法

叶万余   

  1. 广东电网有限责任公司 清远供电局,广东 清远 511510
  • 收稿日期:2021-09-07 发布日期:2022-03-02
  • 作者简介:叶万余 (1974—),男,广东省人,高级工程师,硕士,主要研究方向为电力物联网
  • 基金资助:
    南方电网公司科技项目资助(GDKJXM20209204(031800KK52190127))

Intelligent Network Slicing Management for URLLC Services in Power Internet of Things

YE Wanyu   

  1. Qingyuan Power Supply Bureau, Guangdong Power Grid Co. Ltd., Qingyuan 511510, China
  • Received:2021-09-07 Published:2022-03-02

摘要: 基于5G通信技术的电力物联网正在如火如荼地建设,随之产生的是用电信息采集、输变电状态监测以及精准负荷控制等新型电力物联网业务。为了满足这些业务对5G网络的超低时延和超高可靠性的需求,提出一种面向电力物联网URLLC (ultra reliable low latency communication)业务的智能网络切片管理方法。该方法综合运用5G切片和移动边缘计算 (mobile edge computing, MEC)技术,建立电力业务传输和计算的时延、能耗以及可靠性模型,并通过DQN (deep Q network)算法对切片资源进行优化。仿真实验表明,所提出的智能网络切片管理方法的可靠性高于98%,且优于经典的基于坐标块下降方法和资源平均分配方法。

关键词: 电力物联网, 5G网络, 移动边缘计算, 深度强化学习, 超高可靠与超低时延

Abstract: With the development of 5G-based power internet of things, various new services are emerging, such as electricity consumption information collection, power transmission and transformation status monitoring, and precise load control. An intelligent network slicing management method is proposed for the URLLC (ultra reliable low latency communication) scenario of the power internet of things. This method integrates 5G slicing and MEC (mobile edge computing) technologies to establish the delay, energy consumption and reliability models of power service, and the slicing resources are optimized through the DQN (deep Q network) algorithm. Simulation experiments show that the reliability of the intelligent network slicing management method proposed in the study is higher than 98%, and it is better than the classic coordinate block descent method and the average resource allocation method.

Key words: power internet of things, 5G network, mobile edge computing, deep reinforcement learning, ultra-high reliability and ultra-low latency

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