基于上下文学习的电力物联网接入控制方法

周振宇; 贾泽晗; 廖海君; 赵雄文; 张磊

doi:10.11959/j.issn.1000-436x.2021062

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基于上下文学习的电力物联网接入控制方法

学术论文 | 更新时间：2024-06-05

- 基于上下文学习的电力物联网接入控制方法
- Context-aware learning-based access control method for power IoT
- 通信学报 2021年42卷第3期页码：150-159
- 作者机构：
  
  1. 华北电力大学新能源电力系统国家重点实验室，北京 102206
  2. 东南大学移动通信国家重点实验室，江苏南京 210096
  3. 国网山东省电力公司电力科学研究院，山东济南 250003
- 作者简介：
  
  [ "周振宇（1983- ），男，河北张家口人，博士，华北电力大学教授、博士生导师，主要研究方向为无线通信网络与新技术、物联网与现代传感技术、能源互联网信息通信技术等。" ]
  [ "贾泽晗（1998- ），男，河北保定人，华北电力大学硕士生，主要研究方向为电力物联网、智能电网中无线通信技术等。" ]
  [ "廖海君（1997- ），女，广西贵港人，华北电力大学博士生，主要研究方向为电力物联网、智能电网中无线通信技术等。" ]
  [ "赵雄文（1964- ），男，陕西延安人，博士，华北电力大学教授、博士生导师，主要研究方向为5G和后5G无线通信、电力系统通信等。" ]
  [ "张磊（1989- ），男，河南新乡人，国网山东省电力公司高级工程师，主要研究方向为电力系统运行与控制等。" ]
- 基金信息：
  
  国家电网有限公司科技基金资助项目(SGSDDK00KJJS1900405);东南大学移动通信国家重点实验室开放研究基金资助项目(2021D12)
- DOI：10.11959/j.issn.1000-436x.2021062
  中图分类号： TN915
- 网络出版日期：2021-03，
  
  纸质出版日期：2021-03-25
- 稿件说明：
移动端阅览
周振宇, 贾泽晗, 廖海君, 等. 基于上下文学习的电力物联网接入控制方法[J]. 通信学报, 2021,42(3):150-159.

Zhenyu ZHOU, Zehan JIA, Haijun LIAO, et al. Context-aware learning-based access control method for power IoT[J]. Journal on communications, 2021, 42(3): 150-159.
周振宇, 贾泽晗, 廖海君, 等. 基于上下文学习的电力物联网接入控制方法[J]. 通信学报, 2021,42(3):150-159. DOI： 10.11959/j.issn.1000-436x.2021062.

Zhenyu ZHOU, Zehan JIA, Haijun LIAO, et al. Context-aware learning-based access control method for power IoT[J]. Journal on communications, 2021, 42(3): 150-159. DOI： 10.11959/j.issn.1000-436x.2021062.

摘要

针对6G时代电力物联网海量终端接入冲突严重、队列积压大、能量效率低等问题，提出了一种基于上下文学习的接入控制算法。所提算法基于强化学习和快速上行链路授权技术，考虑终端活跃与休眠2种状态，优化目标为在终端接入服务质量需求的长期约束下最大化网络总能量效率。利用李雅普诺夫优化对长期优化目标与约束进行解耦，将长期优化问题转化为单时隙独立的确定性子问题，并利用基于终端状态感知的上置信界算法进行求解。仿真结果表明，所提算法能够在满足终端接入服务质量需求的同时，有效提高网络总能量效率。相较于传统快速上行链路授权算法，所提算法可提高平均能量效率 48.11%，提高满足接入服务质量需求的终端比例54.95%，降低平均队列积压83.83%。

Abstract

In view of the problems of severe access conflicts

high queue backlog

and low energy efficiency in the massive terminal access scenario of the power Internet of things (power IoT) in 6G era

a context-aware learning-based access control (CLAC) algorithm was proposed.The proposed algorithm was based on reinforcement learning and fast uplink grant technology

considering active state and dormant state of terminals

and the optimization objective was to maximize the total network energy efficiency under the long-term constraint of terminal access service quality requirements.Lyapunov optimization was used to decouple the long-term optimization objective and constraint

and the long-term optimization problem was transformed into a series of single time-slot independent deterministic sub-problems

which could be solved by the terminal state-aware upper confidence bound algorithm.The simulation results show that CLAC can improve the network energy efficiency while meeting the terminal access service quality requirements.Compared with the traditional fast uplink grant

CLAC can improve the average energy efficiency by 48.11%

increase the proportion of terminals meeting access service quality requirements by 54.95%

and reduce the average queue backlog by 83.83%.

关键词

Keywords

references

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