摘要
面向智慧园区内大量电力信息采集设备带来的区域高密度数据传输要求,引入超密集组网技术实现网络覆盖能力提升,将其中的接入决策优化问题建模为长期吞吐量优化问题;进一步利用Lyapunov优化将长期约束与长期优化问题转化为一系列短期优化问题;基于复杂多变的网络环境及多设备接入决策之间的耦合关系,利用引入竞争系数的强化学习算法实现吞吐量与队列漂移加权效用值的最大化。仿真结果表明,该算法考虑设备侧数据队列与能量赤字队列的稳定性,可在满足长期队列稳定性约束的情况下尽可能提升网络吞吐量,且收敛性更优。
Face to the demand for regional high-density data transmission brought by massive power information collection device in smart park,ultra-dense networking technology was introduced to improve network coverage.The access decision optimization problem was modeled as a long-term throughput optimization problem.The long-term constraint and long-term optimization problem were transformed into a series of short-term problems by Lyapunov optimization.Based on the complex and changeable network environment and the coupling between multi-device access decisions,this paper used the reinforcement learning algorithm with competition coefficient to maximize the weighted utility of throughput and queue drift.Simulation results show that the proposed algorithm can take the stability of data queue and energy deficit queue into account,and much improve throughput with better convergence when meeting the long-term queue stability constraints.
作者
熊洪樟
程杰
张崇超
王燕晋
戚成飞
李亚红
Xiong Hongzhang;Cheng Jie;Zhang Chongchao;Wang Yanjin;Qi Chengfei;Li Yahong(Metering Center of State Grid Jibei Electric Power Co.,Ltd.,Beijing 100054,China;Beijing State Grid Siji Network Security Technology Limited Company,Beijing 102200,China)
出处
《计算机应用与软件》
北大核心
2025年第1期41-48,共8页
Computer Applications and Software
基金
国家电网科技项目(SGTYHT/19-JS-215)。
关键词
智慧园区
电力信息采集
接入决策优化
Lyapunov优化
强化学习
Smart park
Power information collection
Access decision optimization
Lyapunov optimization
Reinforcement learning
