摘要
Abstract
To address the energy efficiency bottleneck problem in 5G base stations under low-load scenarios where hardware resource utilization is less than 30%,this study aims to reduce static power consumption and improve green energy-saving performance through dynamic regulation.An optimization scheme based on Dynamic Voltage and Frequency Scaling(DVFS)is proposed,and a three-tier"Perception-Decision-Execution"architecture is built.The perception layer employs a multi-dimensional hardware monitoring system with the 10 kHz sampling rate,the decision layer integrates Deep Reinforcement Learning(DRL)and the LSTM-based load prediction algorithm for real-time Vdd-f configuration optimization.The execution layer incorporates a programmable hybrid Power Management Unit(PMU).Experimental validation on a semi-physical simulation platform shows power reductions of 25.8%,27.4%,and 31.4%under idle,low-load,and tidal-load scenarios,respectively,with a 59.7%improvement in overall energy efficiency and QoS compliance maintained between 97.8%and 100%.This research provides a scalable and efficient technical framework for dynamic energy efficiency management in 5G base stations.关键词
动态电压频率调整(DVFS)/5G基站/低负载功耗/能效优化Key words
Dynamic Voltage Frequency Adjustment(DVFS)/5G base station/low load power consumption/energy efficiency optimization分类
信息技术与安全科学
