基于长短时预测的基站节能策略

doi:10.11959/j.issn.1000-0801.2022043

Abstract

Abstract:

With the development of the mobile communication technology and the acceleration of 5G commercial network deployment, energy consumption of 5G, which will continue to raise the operating expense significantly.How to maximize the energy efficiency while ensuring service experience and equipment safety has always been one of the research focus in the industry.With the challenges including complexity of network architecture and variety of base station types, an AI-based energy-saving technology including policy generation and closed-loop security assurance of “perception, prediction, analysis, and decision” was introduced.After calibration and validation based on the offline dataset, the false-switch-off rate is less than 2%, and the recall rate is not fewer than 84%.Further study shows that the technology has greater potential on energy-saving.

Key words: base station energy-saving, traffic prediction, intelligent

CLC Number:

TN929

Miaomiao ZHANG, Hao ZHAO, Yan ZHOU, Yang ZHANG, Li YU, Yanping LIANG, Chunjie FENG. A long-time ＆ short-time prediction based 5G base station energy-saving policy[J]. Telecommunications Science, 2022, 38(11): 153-162.

Figures/Tables 11

References 26

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	最大值统计	Prophet	联合预测方案
RMSE　　用户数/个	2.3	1.9	1.5
上行PRB利用率	7.6%	3.7%	3.6%
下行PRB利用率	13.1%	5.2%	6.7%
上行业务量/MB	66	29	27
下行业务量/MB	469	245	229
基站关断召回率	39.6%	86.7%	84.3%
基站误关率	1.24%	5.24%	<1.9%
推理时间（单指标单基站）	秒级	5 min	秒级

节能方式	4G/kW·h	5G/kW·h
通道关断	0.02	0.12
浅度休眠/去激活	0.08	0.16
深度休眠	—	0.35

通道关断小区数/个	通道关断平均时长/h	通道关断总时长/h	载波关断小区数/个	载波关断平均时长/h	载波关断总时长/h	深度休眠小区数/个	深度休眠平均时长/h	深度休眠总时长/h
22 673	6.05	137 171.65	8 391	4.71	39 521.61	1 622	5.13	8 320.86

A long-time ＆ short-time prediction based 5G base station energy-saving policy

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