A long-time & short-time prediction based 5G base station energy-saving policy
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 safet...
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Format: | Article |
Language: | zho |
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Beijing Xintong Media Co., Ltd
2022-11-01
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Series: | Dianxin kexue |
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Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022043/ |
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author | Miaomiao ZHANG Hao ZHAO Yan ZHOU Yang ZHANG Li YU Yanping LIANG Chunjie FENG |
author_facet | Miaomiao ZHANG Hao ZHAO Yan ZHOU Yang ZHANG Li YU Yanping LIANG Chunjie FENG |
author_sort | Miaomiao ZHANG |
collection | DOAJ |
description | 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. |
format | Article |
id | doaj-art-988bb6f795614f87a23ce436c18538b1 |
institution | Kabale University |
issn | 1000-0801 |
language | zho |
publishDate | 2022-11-01 |
publisher | Beijing Xintong Media Co., Ltd |
record_format | Article |
series | Dianxin kexue |
spelling | doaj-art-988bb6f795614f87a23ce436c18538b12025-01-15T02:59:56ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012022-11-013815316259575667A long-time & short-time prediction based 5G base station energy-saving policyMiaomiao ZHANGHao ZHAOYan ZHOUYang ZHANGLi YUYanping LIANGChunjie FENGWith 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.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022043/base station energy-savingtraffic predictionintelligent |
spellingShingle | 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 Dianxin kexue base station energy-saving traffic prediction intelligent |
title | A long-time & short-time prediction based 5G base station energy-saving policy |
title_full | A long-time & short-time prediction based 5G base station energy-saving policy |
title_fullStr | A long-time & short-time prediction based 5G base station energy-saving policy |
title_full_unstemmed | A long-time & short-time prediction based 5G base station energy-saving policy |
title_short | A long-time & short-time prediction based 5G base station energy-saving policy |
title_sort | long time short time prediction based 5g base station energy saving policy |
topic | base station energy-saving traffic prediction intelligent |
url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2022043/ |
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