Research on 5G base station energy saving system based on DCNN-LSTM load prediction algorithm
With the rapid construction of the 5G wireless communication network, the energy consumption pressure of operators, and even the overall communication industry, is simultaneously highlighted.Achieving sustainable development of the industry through energy conservation and consumption reduction has b...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
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Beijing Xintong Media Co., Ltd
2023-04-01
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| Series: | Dianxin kexue |
| Subjects: | |
| Online Access: | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023101/ |
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| _version_ | 1841530851065593856 |
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| author | Jianbin WANG Shuchun WANG Shangjin LIAO Shuyuan SHI |
| author_facet | Jianbin WANG Shuchun WANG Shangjin LIAO Shuyuan SHI |
| author_sort | Jianbin WANG |
| collection | DOAJ |
| description | With the rapid construction of the 5G wireless communication network, the energy consumption pressure of operators, and even the overall communication industry, is simultaneously highlighted.Achieving sustainable development of the industry through energy conservation and consumption reduction has become a new research direction for the current 5G network development.Taking the PRB rate as the load evaluation index, LSTM model was improved by using DCNN to extract the depth feature of the cell’s indicators.A set of DCNN-LSTM deep learning model that could predict the future value of PRB rate was proposed.On the basis of the improved algorithm, the network topology of the current 5G access network was optimized.An additional network element and its working system were designed.An intelligent energy-saving system, which ensured the network experience, of 5G base stations was realized. |
| format | Article |
| id | doaj-art-aa56c6e6961d4851adf864da42b336d7 |
| institution | Kabale University |
| issn | 1000-0801 |
| language | zho |
| publishDate | 2023-04-01 |
| publisher | Beijing Xintong Media Co., Ltd |
| record_format | Article |
| series | Dianxin kexue |
| spelling | doaj-art-aa56c6e6961d4851adf864da42b336d72025-01-15T02:58:51ZzhoBeijing Xintong Media Co., LtdDianxin kexue1000-08012023-04-013913314159569081Research on 5G base station energy saving system based on DCNN-LSTM load prediction algorithmJianbin WANGShuchun WANGShangjin LIAOShuyuan SHIWith the rapid construction of the 5G wireless communication network, the energy consumption pressure of operators, and even the overall communication industry, is simultaneously highlighted.Achieving sustainable development of the industry through energy conservation and consumption reduction has become a new research direction for the current 5G network development.Taking the PRB rate as the load evaluation index, LSTM model was improved by using DCNN to extract the depth feature of the cell’s indicators.A set of DCNN-LSTM deep learning model that could predict the future value of PRB rate was proposed.On the basis of the improved algorithm, the network topology of the current 5G access network was optimized.An additional network element and its working system were designed.An intelligent energy-saving system, which ensured the network experience, of 5G base stations was realized.http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023101/5G base station energy savingimproved LSTM algorithm5G system design |
| spellingShingle | Jianbin WANG Shuchun WANG Shangjin LIAO Shuyuan SHI Research on 5G base station energy saving system based on DCNN-LSTM load prediction algorithm Dianxin kexue 5G base station energy saving improved LSTM algorithm 5G system design |
| title | Research on 5G base station energy saving system based on DCNN-LSTM load prediction algorithm |
| title_full | Research on 5G base station energy saving system based on DCNN-LSTM load prediction algorithm |
| title_fullStr | Research on 5G base station energy saving system based on DCNN-LSTM load prediction algorithm |
| title_full_unstemmed | Research on 5G base station energy saving system based on DCNN-LSTM load prediction algorithm |
| title_short | Research on 5G base station energy saving system based on DCNN-LSTM load prediction algorithm |
| title_sort | research on 5g base station energy saving system based on dcnn lstm load prediction algorithm |
| topic | 5G base station energy saving improved LSTM algorithm 5G system design |
| url | http://www.telecomsci.com/zh/article/doi/10.11959/j.issn.1000-0801.2023101/ |
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