Telecommunications Science ›› 2023, Vol. 39 ›› Issue (4): 133-141.doi: 10.11959/j.issn.1000-0801.2023101

• Research and Development • Previous Articles     Next Articles

Research on 5G base station energy saving system based on DCNN-LSTM load prediction algorithm

Jianbin WANG1,2, Shuchun WANG2, Shangjin LIAO3, Shuyuan SHI2   

  1. 1 Zhejiang University, Hangzhou 310027, China
    2 China Telecom Co., Ltd., Hangzhou 310014, China
    3 Huaxin Consulting Co., Ltd., Hangzhou 310052, China
  • Revised:2023-04-16 Online:2023-04-20 Published:2023-04-01
  • Supported by:
    The National Natural Science Foundation of China(U1809211)


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.

Key words: 5G base station energy saving, improved LSTM algorithm, 5G system design

CLC Number: 

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