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Mobile network traffic prediction based on machine learning

In: Proceedings of the 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022)

Author

Listed:
  • Huike Shi

    (Beijing University of Posts and Telecommunications)

Abstract

In order to better cope with the overall network efficiency and energy consumption caused by the tide phenomenon of the network traffic of the base station and the physical capacity expansion caused by the increasing network traffic demand, we need to predict the network traffic of the base station in real time, so as to guide the design of the time-sharing switching program of the base station and provide suggestions for future planning and construction. In this paper, ARIMA model and LSTM model are used to predict the base station traffic respectively, and RMSE is used as the model evaluation index. The experimental results show that the deviation RMSE predicted by ARIMA model is 1.904, and the deviation RMSE predicted by LSTM model is 1.993. Therefore, ARIMA model predicts more accurately, that is, it performs better in predicting the base station traffic data of ARIMA model.

Suggested Citation

  • Huike Shi, 2023. "Mobile network traffic prediction based on machine learning," Advances in Economics, Business and Management Research, in: Hrushikesh Mallick & Gaikar Vilas B. & Ong Tze San (ed.), Proceedings of the 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022), pages 1691-1698, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-098-5_191
    DOI: 10.2991/978-94-6463-098-5_191
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