Mobile network traffic prediction based on machine learning

DOI

10.2991/978-94-6463-098-5_191How to use a DOI?

Keywords

base station traffic prediction; Time series prediction; ARIMA model; LSTM model

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.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Huike Shi
PY  - 2022
DA  - 2022/12/27
TI  - Mobile network traffic prediction based on machine learning
BT  - Proceedings of the 2022 4th International Conference on Economic Management and Cultural Industry (ICEMCI 2022)
PB  - Atlantis Press
SP  - 1691
EP  - 1698
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-098-5_191
DO  - 10.2991/978-94-6463-098-5_191
ID  - Shi2022
ER  -