Academic Journal of Computing & Information Science, 2022, 5(4); doi: 10.25236/AJCIS.2022.050404.
Mengjia Jiang1, Jiayi Wang2
1School of Maths and Physics, Chengdu University of Technology, Chengdu, 610059, China
2School of Labor Economics, Capital University of Economics and Business, Beijing, 100070, China
The mobile base station is an important communication hub, which plays a very important role in the whole Internet. On the one hand, during peak traffic periods, a large number of base stations present problems of load exceeding capacity, making network speeds very slow even when signal conditions are good. On the other hand, the tidal phenomenon of base stations makes it possible for the number of users to drop significantly during certain hours, thus making the traffic load problem of base stations increasingly important. To address this problem, this paper proposes a GRU recurrent neural network based mobile communication base station traffic prediction model and improvements. Using the open-source sample dataset provided by the organising committee of the China Universities Big Data Challenge 2021, the study was carried out by conducting a smoothness test on the time series data, extracting three key indicators, namely the average number of subscribers in a cell, the PDCP traffic in a cell and the average number of activated subscribers, cutting the dataset, with 80% as the training set and 20% as the test set, and importing the GRU neural network prediction model implemented in Python, the results were unsatisfactory. Therefore, the model was improved to build a Bi-GRU-based base station traffic prediction model, and the prediction data was tested, and the error MAPE was less than 0.1, which was a good prediction. The improved base station traffic prediction solution is of great significance to the application of strategies related to the dormant energy saving of base stations based on traffic prediction.
GRU recurrent neural network; Base station traffic prediction; Smoothness test; Bi-GRU; MAPE error test
Mengjia Jiang, Jiayi Wang. Mobile Base Station Traffic Prediction Based on Traffic Data Analysis. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 4: 22-28. https://doi.org/10.25236/AJCIS.2022.050404.
[1] He Y, Li YT. Mobile communication base station traffic prediction based on vector autoregressive model [J]. Industrial Engineering and Management, 2017, 22(04): 79-84.
[2] Hu JW. LSTM-based traffic prediction for mobile communication base stations [J]. Information Technology and Informatization, 2021(05): 84-86.
[3] Zhang Yang, Zhang leucistic. A mobile communication base station traffic prediction method based on LSTM and ARIMA models [J]. Information and Computer (Theoretical Edition), 2021, 33(07): 100-102.
[4] Zhang J, Bai GW, Sha XL, Zhao WT, Shen H. A mobile network traffic prediction model based on spatio-temporal characteristics [J]. Computer Science, 2019, 46(12): 108-113.
[5] Fugang Liu, Ziwei Zhang, Ruolin Zhou. Automatic Modulation Recognition Based on CNN and GRU [J]. Tsinghua Science and Technology, 2022, 27(02): 422- 431.
[6] Zhang Jie, Zhang Zipeng. Research on Fermented Grains Steam Running Condition Prediction Model of Make Chinese Liquor Robot Based on GRU [C]// Proceedings of 2021 3rd International Conference on Advanced Information Science and System (AISS 2021)., 2021: 250-253.
[7] Luo Junxia, Ding Bangxu. Research on the analysis and forecasting method of CPI economic indicators based on ARMA and LSTM [J]. Journal of Huangshan College, 2021, 23(05): 14-18.
[8] JIN Yu, ZHAO Bingwen, ZHENG Hanyu, LI Wan. Research on heating load prediction based on GRU neural network [J]. Science and Technology Bulletin, 2022, 38(01): 68-72.
[9] Zhang Lei, Tian Guo. Strategic analysis of mobile communication base station construction [J]. Electronic Technology, 2021, 50(11): 40-41.