Open AccessTraffic data prediction of mobile communication base station based on wavelet neural network
Author(s) -
Ming Lei,
Rui Qin,
Wentao Mao,
Hongxia Lu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1883/1/012065
Subject(s) - base station , computer science , python (programming language) , real time computing , mobile telephony , artificial neural network , mobile station , data mining , wavelet , base (topology) , mean squared error , computer network , artificial intelligence , mobile radio , statistics , mathematics , mathematical analysis , operating system
With the wide application of new media, users require more and more mobile communication. In order to satisfy users’ high-quality experience and save resources, it is necessary to predict the traffic data of mobile communication base station, so that mobile communication base station can adjust the frequency load quantity according to the traffic fluctuation. From March 1 to April 9, 2018, this paper collects traffic data, selects 40, 000 sets of data, uses python to mine data, and predicts the traffic data of mobile communication base station by establishing wavelet neural network short-time traffic prediction model. The results show that the average accuracy of the short-term prediction model is 43. 15 and the root mean square error is 0. 0076.