Abstract: Next-generation cellular networks play an important role in network management and service provision,so that predictive analysis of mobile network traffic is becoming more and more important to our daily life.To predict the urban cellular traffic,this study designs a traffic prediction model based on multi-channel sparse LSTM.Compared with multilayer perceptron networks or other neural network structures,LSTM is very suitable for processing time series data.Therefore,the designed multi-channel method can effectively capture multi-source network traffic information,and its sparse method can adaptively assign different weights to different traffic time nodes,so that the ability of the deep neural network model to capture important features is improved.This study evaluates the performance of the proposed method with respect to the single-step and multi-step prediction using the cellular traffic data set in Milan,Italy.The experiment results show that the proposed method is more accurate than the benchmark methods.In addition,this study reports the impact of different sampling settings of cellular traffic on the storable length and accuracy of the LSTM network model.

Key words: Cellular traffic, LSTM, Neural networks, Time series