Traffic Prediction with Reservoir Computing for Mobile Networks

Abstract

The accurate traffic model and prediction of mobile network plays an important role in network planning. It is particularly important for the performance analysis of mobile networks. The study in this paper concerns predicting the traffic of mobile network, which is essentially nonlinear, dynamic and affected by immeasurable parameters and variables. The accurate analytical model of the traffic of the mobile network can be hardly obtained. Therefore a predicting method based on history input-output using correlation analysis ideas and Reservoir Computing (RC) is proposed. Correlation analysis is used to select proper input variables of the model. Reservoir Computing is a recent research area, in which a random recurrent topology is constructed, and only the weights of connections in a linear output layer is trained. This make it possible to solve complex tasks using just linear post-processing techniques. The proposed model has been verified on the data from network monitoring system in China Mobile Heilongjiang Co. Ltd.