Mobile Network Traffic Forecasting Using Artificial Neural Networks

Abstract

Mobile communication systems need to adapt to temporally and spatially changing mobile network traffic, due to dynamic characteristics of mobile users, in order to provide high quality of service. Since these changes are not purely random, one can extract the deterministic portion and patterns from the observed network traffic to predict the future network traffic status. Such prediction can be utilized for a series of proactive network management procedures including coordinated beam management, beam activation/deactivation and load balancing. To this end, in this paper, an intelligent predictor using artificial neural networks is proposed and compared with a baseline scheme that uses linear prediction. It is shown that the neural network scheme outperforms the baseline scheme for relatively balanced data traffic between highly random and deterministic mobility patterns. For highly random or deterministic mobility patterns, the performance of the two considered schemes is similar to each other.