Prediction Wireless Network Traffic Evaluation Potential Based on Ensemble Algorithms

This paper proposes intelligence methods for the prediction of wireless network traffic and the evaluation of its development. This solution analyses the changing patterns of actual business traffic data in cellular networks in the time dimension and uses ensemble learning (EL) to predict the tendency of traffic, providing guidance for operators' network optimization deployment from a short-term perspective. The paper depends on a dataset taken from the Eurecom ElasticMon 5G monitoring platform. The approach applied a multi-dimensional mapping relationship between other operational data and traffic in the network using four ensembles of learning: Extreme Gradient Boosting (XGBoost), ADA boosting, gradient boosting algorithm, and a voting regression algorithm. They applied to find the upper limit of traffic that the cellular cell can carry, optimizing the deployment of the network from a long-term perspective. The results showed that XGBoost outperforms other methods, whose best performance ensures the privacy of traffic data.