Forecasting the WIMAX Traffic via modified Artificial Neural Network Models

Abstract

This paper attempts to present a new approach of forecasting the WiMAX traffic by exploiting Artificial Neural Networks (ANN). To develop the model, actual data is gathered from the LibyaMax network that spans the duration of 180 days in total. Traffic data is separated into three cases based on the base stations involved (A, B and AB). The model implements traffic prediction by emphasizing on the maximum and minimum number of online user whereby two different learning algorithms