WiMAX Traffic Forecasting Based On Artificial Intelligence Techniques

The ability to predict the traffic of a particular WiMAX network is crucial in analyzing its
performance. It bears various applications in reality, such as enabling better network
management and admission. Furthermore, traffic forecasting plays a vital role in ensuring that
the quality of service is maintained at the necessary level. Therefore, in this research, a new
model for WiMAX traffic forecasting system for predicting traffic time series based on the
traffic data recorded (TRD) using Artificial Neural Network (ANN), K-Nearest Neighbor
(KNN) and Fuzzy Time Series (FTS) was proposed. The data used in this work are available
from LibyaMax network (WiMAX technology) automated by Libya Telecom and Technology
(LTT) over a period of 180 days which consist of maximum online user, minimum online
user, traffic of MIMO-A and traffic of MIMO-B. The quality of forecasting WiMAX traffic
was obtained by focusing on the Artificial Intelligence (AI) design through comparison of
different configurations and models that consist of different topologies and learning
algorithms. The decision of changing the Artificial Intelligence (Al) architecture is essentially
based on the objective to obtain the best Al model for a flow traffic prediction model.
Different configurations were tested using real traffic data recorded at base stations (A, B and
AB) that belong to a Libyan WiMAX network. Statistical measurement was used to evaluate
different AI configurations to select the best model based on higher performance result. The
outcome of the study indicates that KNN model using maximum and minimum online user as
inputs give good and accurate mean square error results (MSE) in predicting traffic as a
whole.