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...

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Bibliographic Details
Main Author: Daw Abdulsalam Ali Daw
Format: Thesis
Language:English
Subjects:
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Summary: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.