Browsing by Author "Fetihya Mohammed (PhD)"

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    LTE PRB Utilization Prediction for Load Balancing Between Frequency Layers
    (Addis Ababa University, 2024-05) Fetihya Mohammed; Fetihya Mohammed (PhD)
    The ever-increasing number of smart devices and services strains Long-Term Evolution( LTE) network capacity, impacting Key Performance Indicators(KPIs) like user experience. Accurate prediction of LTE resource utilization is crucial for network optimization and improving user experience. Physical Resource Block (PRB) utilization prediction plays a vital role in analyzing resource allocation within the network. This thesis investigates the application of machine learning models for predicting LTE PRB utilization to facilitate load balancing between frequency layers. Three prominent models – Prophet, long short term memory (LSTM), and eXtreme Gradient Boosting(XGBoost) were evaluated and compared. The results demonstrate that Prophet significantly outperforms LSTM and XGBoost in terms of prediction accuracy. Prophet achieved an R-squared value of 0.95 and a Mean Absolute Error(MAE) of 4.98, indicating a highly accurate fit. Conversely, LSTM and XGBoost obtained R-squared values of approximately 0.63 with respective MAE values of around 17. These findings suggest Prophet’s superior accuracy makes it a promising choice for predicting PRB utilization and enabling effective load balancing in LTE networks. This thesis contributes to the field of LTE network optimization by demonstrating the effectiveness of machine learning, particularly Prophet, for PRB utilization prediction. This capability can be leveraged to develop efficient load balancing algorithms that improve network performance and user experience.