Boosting Cell Site Stability: LTE Congestion Prediction with Machine Learning and Deep Learning

Abstract

This paper presents a novel approach to predicting LTE congestion and improving network stability by comparing the performance of machine learning (Random Forest, XGBoost) and deep learning (LSTM, GRU) models. Using a real-world dataset from Orange Egypt, the study focuses on predicting Physical Resource Block (PRB) utilization, a key indicator of congestion and stability. Random Forest, using other KPIs to predict PRB utilization, achieved the highest accuracy (RMSE<4%), while GRU effectively forecasted congestion one week in advance (RMSE ~7%). The comparative evaluation of these models offers unique insights into their strengths, providing practical applications in real-time LTE network management, enabling proactive congestion control and enhanced network stability.