Comparative analysis of statistical and deep learning models for short-term LTE cell traffic prediction
TL;DR
A comparative analysis of the statistical and deep learning models for real-time, cell-level LTE eNodeB traffic forecasting across diverse geospatial environments using real-world data, with practical validation through LSTM- based traffic prediction for network optimization.
Abstract
Accurate short-term traffic prediction in Long-Term Evolution (LTE) networks is essential for proactive resource management and maintaining quality of service. With increasing data demand and dynamic traffic patterns, reliable forecasting at the cell level has become critical for efficient network operations. This analysis compared ARIMA, SARIMA, LSTM and GRU models with real hourly traffic data of urban and rural LTE eNodeBs in Nepal. The dataset was split chronologically into 80% training and 20% testing before preprocessing to prevent data leakage. The LSTM model performed best, achieving MSE of 1.4804, MAE of 0.8770, RMSE of 1.2167, and R-squared of 0.9563. Deep learning, particularly LSTM, was able to learn the non-linear and complex traffic patterns better than other models. This finding highlights the practical applicability of deep learning models in real world telecom network operations. The research provides a comparative analysis of the statistical and deep learning models for real-time, cell-level LTE eNodeB traffic forecasting across diverse geospatial environments using real-world data, with practical validation through LSTM- based traffic prediction for network optimization.