The exponential growth in mobile network traffic, driven by the rapid deployment of 5G technologies and the proliferation of new services, presents significant challenges for telecommunication operators. This thesis addresses these challenges by developing a predictive capacity management solution for 4G and 5G cellular networks. The primary objective is to forecast network traffic and identify potential congestion points up to one year in advance, enabling proactive network management and optimizing resource allocation, particularly through the use of spectral efficiency as a key predictive measure.

This study utilizes data from KPN’s Operations Support System (OSS), comprising 67 days of hourly data across the entire network, with a focus on predicting future traffic and network performance up to one year ahead. The methodology integrates historical data analysis, time series forecasting, and machine learning techniques. The approach combines Cumulative Distribution Function (CDF) modeling for traffic volume prediction with supervised machine learning algorithms, including Linear Regression, Lasso Regression, Random Forest, and CatBoost, to forecast Physical Resource Block (PRB) utilization and spectral efficiency at the sector level.

The detailed analysis identifies Lasso Regression as the most effective model for predicting spectral efficiency, with the lowest Mean Absolute Percentage Error (MAPE). Lasso’s ability to handle extrapolation beyond observed data ranges makes it particularly well-suited for long-term capacity management when combined with CDF-based traffic prediction. The findings demonstrate significant improvements in the accuracy of congestion predictions and the efficiency of resource utilization.

The study also revealed that, without additional resources, the number of congested sectors is expected to increase as traffic demand continues to grow. This highlights the critical need for new spectrum allocation to maintain service quality. Additionally, the research evaluated the impact of deploying new spectrum resources, such as the 3.5 GHz band, in specific sectors. The results showed that the deployment of the 3.5 GHz band significantly reduced congestion and improved network performance and user experience during the forecast period.