Prediction Modelling for Forecast of Standalone Dedicated Control Channel Congestion using Machine Learning

Standalone Dedicated Control Channel (SDCCH) congestion remains a persistent challenge in mobile networks, often resulting in failed call setups, delayed SMS delivery, and degraded user experience. To address this, the present study applied machine learning techniques to forecast SDCCH congestion in four major Nigerian mobile networks (MTN, Airtel, Globacom, and 9mobile} using monthly data obtained from the Network Operations Centres (NOCs) of the Mobile Network Operators (MNOs), spanning January 2015 to December 2024. Two regression algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were employed within a supervised learning framework that incorporated lag features and seasonal dummy variables. Forecasts for January to December 2024 were generated through recursive multi-step prediction, and model performance was evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results showed that both models were able to capture short-term variations but struggled with large fluctuations and nonlinear spikes in actual congestion values. RF slightly outperformed XGBoost for Airtel, while XGBoost yielded better results for MTN, Glo, and 9mobile. However, both models exhibited relatively high percentage errors and a tendency to either flatten variability or underestimate peak congestion levels, limiting their suitability for direct operational deployment. The study concludes that although machine learning holds promise for forecasting SDCCH congestion and supporting proactive capacity planning, further refinement is required. Incorporating larger datasets, additional lag features, and exogenous variables such as traffic load and time-of-day effects could improve predictive accuracy. Overall, XGBoost demonstrated marginally greater reliability than RF, but both models need optimisation before being used for real-time network decision-making.