Abstract
The optimization of mobile network capacity usage is an essential operation to promote positive returns on network investments, prevent capacity bottlenecks, and deliver good end user experience. This study examines the performance of several statistical models to predict voice and data traffic in a mobile network. While no method dominates the others across all time series and prediction horizons, exponential smoothing and ARIMA models are good alternatives to forecast both voice and data traffic. This analysis shows that network managers have at their disposal a set of statistical tools to plan future capacity upgrades with the most effective solution, while optimizing their investment and maintaining good network quality.
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