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Forecasting the capacity of mobile networks

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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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Notes

  1. For instance, a simple neural network, such as a multilayer perceptron with a single hidden layer, 7 inputs (one week of data), 7 hidden neurons and 1 output neuron will have \((7+1) \times 7 + (7+1) = 64\) parameters. Since the data have about 300 observations, only about 5 observations will be available to estimate each parameter.

  2. This procedure is called “handover”.

  3. Typically, mobile network operators do not store daily aggregated data for much longer periods due to storage limitations.

  4. This is probably related to the lower levels of activity at work places during Friday afternoons.

  5. There is some empirical evidence that a simple average of individual forecasts performs better than more sophisticated combination methods [27].

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  1. Nokia, Av. Calouste Gulbenkian 1, 3810-193, Aveiro, Portugal

    João A. Bastos

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Bastos, J.A. Forecasting the capacity of mobile networks. Telecommun Syst 72, 231–242 (2019). https://doi.org/10.1007/s11235-019-00556-w

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