On the predictability of next generation mobile network traffic using artificial neural networks

Summary Though the introduction of the new 4th Generation mobile access technologies promises to satisfy the increasing bandwidth demand of the end‐users, it poses in parallel the need for novel resource management approaches at the side of the base station. To this end, schemes that try to predict the forthcoming bandwidth demand using supervised learning methods have been proposed in the literature. However, there are still open issues concerning the training phase of such methods. In the current work, the authors propose a novel scheme that dynamically selects a proper training set for artificial neural network prediction models, based on the statistical characteristics of the collected data. It is demonstrated that an initial statistical processing of the collected data and the subsequent selection of the training set can efficiently improve the performance of the prediction model. Finally, the proposed scheme is validated using network traffic collected by real, fully operational base stations. Copyright © 2013 John Wiley & Sons, Ltd. The increasing demand for wireless broadband services necessitates for novel resource management approaches at the side of the base station. To this end, a supervised learning method for predicting the forthcoming network traffic demand is proposed in the current paper. It is shown that selecting an appropriate dataset for the training phase of the prediction model, based on an initial statistical processing of the collected data, can further improve its performance.