A Supervised Learning Approach to Cognitive Access Point Selection

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In this paper, the authors present a cognitive AP selection scheme based on a supervised learning approach. In their proposal the mobile station collects measurements regarding the past link conditions and throughput performance, and leverages on this data in order to learn how to predict the performance of the available APs in order to select the best one. The prediction capabilities in their scheme are achieved by employing a Multi-layer Feed-forward Neural Network (MFNN) to learn the correlation between the observed environmental conditions and the obtained performance.