Abstract
Path loss prediction is a crucial task for the planning of networks in modern mobile communication systems. Learning machine-based models seem to be a valid alternative to empirical and deterministic methods for predicting the propagation path loss. As learning machine performance depends on the number of input features, a good way to get a more reliable model can be to use techniques for reducing the dimensionality of the data. In this paper we propose a new approach combining learning machines and dimensionality reduction techniques. We report results on a real dataset showing the efficiency of the learning machine-based methodology and the usefulness of dimensionality reduction techniques in improving the prediction accuracy.
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Piacentini, M., Rinaldi, F. Path loss prediction in urban environment using learning machines and dimensionality reduction techniques. Comput Manag Sci 8, 371–385 (2011). https://doi.org/10.1007/s10287-010-0121-8
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DOI: https://doi.org/10.1007/s10287-010-0121-8