Path Loss Prediction in Urban Environment Using Learning Machines and Dimensionality Reduction Techniques

Mauro Piacentini () and Francesco Rinaldi ()
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Mauro Piacentini: Dipartimento di Informatica e Sistemistica Sapienza Universita' di Roma, Roma, Italy
Francesco Rinaldi: Dipartimento di Informatica e Sistemistica Sapienza Universita' di Roma, Roma, taly

No 2009-11, DIS Technical Reports from Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza"

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.

Keywords: Path Loss Prediction; Learning Machines; Dimensionality Reduction Techniques. (search for similar items in EconPapers)
Pages: 14 pages
Date: 2009-07
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