Path Loss Prediction in Urban Environment Using Learning Machines and Dimensionality Reduction Techniques
AbstractPath 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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Bibliographic InfoPaper provided by Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza" in its series DIS Technical Reports with number 2009-11.
Length: 14 pages
Date of creation: Jul 2009
Date of revision:
Path Loss Prediction; Learning Machines; Dimensionality Reduction Techniques.;
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