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International Journal of Antennas and Propagation
Volume 2012 (2012), Article ID 351487, 7 pages
http://dx.doi.org/10.1155/2012/351487
Research Article

Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions

1Department of Electronics and Telecommunications, University of the Basque Country, Alameda Urquijo s/n, 48013 Bilbao, Spain
2Department of Signal Theory and Communications, University of Vigo, Campus Universitario s/n, 36200 Vigo, Spain

Received 26 July 2012; Accepted 19 October 2012

Academic Editor: César Briso Rodríguez

Copyright © 2012 Ignacio Fernández Anitzine et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper analyzes the use of artificial neural networks (ANNs) for predicting the received power/path loss in both outdoor and indoor links. The approach followed has been a combined use of ANNs and ray-tracing, the latter allowing the identification and parameterization of the so-called dominant path. A complete description of the process for creating and training an ANN-based model is presented with special emphasis on the training process. More specifically, we will be discussing various techniques to arrive at valid predictions focusing on an optimum selection of the training set. A quantitative analysis based on results from two narrowband measurement campaigns, one outdoors and the other indoors, is also presented.