Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions
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...
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2012/351487 |
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| _version_ | 1832566381025951744 |
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| author | Ignacio Fernández Anitzine Juan Antonio Romo Argota Fernado Pérez Fontán |
| author_facet | Ignacio Fernández Anitzine Juan Antonio Romo Argota Fernado Pérez Fontán |
| author_sort | Ignacio Fernández Anitzine |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f8d963d063ea44fd8c41a7eaacd74615 |
| institution | Kabale University |
| issn | 1687-5869 1687-5877 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-f8d963d063ea44fd8c41a7eaacd746152025-02-03T01:04:24ZengWileyInternational Journal of Antennas and Propagation1687-58691687-58772012-01-01201210.1155/2012/351487351487Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss PredictionsIgnacio Fernández Anitzine0Juan Antonio Romo Argota1Fernado Pérez Fontán2Department of Electronics and Telecommunications, University of the Basque Country, Alameda Urquijo s/n, 48013 Bilbao, SpainDepartment of Electronics and Telecommunications, University of the Basque Country, Alameda Urquijo s/n, 48013 Bilbao, SpainDepartment of Signal Theory and Communications, University of Vigo, Campus Universitario s/n, 36200 Vigo, SpainThis 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.http://dx.doi.org/10.1155/2012/351487 |
| spellingShingle | Ignacio Fernández Anitzine Juan Antonio Romo Argota Fernado Pérez Fontán Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions International Journal of Antennas and Propagation |
| title | Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions |
| title_full | Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions |
| title_fullStr | Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions |
| title_full_unstemmed | Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions |
| title_short | Influence of Training Set Selection in Artificial Neural Network-Based Propagation Path Loss Predictions |
| title_sort | influence of training set selection in artificial neural network based propagation path loss predictions |
| url | http://dx.doi.org/10.1155/2012/351487 |
| work_keys_str_mv | AT ignaciofernandezanitzine influenceoftrainingsetselectioninartificialneuralnetworkbasedpropagationpathlosspredictions AT juanantonioromoargota influenceoftrainingsetselectioninartificialneuralnetworkbasedpropagationpathlosspredictions AT fernadoperezfontan influenceoftrainingsetselectioninartificialneuralnetworkbasedpropagationpathlosspredictions |