Bounding the Practical Error of Path Loss Models

We seek to provide practical lower bounds on the prediction accuracy of path loss models. We describe and implement 30 propagation models of varying popularity that have been proposed over the last 70 years. Our analysis is performed using a large corpus of measurements collected on production netwo...

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Main Authors: Caleb Phillips, Douglas Sicker, Dirk Grunwald
Format: Article
Language:English
Published: Hindawi Limited 2012-01-01
Series:International Journal of Antennas and Propagation
Online Access:http://dx.doi.org/10.1155/2012/754158
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spelling doaj-8796530676bb4d528ae8554940cec56c2020-11-25T02:20:23ZengHindawi LimitedInternational Journal of Antennas and Propagation1687-58691687-58772012-01-01201210.1155/2012/754158754158Bounding the Practical Error of Path Loss ModelsCaleb Phillips0Douglas Sicker1Dirk Grunwald2Computer Science Department, University of Colorado Boulder, Boulder, CO 80309, USAComputer Science Department, University of Colorado Boulder, Boulder, CO 80309, USAComputer Science Department, University of Colorado Boulder, Boulder, CO 80309, USAWe seek to provide practical lower bounds on the prediction accuracy of path loss models. We describe and implement 30 propagation models of varying popularity that have been proposed over the last 70 years. Our analysis is performed using a large corpus of measurements collected on production networks operating in the 2.4 GHz ISM, 5.8 GHz UNII, and 900 MHz ISM bands in a diverse set of rural and urban environments. We find that the landscape of path loss models is precarious: typical best-case performance accuracy of these models is on the order of 12–15 dB root mean square error (RMSE) and in practice it can be much worse. Models that can be tuned with measurements and explicit data fitting approaches enable a reduction in RMSE to 8-9 dB. These bounds on modeling error appear to be relatively constant, even in differing environments and at differing frequencies. Based on our findings, we recommend the use of a few well-accepted and well-performing standard models in scenarios where a priori predictions are needed and argue for the use of well-validated, measurement-driven methods whenever possible.http://dx.doi.org/10.1155/2012/754158
collection DOAJ
language English
format Article
sources DOAJ
author Caleb Phillips
Douglas Sicker
Dirk Grunwald
spellingShingle Caleb Phillips
Douglas Sicker
Dirk Grunwald
Bounding the Practical Error of Path Loss Models
International Journal of Antennas and Propagation
author_facet Caleb Phillips
Douglas Sicker
Dirk Grunwald
author_sort Caleb Phillips
title Bounding the Practical Error of Path Loss Models
title_short Bounding the Practical Error of Path Loss Models
title_full Bounding the Practical Error of Path Loss Models
title_fullStr Bounding the Practical Error of Path Loss Models
title_full_unstemmed Bounding the Practical Error of Path Loss Models
title_sort bounding the practical error of path loss models
publisher Hindawi Limited
series International Journal of Antennas and Propagation
issn 1687-5869
1687-5877
publishDate 2012-01-01
description We seek to provide practical lower bounds on the prediction accuracy of path loss models. We describe and implement 30 propagation models of varying popularity that have been proposed over the last 70 years. Our analysis is performed using a large corpus of measurements collected on production networks operating in the 2.4 GHz ISM, 5.8 GHz UNII, and 900 MHz ISM bands in a diverse set of rural and urban environments. We find that the landscape of path loss models is precarious: typical best-case performance accuracy of these models is on the order of 12–15 dB root mean square error (RMSE) and in practice it can be much worse. Models that can be tuned with measurements and explicit data fitting approaches enable a reduction in RMSE to 8-9 dB. These bounds on modeling error appear to be relatively constant, even in differing environments and at differing frequencies. Based on our findings, we recommend the use of a few well-accepted and well-performing standard models in scenarios where a priori predictions are needed and argue for the use of well-validated, measurement-driven methods whenever possible.
url http://dx.doi.org/10.1155/2012/754158
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