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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2012-01-01
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Series: | International Journal of Antennas and Propagation |
Online Access: | http://dx.doi.org/10.1155/2012/754158 |
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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 |
work_keys_str_mv |
AT calebphillips boundingthepracticalerrorofpathlossmodels AT douglassicker boundingthepracticalerrorofpathlossmodels AT dirkgrunwald boundingthepracticalerrorofpathlossmodels |
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