A 3-D indoor analysis of path loss modeling using kriging techniques
Date
2022Author
Version
Acceso abierto / Sarbide irekia
Type
Artículo / Artikulua
Version
Versión publicada / Argitaratu den bertsioa
Project Identifier
AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095499-B-C31/ES/
Impact
|
10.1109/LAWP.2022.3162160
Abstract
This study proposes a novel measurement-based method to predict and model three-dimensional (3-D) path loss in indoor scenarios, which first regresses 28 GHz measurements via median path loss modeling and then includes ordinary Kriging to interpolate shadowing. The performance of this method is evaluated by investigating the spatial structure that follows shadowing through the semivariogram, cova ...
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This study proposes a novel measurement-based method to predict and model three-dimensional (3-D) path loss in indoor scenarios, which first regresses 28 GHz measurements via median path loss modeling and then includes ordinary Kriging to interpolate shadowing. The performance of this method is evaluated by investigating the spatial structure that follows shadowing through the semivariogram, covariance function, and correlogram as variography tools. It is shown that semivariogram outperforms the other statistics to describe shadowing spatial continuity in path loss modeling in terms of the mean absolute error. [--]
Subject
Indoor path loss model,
Kriging,
Three-dimen- sional (3-D),
Variography
Publisher
IEEE
Published in
Ieee Antennas And Wireless Propagation Letters, 2022, 21 (6),1218-1222
Departament
Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica, Electrónica y de Comunicación /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC
Publisher version
Sponsorship
This work was supported by the National Council of Science and Technology CONACYT, through
the student scholarship number 746015, under Project RTI2018-095499-B-C31,
funded by the Ministerio de Ciencia, Innovación y Universidades, Gobierno de
España (MCIU/AEI/FEDER, UE).