Terrain-based adaption of propagation model loss parameters using non-linear square regression

Abstract Reliable and real-time propagation loss modeling play a significant role in the efficient planning, development, and optimization of macrocellular communication networks in a given terrain. Thus, the need to adapt or tune an existing model to enhance its signal prediction accuracy in a spec...

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Autores principales:	Joseph Isabona, Agbotiname Lucky Imoize
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Radio wave propagation Terrestrial terrains Egli model adaptation Propagation loss Non-linear square regression Levenberg-Marquart algorithm Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/b5ee21faa22045669135c7502211cfc1
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spelling	oai:doaj.org-article:b5ee21faa22045669135c7502211cfc12021-11-28T12:14:26ZTerrain-based adaption of propagation model loss parameters using non-linear square regression10.1186/s44147-021-00035-71110-19032536-9512https://doaj.org/article/b5ee21faa22045669135c7502211cfc12021-11-01T00:00:00Zhttps://doi.org/10.1186/s44147-021-00035-7https://doaj.org/toc/1110-1903https://doaj.org/toc/2536-9512Abstract Reliable and real-time propagation loss modeling play a significant role in the efficient planning, development, and optimization of macrocellular communication networks in a given terrain. Thus, the need to adapt or tune an existing model to enhance its signal prediction accuracy in a specified terrain becomes imperative. In this paper, we proposed and applied a non-linear square regression method based on the Levenberg-Marquart (LM) algorithm to adapt and improve the empirical propagation loss estimation accuracy of the Egli model for two major cities in Nigeria. A comprehensive propagation loss measurement acquired over Long Term Evolution (LTE) mobile broadband networks operating at 2630 MHz for four different cities was collected using TEMS investigation tools to achieve the Egli model adaption. Results indicate that the adapted Egli model displays a high estimation accuracy over the Gauss-Newton (GN) algorithm leveraging the non-linear regression method employed to benchmark the propagation loss estimation. Using six standard statistical indicators, the adapted Egli model displayed lower estimation errors than the classical Egli model across the tested locations in the two cities investigated. Finally, the LM-adapted Egli model was compared with extensive measurements from another eNodeB in Port Harcourt different from the initial four eNodeBs investigated. The results indicate that the adapted model is suitable for deployment in related macrocellular environments.Joseph IsabonaAgbotiname Lucky ImoizeSpringerOpenarticleRadio wave propagationTerrestrial terrainsEgli model adaptationPropagation lossNon-linear square regressionLevenberg-Marquart algorithmEngineering (General). Civil engineering (General)TA1-2040ENJournal of Engineering and Applied Science, Vol 68, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Radio wave propagation Terrestrial terrains Egli model adaptation Propagation loss Non-linear square regression Levenberg-Marquart algorithm Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Radio wave propagation Terrestrial terrains Egli model adaptation Propagation loss Non-linear square regression Levenberg-Marquart algorithm Engineering (General). Civil engineering (General) TA1-2040 Joseph Isabona Agbotiname Lucky Imoize Terrain-based adaption of propagation model loss parameters using non-linear square regression
description	Abstract Reliable and real-time propagation loss modeling play a significant role in the efficient planning, development, and optimization of macrocellular communication networks in a given terrain. Thus, the need to adapt or tune an existing model to enhance its signal prediction accuracy in a specified terrain becomes imperative. In this paper, we proposed and applied a non-linear square regression method based on the Levenberg-Marquart (LM) algorithm to adapt and improve the empirical propagation loss estimation accuracy of the Egli model for two major cities in Nigeria. A comprehensive propagation loss measurement acquired over Long Term Evolution (LTE) mobile broadband networks operating at 2630 MHz for four different cities was collected using TEMS investigation tools to achieve the Egli model adaption. Results indicate that the adapted Egli model displays a high estimation accuracy over the Gauss-Newton (GN) algorithm leveraging the non-linear regression method employed to benchmark the propagation loss estimation. Using six standard statistical indicators, the adapted Egli model displayed lower estimation errors than the classical Egli model across the tested locations in the two cities investigated. Finally, the LM-adapted Egli model was compared with extensive measurements from another eNodeB in Port Harcourt different from the initial four eNodeBs investigated. The results indicate that the adapted model is suitable for deployment in related macrocellular environments.
format	article
author	Joseph Isabona Agbotiname Lucky Imoize
author_facet	Joseph Isabona Agbotiname Lucky Imoize
author_sort	Joseph Isabona
title	Terrain-based adaption of propagation model loss parameters using non-linear square regression
title_short	Terrain-based adaption of propagation model loss parameters using non-linear square regression
title_full	Terrain-based adaption of propagation model loss parameters using non-linear square regression
title_fullStr	Terrain-based adaption of propagation model loss parameters using non-linear square regression
title_full_unstemmed	Terrain-based adaption of propagation model loss parameters using non-linear square regression
title_sort	terrain-based adaption of propagation model loss parameters using non-linear square regression
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/b5ee21faa22045669135c7502211cfc1
work_keys_str_mv	AT josephisabona terrainbasedadaptionofpropagationmodellossparametersusingnonlinearsquareregression AT agbotinameluckyimoize terrainbasedadaptionofpropagationmodellossparametersusingnonlinearsquareregression
_version_	1718408162550218752

Terrain-based adaption of propagation model loss parameters using non-linear square regression

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