Ordinary least squares in COST 231 Hata key parameters optimization base on experimental data

Drozdova, Vera G.; Akhpashev, Ruslan V.

doi:10.1109/sibircon.2017.8109878

Cited by 5 publications

(1 citation statement)

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“…No obstante, también se incorporan otros, dentro de los cuales están: altura de antenas, tipo de entorno, características de los obstáculos. Estos modelos resultan de gran importancia en la predicción de áreas de cobertura, en análisis de interferencia y para determinar parámetros de Estaciones Base (EB) [6].…”

Section: Introductionunclassified

Comparación de modelos de propagación de ondas de radio de un canal inalámbrico en el área urbana de la ciudad de Barranquilla

Barrios-Ulloa

2021

CESTA

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Tres modelos de propagación empíricos fueron comparados: Cost 231 Hata, Walfisch Ikegami (WI) e Interino de la Universidad de Stanford (SUI); para determinar su efectividad en un entorno urbano. Se hicieron mediciones en la banda de los 1900 MHz en un sector de la ciudad de Barranquilla, Colombia; y la evaluación se hizo mediante comparación de valores de error de predicción y relativo de cada modelo con respecto a las mediciones. Los resultados muestran que el modelo WI presentó el menor error relativo en comparación con el Cost 231 Hata y SUI tipo B. Los niveles altos de error indican que los modelos comparados no representan adecuadamente las mediciones en el escenario considerado.

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Section: Introductionunclassified

Comparación de modelos de propagación de ondas de radio de un canal inalámbrico en el área urbana de la ciudad de Barranquilla

Barrios-Ulloa

2021

CESTA

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Large-scale Signal Attenuation and Shadow Fading Measurement and Modelling for Efficient Wireless Network Design and Management

Isabona

Kehinde

Imoize

et al. 2022

2022 IEEE Nigeria 4th International Conference on Disruptive Technologies for Sustainable Development (NIGERCON)

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

Isabona

Imoize

2021

J. Eng. Appl. Sci.

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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.

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Ordinary least squares in COST 231 Hata key parameters optimization base on experimental data

Cited by 5 publications

References 0 publications

Comparación de modelos de propagación de ondas de radio de un canal inalámbrico en el área urbana de la ciudad de Barranquilla

Comparación de modelos de propagación de ondas de radio de un canal inalámbrico en el área urbana de la ciudad de Barranquilla

Large-scale Signal Attenuation and Shadow Fading Measurement and Modelling for Efficient Wireless Network Design and Management

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

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