Azpilicueta Fernández de las Heras, Leyre

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https://academica-e.unavarra.es/handle/2454/48642

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Azpilicueta Fernández de las Heras

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Leyre

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Ingeniería Eléctrica, Electrónica y de Comunicación

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ISC. Institute of Smart Cities

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0000-0002-3821-0105

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750752

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810018

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Author: Aragón Zavala, Alejandro × Start date: 2022 ×

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    PublicationOpen Access
    Tuning selection impact on kriging-aided in-building path loss modeling
    (IEEE, 2022) Diago Mosquera, Melissa; Aragón Zavala, Alejandro; Rodríguez Corbo, Fidel Alejandro; Celaya Echarri, Mikel; Shubair, Raed M.; Azpilicueta Fernández de las Heras, Leyre; Estadística, Informática y Matemáticas; Ingeniería Eléctrica, Electrónica y de Comunicación; Estatistika, Informatika eta Matematika; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
    How do you know you select enough tuning dataset from measurements to guarantee model prediction accuracy? Tuning datasets are often selected based on simple random sampling with predefined rates. Usually, these rates are determined as a/b, where a% of the data goes to training and the remaining b% goes to testing. But it is not clear to what extent tuning dataset in order to minimize the estimation path loss errors. It is, thus, required to analyze the performance of channel modeling by selecting—among all measurement samples—appropriate tuning dataset. Using radio measurements and deterministic Ray Launching techniques to collect enough reliable samples, this letter analyzes the impact of tuning dataset selection—expressed in terms of the mean absolute error and cost—on a novel Kriging-aided in-building measurement-based path loss prediction model.
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    PublicationOpen Access
    A 3-D indoor analysis of path loss modeling using kriging techniques
    (IEEE, 2022) Diago Mosquera, Melissa; Aragón Zavala, Alejandro; Azpilicueta Fernández de las Heras, Leyre; Shubair, Raed M.; Falcone Lanas, Francisco; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación
    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.