Diago Mosquera, MelissaAragón Zavala, AlejandroRodríguez Corbo, Fidel AlejandroCelaya Echarri, MikelShubair, Raed M.Azpilicueta Fernández de las Heras, Leyre2024-01-242024-01-242022Diago-Mosquera, M., Aragon-Zavala, A., Rodriguez-Corbo, F. A., Celaya-Echarri, M., Shubair, R., & Azpilicueta, L. (2022). Tuning selection impact on kriging-aided in-building path loss modeling. IEEE Antennas and Wireless Propagation Letters, 21(1), 84-88. https://doi.org/10.1109/LAWP.2021.31186731536-122510.1109/LAWP.2021.3118673https://academica-e.unavarra.es/handle/2454/47141How 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.application/pdfengThis work is licensed under a Creative Commons Attribution 4.0 License.Indoor path loss modelKrigingRadio propagationTuning datasetinfo:eu-repo/semantics/article2024-01-24info:eu-repo/semantics/openAccess