Machine-Learning-Based Urban Path Loss Prediction at 900 MHz: Principal Component Analysis, Clustering, Feature Importance and Regression

In this work, the possibility of applying machine learning (ML) techniques to analyze and predict radio wave propagation losses in urban environments is explored. Thus, from a measurement campaign–conducted at 900 MHz in Cartagena, Spain– and the obtaining, by means of digital terrain...

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Ngā kaituhi matua:	Jose Lorente-Lopez, Ignacio Rodriguez-Rodriguez, Jose-Victor Rodriguez, Jose Maria Molina Garcia-Pardo, Juan Aznar-Poveda
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	IEEE 2026-01-01
Rangatū:	IEEE Open Journal of Antennas and Propagation
Ngā marau:	Machine learning techniques path-loss prediction radio wave propagation models urban environments
Urunga tuihono:	https://ieeexplore.ieee.org/document/11231369/
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Machine-Learning-Based Urban Path Loss Prediction at 900 MHz: Principal Component Analysis, Clustering, Feature Importance and Regression

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