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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2021
    • vol. 6, nº 6, june 2021
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2021
    • vol. 6, nº 6, june 2021
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    Machine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping

    Autor: 
    Gupta, Akansha
    ;
    Ghanshala, Kamal
    ;
    Joshi, R. C.
    Fecha: 
    06/2021
    Palabra clave: 
    propagation loss; received signal strength indicator (RSSI); radio; machine learning; classification; support vector machine; 5G; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12978
    DOI: 
    https://doi.org/10.9781/ijimai.2021.03.004
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2923
    Open Access
    Resumen:
    This article offers a thorough analysis of the machine learning classifiers approaches for the collected Received Signal Strength Indicator (RSSI) samples which can be applied in predicting propagation loss, used for network planning to achieve maximum coverage. We estimated the RMSE of a machine learning classifier on multivariate RSSI data collected from the cluster of 6 Base Transceiver Stations (BTS) across a hilly terrain of Uttarakhand-India. Variable attributes comprise topology, environment, and forest canopy. Four machine learning classifiers have been investigated to identify the classifier with the least RMSE: Gaussian Process, Ensemble Boosted Tree, SVM, and Linear Regression. Gaussian Process showed the lowest RMSE, R- Squared, MSE, and MAE of 1.96, 0.98, 3.8774, and 1.3202 respectively as compared to other classifiers.
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