Machine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping

Authors

Akansha Gupta
Kamal Ghanshala
R. C. Joshi

DOI

10.9781/ijimai.2021.03.004

Abstract

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.

Source Publication

International Journal of Interactive Multimedia and Artificial Intelligence

Recommended Citation

Gupta, Akansha; Ghanshala, Kamal; and Joshi, R. C. (2021) "Machine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping," International Journal of Interactive Multimedia and Artificial Intelligence: Vol. 6: Iss. 6, Article 8.
DOI: 10.9781/ijimai.2021.03.004
Available at: https://ijimai.researchcommons.org/ijimai/vol6/iss6/8