01634nas a2200265   4500000000100000000000100001008004100002260001200043653002100055653004600076653001000122653002100132653001900153653002700172653000700199100001800206700002000224700001600244245014600260856008100406300001200487490000600499520084900505022001401354       2021                            d  c06/202110aPropagation Loss10aReceived Signal Strength Indicator (RSSI)10aRadio10aMachine Learning10aClassification10aSupport Vector Machine10a5G1 aAkansha Gupta1 aKamal Ghanshala1 aR. C. Joshi00aMachine Learning Classifier Approach with Gaussian Process, Ensemble boosted Trees, SVM, and Linear Regression for 5G Signal Coverage Mapping  uhttps://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_16.pdf  a156-1630 v63 aThis 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.  a1989-1660