Performance Analysis Of Out-of-the-shelf Regressors for Accurate Indoor Positioning

This paper presents a novel indoor positioning system utilizing machine learning for user localization based on path loss estimates from strategically placed access points. We compare the performance of different out-of-the-shelf regressors and leverage the Extra Trees Regressor algorithm's randomness, avoiding overfitting and outperforming traditional methods like K-Nearest Neighbors. Our system, simulated under the 3GPP 28 GHz indoor channel model, focuses on improving Root Mean Square Error and R-squared metrics. Findings affirm the system's robustness and machine learning's potential in enhancing indoor positioning accuracy.