MDT Geolocation Through Machine Learning: Evaluation of Supervised Regression ML Algorithms
Canadell Solana, Aria
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Minimizing Drive Test is a statistical protocol used to evaluate the network performance. It provides several benefits with respect to traditional drive test analysis; however, multiple inconveniences exist that prevent cell companies from precisely retrieving most of the locations of these reports. . MATLAB and Jupyter Notebook were used to prepare the data and create the models. Multiple supervised regression algorithms were tested and evaluated. The best predictions were obtained from the K-Nearest Neighbor algorithm with one ‘k’ and distance-weighted predictions. The UE geolocation was predicted with a median accuracy of 5.42 meters, a mean error of 61.62 meters, and a mode distance error of zero meters. Based on these results, there is evidence of the promising potential of machine learning algorithms applied to MDT geolocation problems.