2020
DOI: 10.3390/telecom1020009 View full text |Buy / Rent full text
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Abstract: Machine learning models have been widely deployed to tackle the problem of radio propagation. In addition to helping in the estimation of path loss, they can also be used to better understand the details of various propagation scenarios. Our current work exploits the inherent ranking of feature importances provided by XGBoost and Random Forest as a means of indicating the contribution of the underlying propagation mechanisms. A comparison between two different transmitter antenna heights, revealing the associa… Show more

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