1. Comparing Machine Learning Methods for Air-to-Ground Path Loss Prediction
- Author
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George V. Tsoulos, Sotirios P. Sotiroudis, Sotirios K. Goudos, Georgia Athanasiadou, and George Vergos
- Subjects
Ensemble forecasting ,Computer science ,business.industry ,Machine learning ,computer.software_genre ,Random forest ,k-nearest neighbors algorithm ,Support vector machine ,Base station ,ComputingMethodologies_PATTERNRECOGNITION ,Path loss ,Ray tracing (graphics) ,Artificial intelligence ,AdaBoost ,business ,computer - Abstract
Machine Learning-based models gain increasingly momentum regarding the problem of path loss prediction. The work at hand deploys four machine learning algorithms (k Nearest Neighbors - kNN, Support Vector Regression - SVR, Random Forest - RF and AdaBoost), in order to simulate the radio coverage provided from a flying base station in the greek city of Tripolis. Their comparison shows that tree-based ensemble models (RF and AdaBoost) can be used as fast and reliable alternatives to the Ray Tracing technique.
- Published
- 2021
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