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Conference Paper
Comparison of Machine Learning Algorithms for Propagation Path Loss Prediction Using Ray Tracing Data
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- Authors
- Chaewon Yoon, Junseok Kim, Hyuk-Je Kim, Chung Sup Kim, Sung Woong Choi, Il-Kyoo Lee
- Issue Date
- 2024-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2024, pp.2182-2184
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we compared several regression algorithms through machine learning approach to predict path loss in an outdoor environment using only the location information of Tx and Rx. We constructed an outdoor environment with intersections, buildings and collected path loss data through Ray-tracing. After training by adjusting hyperparameters, two algorithms with low Mean Squared Error (MSE) values were compared in detail by statistically comparing the values of Mean Absolute Error (MAE), MSE, Root Mean Square Error (RMSE), and R2 using K-fold cross validation. The results showed that the K-Nearest Neighbor (K-NN) algorithm had the best prediction performance for path loss. The prediction model also showed faster prediction time compared to the estimation time of Ray tracing.
- KSP Keywords
- Cross validation(CV), K-Nearest Neighbor(KNN), K-fold cross validation, Location information, Machine Learning Algorithms, Machine Learning Approach, Mean Absolute Error, Outdoor environments, Path Loss Prediction, Prediction Time, Propagation path loss