Path Loss Measurement Validation Using Naïve Bayes Classification for High-Speed Rail
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This work proposes path loss measurement validation for high-speed rail wireless communication using Naïve Bayes Classification Method. Path loss propagation model is a major component in wireless network planning in order to meet expected service level requirements and optimized cell towers distribution. However, path loss in high-speed rail propagates rapidly because of unexpected environmental shifts, high mobility, and unique channel modelling. All these unique characteristics lead to inaccuracies and uncertainties in the path loss measurement. Naïve Bayes Classification with Gaussian Classifier offers a better solution in validating path loss measurement than the conventional method for typical wireless communication, which provides statistical descriptions of site-specific environments. The measurement dataset has been taken as continuous values and it is assumed to be distributed according to normal distribution. The dataset has been collected from a running high-speed train. Data discretization has been used as a pre-processing step of transforming continuous data attribute values into a finite set of intervals consisting of path loss in maximum and minimum speed for accuracy or inaccuracy class. The satisfying result of the best performance has been obtained in Accuracy Value (0.9114), Precision Value (0.9147), Recall Value (0.8772), F1-score (0.8956), and Area Under Curve score (0.83). The results have shown that the Naive Bayes method can potentially replace the conventional method in validating path loss measurement, especially for high-speed rail.
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