Using Learning Methods for V2V Path Loss Prediction

Predicting the performance of vehicular communication networks is challenging due to the interplay of multiple factors. One prominently influencing factor is the wireless channel between the transmitter and the receiver. We address the problem of predicting the path loss between two communicating vehicles by using a non-parameterized, data-driven approach. Specifically, we apply Random Forest, a non-parametric learning method, to real world vehicle-to-vehicle communications dataset and evaluate it with respect to its prediction accuracy and generalization capability. We show that availability of additional information to the non-parametric model results in better performance than the well known parameterized log distance path loss model. We further discuss the relative contribution of different features for the model accuracy and conclude that careful selection of features can achieve results nearly as accurate as using all available features. Finally, we discuss several aspects that need to be considered while using such data-driven prediction models along with applications of V2V path loss prediction.