Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels

The use of vehicular communications is anticipated to improve safety in road traffic. The traditional radio channel models that describe the effects of radio wave propagation in dynamic vehicular environments have their own limitations. In this paper, machine learning techniques are applied for radio channel modeling in urban vehicular environments. A large data set of path loss and RMS delay spread is computed using ray-tracing for a Line-of-Sight (LOS) straight road and a Non Line-of-Sight (NLOS) intersection road scenario. Fourteen input features are used to train three machine learning models for vehicular channel prediction. The models considered in this work include Multi-layer perceptron (MLP), Convolutional neural network (CNN) and Random forest (RF). The results show that RF gives better performance than MLP and CNN models in prediction of path loss and RMS delay spread in urban vehicular channels.