Machine Learning Approaches for Radio Propagation Modeling in Urban Vehicular Channels

Abstract

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 raytracing 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.

Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review

“…In [81], among the four machine learning models that were examined for their suitability in path loss prediction at 2.1 GHz, the RF model outperformed the rest of the models with minimum RMSE, MAE, and MAPE values of 4.662 dB, 3.19 dB, and 2.96%, respectively. Similarly, in [88], the RF model outperformed the two other models such as the CNN and MLP at 5.9 GHz, having the minimum RMSE and MAE values of 1.44 dB and 2.19 dB, respectively, followed by the CNN model with an RMSE value of 2.03 dB, as shown in Figure 6. Also, the RF model outperformed the KNN model at 3.5 GHz in [95], with a minimum RMSE value of 3.42 dB.…”

“…Similar approaches in the deployment of machine learning models to predict path loss in the mid-band channel revealed that the random forest model performed excellently in [88,94,95] in an urban environment, while the neural-network-based models performed similarly in [81,84,86]. However, because every urban environment is made of unique layouts and streets, researchers should think about improving the models' performance in the propagation of mid-band 5G network deployment and future wireless communication networks in distinct scenarios of complex urban environments.…”

“…Three machine learning models were used in [88] for radio channel modeling in urban vehicle environments. However, the inquiry was only able to simulate LOS and NLOS conditions utilizing the ray tracing-generated data set.…”

“…In an attempt to present a reliable and stable ML model in [31,[75][76][77][82][83][84][85][87][88][89]91,92] for high-band channels in urban environments, the k-NN, MPL, CNN, and SVR models performed similarly across all frequencies under consideration, with the random forest model (RF) delivering the most accurate predictions in [77,81] and the XGBoost model in [83] in an urban environment.…”

On Machine Learning Techniques Toward Path Loss Modeling in 5g and Beyond Wireless Systems

“…Additionally, vehicular communication is a key technology for intelligent transportation systems (ITS) due to the need for safer, more efficient, and sustainable transportation [10]. ITS applications consider the exchange of data in different vehicular communication among vehicles and with road infrastructure referred to as vehicle-to-vehicle (V2V) [11,12], vehicle-to-infrastructure (V2I) [13,14,15], and vehicle-to-everything (V2X) [16] to provide secure and reliable wireless communications between vehicles to road infrastructure, and among vehicles, respectively, expected to operate in the frequency band below 6 GHz [17,18], and in millimeter wave [11], oriented to short-range communications [19].…”