Machine Learning-Based Line-Of-Sight Prediction in Urban Manhattan-Like Environments

Abstract

This paper considers the problem of predicting whether or not a transmitter and a receiver are in Line-of-Sight (LOS) condition. While this problem can be easily solved using a digital urban database and applying ray tracing, we consider the scenario in which only few high-level features descriptive of the propagation environment and of the radio link are available. LOS prediction is modelled as a binary classification Machine Learning problem, and a baseline classifier based on Gradient Boosting Decision Trees (GBDT) is proposed. A synthetic raytracing dataset of Manhattan-like topologies is generated for training and testing a GBDT classifier, and its generalization capabilities to both locations and environments unseen at training time are assessed. Results show that the GBDT model achieves good classification performance and provides accurate LOS probability modelling. By estimating feature importance, it can be concluded that the model learned simple decision rules that align with common sense.