Path Loss Prediction Using Deep Learning

Radio deployments and spectrum planning benefit from path loss predictions. Obstructions along a communications link are often considered implicitly or through derived metrics such as representative clutter height or total obstruction depth. In this paper, we propose a path-specific path loss prediction method that uses convolutional neural networks to automatically perform feature extraction from high-resolution obstruction height maps. Our methods result in low prediction error in a variety of environments without requiring derived metrics.