Physics-informed neural networks for pathloss prediction

This paper presents a novel physics-informed machine learning method for pathloss prediction that significantly enhances generalization and prediction accuracy. The approach uniquely integrates both the inherent physical relationships within the spatial loss field and empirical pathloss measurements directly into the neural network’s training process. This dual-constraint learning problem enables the model to achieve superior performance with fewer layers and parameters, resulting in exceptionally fast inference times crucial for subsequent applications like localization. Furthermore, the physics-informed framework substantially reduces the need for extensive training data, making this method highly adaptable and practical for diverse real-world pathloss prediction challenges.