Physics-informed neural networks for pathloss prediction

TL;DR

This paper presents a physics-informed neural network approach for pathloss prediction that enhances accuracy and efficiency by incorporating physical laws and limited data, suitable for real-world applications.

Contribution

It introduces a novel physics-informed training method for neural networks that improves pathloss prediction with fewer layers and less data.

Findings

Improved generalization and prediction quality

Fast inference times suitable for localization

Effective with limited training data

Abstract

This paper introduces a physics-informed machine learning approach for pathloss prediction. This is achieved by including in the training phase simultaneously (i) physical dependencies between spatial loss field and (ii) measured pathloss values in the field. It is shown that the solution to a proposed learning problem improves generalization and prediction quality with a small number of neural network layers and parameters. The latter leads to fast inference times which are favorable for downstream tasks such as localization. Moreover, the physics-informed formulation allows training and prediction with a small amount of training data which makes it appealing for a wide range of practical pathloss prediction scenarios.