Physics-Inspired Convolutional Neural Network for Scalable Modeling of Radio Wave Propagation

Abstract

Reliable estimation of radio wave propagation across irregular terrain is essential for the effective planning and optimization of contemporary wireless communication systems, such as cellular networks, broadcasting infrastructure, and radar systems. The intricate interaction between electromagnetic waves and environmental features—including mountains, depressions, and artificial structures—requires sophisticated modeling approaches capable of accounting for diffraction, reflection, scattering, and shadowing effects caused by terrain variations. The use of the split-step parabolic equation (SSPE) method for modeling radio wave propagation over irregular terrain has become increasingly popular. However, high computational cost limits its practical deployment. This has led to growing interest in machine learning (ML) as a more efficient alternative. A major challenge of ML in electromagnetic applications lies in accurately predicting results for scenarios not represented in the training data—a limitation not yet fully addressed by existing ML-based propagation models. To overcome this challenge, a high-fidelity, scalable physics-inspired modeling framework is presented. The proposed method effectively adapts to various terrain profiles and antenna setups, demonstrating strong extrapolation performance beyond the training set. Furthermore, another key innovation is the integration of prior knowledge from deterministic physics-based models into the neural network architecture. Additionally, tailoring the network structure to reflect the physical characteristics of terrain-based wave propagation significantly enhances both prediction accuracy and scalability.