volume | PIER Journals

Abstract

Accurate modeling of millimeter-wave (mmWave) and terahertz (THz) electromagnetic wave propagation is crucial for analyzing and designing emerging high-frequency wireless systems at an early stage. Conventional parabolic equation (PE)-based models offer high computational efficiency but suffer from reduced accuracy at mmWave/THz frequencies owing to material losses, fine-scale scattering, and complex non-line-of-sight (NLOS) interactions. Although purely data-driven approaches are flexible, they often lack physical consistency and generalization capability. This study proposes an AI-enhanced parabolic equation (AI-PE) framework that integrates a wide-angle PE solver with a neural-network-based residual correction model. The AI component learns systematic PE prediction errors associated with frequency-dependent attenuation, diffraction, and scattering while preserving the underlying physical structure of the wave model. Validation was performed against full-wave and ray-tracing reference solutions in representative indoor corridor and urban microcell scenarios. The numerical results at 28, 60, and 140 GHz demonstrate a 25-40% reduction in the path-loss prediction error, improved statistical agreement of the RMS delay-spread estimates, and over 50% reduction in the computational cost compared with deterministic ray tracing. The energy conservation and phase continuity of the corrected fields were explicitly verified. The framework was primarily validated for interpolation within the trained frequency range and demonstrated robust performance across structured propagation environments.

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Dr. Mohammad Ahmad