Hybrid Ray-Tracing and Physics-Embedded Neural Modeling for High-Fidelity Channel Reconstruction in Wireless Digital Twins

2026-10-04

AI Summary

This study addresses the limited accuracy of ray tracing (RT) in diffraction and refraction scenarios within wireless digital twins by proposing a hybrid channel reconstruction framework that integrates RT with physics-embedded neural networks. The framework innovatively incorporates an edge diffraction kernel, a lossy dielectric propagation model, and a local tunneling strategy, effectively balancing computational efficiency with modeling fidelity. Experimental results demonstrate that the proposed method significantly outperforms conventional pure RT approaches, yielding predictions that closely match full-wave simulation reference solutions. Furthermore, the framework has been successfully integrated into an indoor digital twin system, establishing a new paradigm for high-fidelity channel modeling in complex electromagnetic environments.

Abstract

Many existing wireless digital twins (DTs) rely heavily on ray-tracing (RT)-based channel simulators to achieve scalability in large-scale environments. However, the geometric-optics (GO) approximations underlying RT become inaccurate in propagation regimes dominated by diffraction and refraction, which are prevalent at sub-6 GHz carrier frequencies. This paper presents a hybrid RT and physics-embedded physics-informed neural network (PE-PINN) framework that enhances site-specific wireless DTs with physics-consistent full-wave accuracy. Building on prior PE-PINN work, we introduce targeted architectural extensions, including a finite-length edge diffraction kernel and a lossy dielectric formulation based on complex wavenumbers, to accurately model electromagnetic wave interactions with realistic objects and materials. To balance accuracy and efficiency, we further propose a localized tunnel strategy that deploys PE-PINN models only along dominant propagation paths, selectively replacing or augmenting RT predictions in regions where GO assumptions break down. Comprehensive evaluations against full-wave COMSOL simulations demonstrate that the proposed framework closely matches reference solutions and significantly outperforms RT-only approaches in diffraction- and refraction-dominated scenarios. We further demonstrate seamless integration of the proposed approach into a realistic indoor wireless DT, highlighting its practicality and scalability for next-generation wireless network design.

Problem

• Wireless Digital Twins
• Ray-Tracing
• Channel Reconstruction
• Diffraction and Refraction
• Sub-6 GHz

Innovation

• Wireless Digital Twin
• Physics-Embedded Neural Network
• Ray-Tracing
• Edge Diffraction Kernel
• Localized Tunnel Strategy