发表机构
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对无线数字孪生中射线追踪在衍射折射场景精度不足的问题，提出混合RT与物理嵌入神经网络的框架，通过架构扩展和局部隧道策略实现高保真信道重建，显著优于纯RT方法。
AI 中文摘要
许多现有的无线数字孪生（DT）在大型环境中实现可扩展性时，严重依赖基于射线追踪（RT）的信道模拟器。然而，RT所依据的几何光学（GO）近似在由衍射和折射主导的传播机制中变得不准确，而这些机制在sub-6 GHz载波频率下普遍存在。本文提出了一种混合RT与物理嵌入物理信息神经网络（PE-PINN）框架，该框架以物理一致的全波精度增强特定地点的无线DT。在先前PE-PINN工作的基础上，我们引入了针对性的架构扩展，包括有限长度边缘衍射核和基于复波数的有损介电公式，以准确模拟电磁波与真实物体和材料的相互作用。为了平衡精度与效率，我们进一步提出了一种局部隧道策略，仅在主要传播路径上部署PE-PINN模型，在GO假设失效的区域选择性地替换或增强RT预测。针对全波COMSOL模拟的全面评估表明，所提出的框架与参考解紧密匹配，并且在衍射和折射主导的场景中显著优于仅使用RT的方法。我们进一步展示了所提出方法在现实室内无线DT中的无缝集成，突显了其对于下一代无线网络设计的实用性和可扩展性。
英文摘要
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.
CommentsSubmitted to an IEEE transaction
