DOI: 10.5281/zenodo.17490117
title: Temporal–Spatial Wavefront Sequence Dataset

description: This dataset accompanies the study "Maxwell-Informed Deep Network for Spatial Wireless Channel Modeling".

Code: https://github.com/WangShuchen97/EMPhyNet

 

The datasets are:

1. matlab.zipThis package provides both the scenario data and the generated Temporal–Spatial Wavefront Sequence (TSWS) dataset. The folder ./matlab/map_data contains environmental maps derived from OpenStreetMap, which are utilized in the Ray Tracing process implemented in ./matlab/main.m to produce the TSWS dataset ./matlab/data. The file also includes the processed building data, stored in ./matlab/map_height.

2. data.zipThis package provides datasets for training and testing the EMPhyNet model, compiled by the script ./data_m2pt.py using the TSPFS data (./matlab/data) and building distribution information (./matlab/map_height).

3. checkpoints.zipThis package includes the pretrained EMPhyNet model parameters, comprising three specialized submodels responsible for wavefront trajectory prediction, real component estimation, and imaginary component estimation, respectively. These checkpoints can be directly loaded for evaluation or fine-tuning.

4. results.zipThis file contains the simulation and inference results generated by the trained models. The results can be visualized and evaluated using the script Visualization_and_MSE.py.
description: In wireless communications, interactions of electromagnetic (EM) waves with environmental scatterers lead to multipath propagation, which underpins the spatial multiplexing capabilities of advanced technologies such as large-scale Multiple-Input Multiple-Output (MIMO), beamforming, and Reconfigurable Intelligent Surfaces (RIS). However, the efficiency of these novel systems essentially rely on the accurate understanding of the multipath fading channel, while the capability of traditional channel modelling methods are limited to single-link channel estimation and fail to fully exploit the physical principles of spatial multipath transmission and environmental interactions.We reveal that spatially distributed channel impulse responses (CIRs), when considered jointly, form spatiotemporal wavefronts that govern multipath signal propagation. Building on this physical insight, we introduce the Temporal–Spatial Wavefront Sequence (TSWS) model as a unified representation of spatial CIR evolution, and construct a large-scale TSWS dataset using ray tracing (RT) across 1,075 real-world environments, providing a foundation for systematic channel modeling and transmission-oriented research.

In addition, leveraging the first-principles in physics, we embed Finite Difference Time Domain (FDTD) iterative framework, a full-wave method for solving Maxwell’s equations, into our proposed EM Physics-Informed Network (EMPhyNet), enabling near-real-time, millisecond-level inference of TSWS. The EMPhyNet effectively learns spatial propagation patterns such as direct paths, reflections, amplitude decay, and most importantly, the phase variations, achieving Normalized Root Mean Square Error (NRMSE) and improved masked NRMSE of approximately 0.073 and 0.11, corresponding to 29.5% and 28.0% improvements over the U-shaped Network (U-Net) baseline. Evaluation of variant models shows that those without explicit Maxwell propagation priors fail to capture meaningful signal features.

Together, TSWS and EMPhyNet integrate physics principles with deep learning to accurately reconstruct both amplitude and phase, enabling high-fidelity spatial wireless channel characterization for next-generation communication systems.