OpenGERT: Open Source Automated Geometry Extraction with Geometric and Electromagnetic Sensitivity Analyses for Ray-Tracing Propagation Models

TL;DR

OpenGERT addresses the challenge of accurately modeling RF propagation in urban environments by automating geometry extraction from multiple data sources and integrating with NVIDIA Sionna RT for ray-tracing. The authors show that building height and position perturbations drive large variability in $PG$, $MED$, and $DS$, while dielectric property uncertainties have comparatively smaller effects on $K$-factor. They implement two data pipelines (Blender-based and Python-based) to generate consistent 3D scenes for DST applications across Munich and Etoile and quantify how geometry and material uncertainties propagate to channel statistics, using 50 perturbations per Tx location. This work underscores the importance of precise environmental modeling for reliable propagation predictions and provides open-source tooling to support community adoption.

Abstract

Accurate RF propagation modeling in urban environments is critical for developing digital spectrum twins and optimizing wireless communication systems. We introduce OpenGERT, an open-source automated Geometry Extraction tool for Ray Tracing, which collects and processes terrain and building data from OpenStreetMap, Microsoft Global ML Building Footprints, and USGS elevation data. Using the Blender Python API, it creates detailed urban models for high-fidelity simulations with NVIDIA Sionna RT. We perform sensitivity analyses to examine how variations in building height, position, and electromagnetic material properties affect ray-tracing accuracy. Specifically, we present pairwise dispersion plots of channel statistics (path gain, mean excess delay, delay spread, link outage, and Rician K-factor) and investigate how their sensitivities change with distance from transmitters. We also visualize the variance of these statistics for selected transmitter locations to gain deeper insights. Our study covers Munich and Etoile scenes, each with 10 transmitter locations. For each location, we apply five types of perturbations: material, position, height, height-position, and all combined, with 50 perturbations each. Results show that small changes in permittivity and conductivity minimally affect channel statistics, whereas variations in building height and position significantly alter all statistics, even with noise standard deviations of 1 meter in height and 0.4 meters in position. These findings highlight the importance of precise environmental modeling for accurate propagation predictions, essential for digital spectrum twins and advanced communication networks. The code for geometry extraction and sensitivity analyses is available at github.com/serhatadik/OpenGERT/.

Figures (6)

• Figure 1: Geometry extraction pipeline.
• Figure 2: Transmitter Locations Used for Sensitivity Analysis on the Munich and Etoile Scenes
• Figure 3: Analysis of Path Gain, Mean Excess Delay, and Delay Spread Standard Deviations and Link Outage Frequency with Height Perturbation in Etoile Scene
• Figure 4: Histograms of Broken Links for Different Types of Perturbations in Munich and Etoile.
• Figure 5: Combined Analysis of Path Gain, Mean Excess Delay, and Delay Spread Standard Deviations for Munich and Etoile Across All Perturbation Types