3D Wi-Fi Signal Measurement in Realistic Digital Twin Testbed Environments Using Ray Tracing

TL;DR

This paper introduces a digital twin-based Wi-Fi signal measurement system using ray tracing and 3D environment reconstruction, achieving high fidelity and improved signal modeling for next-generation indoor wireless networks.

Contribution

It presents a novel digital twin framework integrating LiDAR-based environment modeling with GPU-accelerated ray tracing for accurate Wi-Fi signal prediction.

Findings

Up to 21 dB higher path gain compared to commercial simulators

High spatial correlation of 0.98 with onsite RSSI measurements

Effective modeling of frequency-dependent material attenuation for Wi-Fi 6E/7

Abstract

Accurate and efficient modeling of indoor wireless signal propagation is crucial for the deployment of next-generation Wi-Fi. This paper presents a digital twin-based measurement system that integrates real-world 3D environment reconstruction with deterministic ray tracing for physically grounded electromagnetic modeling. Building geometry is obtained through LiDAR scanning, followed by object segmentation and assignment of ITU-R standard material parameters. The propagation process is simulated with a GPU-accelerated ray-tracing engine that generates path-level channel attributes, including delay, power, angular dispersion, and Ricean K-factor. Under identical runtime constraints, the proposed system is evaluated against a commercial measurement simulator, demonstrating up to 21 dB higher path gain and consistently improved signal-to-interference-plus-noise ratio in line-of-sight conditions. Additionally, experiments against onsite RSSI measurements confirm a high spatial correlation of 0.98 after calibration, proving the system's fidelity in real-world settings. Furthermore, coverage analysis across 2.4 GHz, 5 GHz, and 6 GHz bands demonstrates the capability of system to model frequency-dependent material attenuation for Wi-Fi 6E/7 networks. Finally, the system offers interactive 3D visualization and on-demand data extraction, highlighting its potential for digital twin-driven wireless system design and optimization.