Calibration of NYURay for Ray Tracing using 28, 73, and 142 GHz Channel Measurements conducted in Indoor, Outdoor, and Factory Scenarios

TL;DR

This paper introduces NYURay, a fast calibration method for a 3D ray tracer at mmWave and sub-THz frequencies, achieving high accuracy in diverse environments with simplified assumptions, useful for wireless channel modeling and machine learning.

Contribution

A novel, simplified calibration approach for NYURay that assumes angle-independent reflection, reducing complexity and computation time while maintaining high accuracy.

Findings

Directional multipath power prediction error < 3 dB indoors and < 2 dB outdoors

Root mean square delay and angular spread underpredicted but overall agreement achieved

High accuracy in site-specific wireless channel simulation across various scenarios

Abstract

Site-specific wireless channel simulations via ray tracers can be used to effectively study wireless, decreasing the need for extensive site-specific radio propagation measurements. To ensure that ray tracer simulations faithfully reproduce wireless channels, calibration of simulation results against real-world measurements is required. In this study we introduce NYURay, a 3D ray tracer specifically tailored for mmWave and sub-THz frequencies. To reliably generate site-specific wireless channel parameters, NYURay is calibrated using radio propagation measurements conducted at 28, 73, and 142 GHz in diverse scenarios such as outdoor areas, indoor offices, and factories. Traditional ray tracing calibration assumes angle-dependent reflection, requiring slow iterative optimization techniques with no closed form solution. We propose a simpler and quicker novel calibration method that assumes angle-independent reflection. The effectiveness of the proposed calibration approach is demonstrated using NYURay. When comparing the directional multipath power predicted by NYURay to the actual measured power, the standard deviation in error was less than 3 dB in indoor office environments and less than 2 dB in outdoor and factory environments. The root mean square (RMS) delay spread and angular spread was underpredicted by NYURay due to incomplete environmental maps available for calibration, however an overall agreement between the measured and simulated values was observed. These results highlight the high level of accuracy NYURay provides in generating the site-specific real-world wireless channel, that could be used to generate synthetic data for machine learning.