On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments

2025-07-25

AI Summary

This study addresses the low fidelity of Sionna v1.0.2 in ray-tracing simulations of outdoor cellular links in Rome’s urban environment. We systematically evaluate the impact of geometric modeling, antenna radiation characteristics, and urban residual noise on RF simulation accuracy. A multi-parameter sensitivity analysis framework—incorporating path depth, scattering type, carrier frequency, antenna height, and radiation pattern—is proposed, grounded in real-world measurement data. A lightweight greedy optimization algorithm is designed to jointly calibrate antenna position and orientation. Results show Spearman correlation improvements of 5%–130% across base stations; kNN localization using purely simulated RSSI reduces error by ~33% in real deployments, yet remains 2.1× higher than measured baselines. The core contribution lies in identifying antenna pose as the dominant factor limiting simulation transferability, and in demonstrating that accurate modeling of urban residual noise remains a critical bottleneck for high-fidelity outdoor RF simulation.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

We study the realism of Sionna v1.0.2 ray-tracing for outdoor cellular links in central Rome. We use a real measurement set of 1,664 user-equipments (UEs) and six nominal base-station (BS) sites. Using these fixed positions we systematically vary the main simulation parameters, including path depth, diffuse/specular/refraction flags, carrier frequency, as well as antenna's properties like its altitude, radiation pattern, and orientation. Simulator fidelity is scored for each base station via Spearman correlation between measured and simulated powers, and by a fingerprint-based k-nearest-neighbor localization algorithm using RSSI-based fingerprints. Across all experiments, solver hyper-parameters are having immaterial effect on the chosen metrics. On the contrary, antenna locations and orientations prove decisive. By simple greedy optimization we improve the Spearman correlation by 5% to 130% for various base stations, while kNN-based localization error using only simulated data as reference points is decreased by one-third on real-world samples, while staying twice higher than the error with purely real data. Precise geometry and credible antenna models are therefore necessary but not sufficient; faithfully capturing the residual urban noise remains an open challenge for transferable, high-fidelity outdoor RF simulation.

Problem

• Evaluating realism of ray-tracing for urban RF tasks
• Assessing impact of antenna properties on simulation accuracy
• Improving localization error with optimized simulation parameters

Innovation

• Ray-tracing simulation with varied parameters
• Greedy optimization for improved correlation
• Fingerprint-based kNN localization algorithm