On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments
This work addresses the challenge of achieving high-fidelity RF simulation for urban environments, which is crucial for reliable learning-based tasks, but it is incremental as it highlights limitations and partial improvements.
The study evaluated the realism of Sionna v1.0.2 ray-tracing for outdoor cellular links in Rome, finding that antenna locations and orientations significantly impact simulation fidelity, with optimization improving Spearman correlation by 5% to 130% and reducing kNN-based localization error by one-third compared to real-world samples.
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.