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

ArXiv CS.AI2026-06-19NEWSen作者: Armen Manukyan, Hrant Khachatrian, Edvard Ghukasyan, Theofanis P. Raptis

详细信息

来源站点
ArXiv CS.AI
作者
Armen Manukyan, Hrant Khachatrian, Edvard Ghukasyan, Theofanis P. Raptis
文章类型
NEWS
语言
en
发布日期
2026-06-19

摘要

arXiv:2507.19653v2 Announce Type: replace-cross 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.