cs.AI updates on arXiv.org 07月29日 12:21
On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments
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本文研究了Sionna v1.0.2射线追踪在罗马城市户外蜂窝链路中的现实性,通过实际测量数据和不同参数模拟,探讨了路径深度、天线特性等对模拟精度的影响,并提出了优化方法。

arXiv:2507.19653v1 Announce Type: 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. 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.

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Sionna v1.0.2 射线追踪 城市环境 模拟精度 天线特性
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