实测验证：城市无线信道仿真需精准建模天线位置与方向。
On the Limitations of Ray-Tracing for Learning-Based RF Tasks in Urban Environments
- 通过调整天线位置和方向，显著提升仿真与实测信号强度的相关性。
- 优化后信道仿真相关性提升5%至130%，定位误差降低约三分之一。
- 现有仿真难以还原真实城市环境中的复杂干扰，限制了迁移应用。
本文研究Sionna v1.0.2在罗马市中心室外蜂窝链路中的仿真真实度。基于1,664个用户设备（UE）和6个基站（BS）的实测数据，固定位置下系统性地改变路径深度、反射/散射/折射开关、载波频率及天线高度、辐射图、方位角等参数。通过斯皮尔曼相关系数评估各基站的仿真保真度，并采用基于接收信号强度指示（RSSI）指纹的k近邻（kNN）定位算法进行验证。实验表明，求解器超参数对结果影响微乎其微；而天线位置与方向则起决定性作用。通过简单贪心优化，多个基站的斯皮尔曼相关性提升5%至130%，仅用仿真数据作为参考的kNN定位误差在真实样本上下降约三分之一，但仍为纯实测数据误差的两倍。因此，精确几何与可信天线模型虽必要，但无法完全还原城市残余噪声，高保真可迁移的室外射频仿真仍面临挑战。
原文摘要 · Abstract (English)
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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