3GPP Release 19 has initiated the standardization of integrated sensing and communications (ISAC), including a channel model for monostatic sensing, evaluation scenarios, and performance assessment methodologies. These common assumptions provide an important basis for ISAC evaluation, but reproducible end-to-end studies still require a transparent sensing implementation. This paper evaluates 5G New Radio (NR) base station (gNB)-based monostatic sensing for the Unmanned Aerial Vehicle (UAV) use case using a 5G NR downlink Cyclic Prefix-Orthogonal Frequency Division Multiplexing (CP-OFDM) waveform and positioning reference signals (PRS), following 3GPP Urban Macro-Aerial Vehicle (UMa-AV) scenario assumptions. We present an end-to-end processing chain for multi-target detection and 3D localization, achieving more than 70% detection probability with less than 5% false alarm rate, in the considered scenario. For correctly detected targets, localization errors are on the order of a few meters, with a 90th-percentile error of 4m and 6m in the vertical and horizontal directions, respectively. To support reproducible baseline studies and further research, we release the simulator 5GNRad, which reproduces our evaluation

翻译：3GPP Release 19启动了集成感知与通信（ISAC）的标准化工作，包括单站感知的通道模型、评估场景和性能评估方法论。这些通用假设为ISAC评估提供了重要基础，但可复现的端到端研究仍需透明的感知实现方案。本文利用5G NR下行链路循环前缀-正交频分复用（CP-OFDM）波形和定位参考信号（PRS），并遵循3GPP Urban Macro-Aerial Vehicle（UMa-AV）场景假设，评估了基于5G新空口（NR）基站（gNB）的单站感知在无人机（UAV）场景中的应用。我们提出了一套用于多目标检测与三维定位的端到端处理链，在所考虑场景下实现了超过70%的检测概率且虚警率低于5%。对于正确检测到的目标，定位误差在米量级，垂直方向和水平方向的90百分位误差分别为4米和6米。为支持可复现的基线研究和后续工作，我们开源了仿真器5GNRad，该工具可复现我们的评估结果。