Evaluation of gNB Monostatic Sensing for UAV Use Case

TL;DR

This paper assesses 5G NR-based monostatic sensing for UAVs, demonstrating effective multi-target detection and localization with a new simulator to support reproducible research.

Contribution

It provides an end-to-end processing chain for UAV sensing using 5G NR signals and releases a simulator for reproducible baseline studies.

Findings

Over 70% detection probability with less than 5% false alarms.

Localization errors are within a few meters, with 90th-percentile errors of 4m vertically and 6m horizontally.

Abstract

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