Problem
Research questions and friction points this paper is trying to address.
monostatic sensing
UAV
5G NR
ISAC
3D localization
Evaluation of gNB Monostatic Sensing for UAV Use Case
🤖 AI Summary
This work addresses the lack of reproducible end-to-end evaluation methodologies for monostatic sensing systems in 5G base stations tailored to unmanned aerial vehicle (UAV) scenarios. Building upon the 3GPP UMa-AV channel model, the study presents the first standards-compliant gNB monostatic sensing processing chain under Release 19, integrating 5G NR downlink CP-OFDM waveforms with positioning reference signals (PRS) to enable multi-target detection and three-dimensional localization. In UAV-centric evaluations, the proposed approach achieves a detection probability exceeding 70% with a false alarm rate below 5%, while 90% of correctly detected targets exhibit horizontal and vertical localization errors under 6 meters and 4 meters, respectively. The accompanying simulation platform, 5GNRad, has been open-sourced to facilitate reproducible benchmarking research.
📝 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
Innovation
Methods, ideas, or system contributions that make the work stand out.
Integrated Sensing and Communications (ISAC)
monostatic sensing
5G NR
UAV localization
end-to-end simulation
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