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Evaluation of gNB Monostatic Sensing for UAV Use Case

Steve Blandino, Neeraj Varshney, Jian Wang, Jack Chuang, Camillo Gentile, Nada Golmie

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

Evaluation of gNB Monostatic Sensing for UAV Use Case

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

Paper Structure

This paper contains 23 sections, 14 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. 5G NR Monostatic Radar
  3. 3GPP Release-19 ISAC Channel Model
  4. Sensing target model and RCS
  5. Target channel construction for monostatic sensing
  6. Monostatic background channel model
  7. Baseline Signal Processing Chain
  8. Channel estimation
  9. Per-RF-chain range processing
  10. Static clutter suppression
  11. Doppler processing and range--Doppler map
  12. CA-CFAR detection
  13. Angle estimation
  14. Beamspace FFT
  15. Bartlett scan
  16. ...and 8 more sections

Figures (5)

  • Figure 1: A TRP transmits and receives a CP-OFDM waveform, which propagates through the 3GPP channel model.
  • Figure 2: Detection performance versus CA-CFAR threshold: ROC ($P_d$ vs. $P_{FA}$) and distributions of true positives (TP) and false alarms (FP) across drops.
  • Figure 3: TP and FN versus range, elevation angle, and radial velocity.
  • Figure 4: Empirical CDF of 3D positioning error and velocity error for detected targets.
  • Figure 5: Left: Sensitivity to residual self-interference. Right: Sensitivity to CPI length.