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REVIEW 3 major objections 5 minor 1 cited by

Detecting Airborne Objects with 5G NR Radars

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A single 5G New Radio base station can detect and localize small drones using the standard Positioning Reference Signals already in the system, with 3D position errors within 4 meters in dense urban microcells and within 8 meters in…

desk verdict Useful simulation framework with real release value, but the headline detection rates are uninterpretable because the target's Doppler/velocity is never stated and the clutter-suppression step cancels slow-moving UAVs. read the letter →

arxiv 2505.24763 v1 pith:2DSB6XP5 submitted 2025-05-30 eess.SP cs.NI

Steve Blandino , Nada Golmie , Anirudha Sahoo , Thao Nguyen , Tanguy Ropitault , David Griffith , Amala Sonny This is my paper
classification eess.SPcs.NI
keywords 5GNewRadioPositioningReferenceSignalsintegratedsensingandcommunicationdronedetectionUAVlocalizationOFDMradarcluttersuppressionurbanpropagation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that a standard 5G New Radio base station can act as a radar for detecting and locating small drones, using the Positioning Reference Signals (PRS) already defined in the 5G standard rather than a dedicated radar waveform. A complete processing chain is simulated—background clutter removal, angle-of-arrival search, range estimation, and 3D position reconstruction—and tested in standard urban microcell and macrocell propagation environments. The central finding is that performance depends strongly on deployment: dense urban microcells miss up to about 16% of drones at 25 m altitude, dropping to about 3% at 50 m and negligible above 100 m, while macrocell deployments miss almost nothing below 100 m and only about 1% at 200 m. When a detection is made, the 3D position error stays within about 4 m in the microcell and 8 m in the macrocell case. This matters because, if true, the existing cellular network could supply a low-cost layer of airspace monitoring without new spectrum or dedicated radar hardware.

What carries the argument

The central mechanism is the PRS comb resource grid treated as an OFDM radar waveform. PRS occupies every $K$-th subcarrier, so the phase shift across the active tones encodes the round-trip delay, and the repeated PRS occasions provide the repeated time samples needed for Doppler and for background removal. The processing chain targets three operations: clutter suppression by subtracting the empirical mean over PRS symbols, which relies on the static-environment assumption; angle-of-arrival estimation by a beam-sweeping search over the analog beamforming codebook; and range estimation by an IDFT over the active subcarriers. Detection is decided by comparing the peak-to-average ratio of the range profile to a threshold, and the final 3D position is computed from the angle and range estimates.

What would settle it

Run the open-source simulator with many more background scatterers than the three used in the baseline, such as ten or twenty reference points or one moving scatterer, and observe the 25 m urban-microcell missed-detection rate: if it stays near 16% and position error near 4 m, the central claim is robust; if it rises sharply, the reported performance is an artifact of the simulated clutter. An outdoor 30 GHz monostatic test with a small drone at 25 m in a dense urban canyon and a ground-truth tracker would settle the same question directly.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that standardized PRS pilot symbols—periodic comb-pattern signals already transmitted by 5G base stations—carry enough phase and delay information for a single base station to act as a monostatic radar, meaning one site that both transmits and receives, for a small drone. The base station transmits PRS, receives the round-trip echo, subtracts the time-averaged received signal to remove static urban clutter, sweeps a beam codebook to find the drone's azimuth and elevation, estimates range from the phase progression across active subcarriers via an IDFT, and declares a detection when the range profile's peak-to-average ratio exceeds a threshold. In the most difficult tested configuration, a dense urban microcell with the drone at 25 m altitude, the missed-detection probability reaches about 16%, falling to about 3% at 50 m and negligible at 100 m and above. In the urban macrocell configuration, missed detections are negligible below 100 m and about 1% at 200 m. Position error grows with target distance, staying within about 4 m in the microcell and within 8 m in the macrocell across the tested altitudes.

Load-bearing premise

The whole result rests on the assumption that the urban background is static enough that subtracting the time-averaged received signal removes it completely, and that the simulated three-point clutter scene stands in for a real city.

Editorial extensions

If this is right

  • Existing 5G base stations could act as a complementary drone-detection layer in cities, using only standard PRS resources and no extra spectrum or radar transmitter.
  • The best deployment for close, low-altitude targets is a dense urban microcell, where position error stays near 4 m, but it pays for that accuracy with a 16% missed-detection rate at 25 m altitude.
  • The best deployment for reliable detection is a macrocell, where missed detections are negligible below 100 m but position error grows to about 8 m as target distance increases.
  • The 3.4 dB peak-to-average detection threshold gives a single operating point with a specific false-alarm and detection trade-off across all tested scenarios and altitudes.
  • Because the range estimate comes from a fixed-width IDFT over the active PRS subcarriers, the same chain can be reconfigured by changing the comb spacing $K$ and the PRS occasion length, trading range ambiguity for detection quality.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The reported numbers are bounded by a simulated clutter scene made of only three synthetic reference points; a real urban deployment would likely see denser and partially moving clutter, so the 16% microcell miss rate and the 4 m/8 m errors are best-case estimates rather than guarantees.
  • The same PRS-based processing could be tried at lower frequencies or with wider aggregated bandwidths, which would change range resolution and clutter behavior in ways the paper does not test.
  • The single-target peak detector would need to become a multi-peak resolver before the chain can track several drones, since the current angle and range estimators pick only the strongest reflection.
  • A cooperative version with several base stations sharing angle and range estimates would likely mitigate the low-altitude microcell gap and the long-distance macrocell error, because the different viewing geometries would help disambiguate non-line-of-sight paths.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper studies whether a single 5G NR base station can use standardized Positioning Reference Signals (PRS) as a monostatic OFDM radar to detect and localize UAVs. It builds a complete processing chain: channel and clutter modeling, angle-of-arrival estimation with temporal mean subtraction, effective channel estimation, range estimation via IDFT, peak-to-average detection, and 3D position reconstruction. Simulation results are reported for 3GPP UMi-AV and UMa-AV scenarios at 30 GHz for UAV altitudes of 25, 50, 100, and 200 m, claiming missed-detection rates up to about 16% in UMi at 25 m, negligible misses in UMi above 100 m, near-zero misses in UMa below 100 m, and position errors within about 4 m in UMi and 8 m in UMa. The authors have released the simulation platform as open-source software.

Significance. If the results hold, the paper would provide a useful feasibility data point for using existing 5G NR infrastructure for passive or cooperative sensing of aerial targets, which is relevant to ISAC standardization and to practical drone-detection applications. The paper is strongest where it is explicit and reproducible: it follows established OFDM radar processing, uses 3GPP TR 36.777 and TR 38.901 models, adopts a 3GPP-motivated UAV RCS value, and releases the code, which are genuine strengths. The central feasibility claim, however, is currently built on a simulation whose target Doppler is unspecified and whose clutter environment is extremely sparse; these omissions directly affect the validity of the headline detection numbers.

major comments (3)
  1. [Section III-A, Eq. (9); Section IV-A] The clutter-suppression step subtracts the empirical mean over the symbol index l: eH = H - E_l[H]. For the target model in Eq. (7), hST_q,l = a_q exp(j2π f_D,q l), this subtraction leaves a residual that vanishes as f_D,q approaches zero and exactly cancels a zero-Doppler target. The simulation setup in Section IV-A specifies LPRS=4 symbols per occasion and NPRS=256 occasions, but it does not state the UAV radial velocity or the Doppler shift f_D used in the simulations, nor whether l in Eq. (9) runs over the four symbols within an occasion, over all 256 occasions, or both. At 30 GHz, a 10 m/s radial velocity gives f_D ≈ 2 kHz, which corresponds to only about 0.4 rad of phase advance across four 120 kHz-subcarrier OFDM symbols; under Eq. (9), most of the target energy is then removed together with the static clutter. The reported miss rates in Fig. 5a are therefore uninterpretable without knowing the assumed target Doppler, and the paper's feasibility claim does not currently cover hovering or slow-moving UAVs, which are a primary use case for drone detection.
  2. [Section IV-B, Fig. 5] The detection threshold η = 3.4 dB is selected from the same 64,000-sample simulated dataset that is then used to report the miss-detection probabilities in Fig. 5a. The reported values (about 16% in UMi at 25 m, about 3% at 50 m, about 1% in UMa at 200 m) are in-sample operating points evaluated at a threshold tuned on the same draws. The paper should provide receiver operating characteristics or PAR detection/false-alarm curves and demonstrate that the qualitative conclusions are stable under reasonable threshold variation, or use a separate calibration set and a test set. Without that, the absolute miss-rate numbers are not robust evidence for the feasibility claim.
  3. [Section II-B, Eq. (5); Section IV-A] The background clutter is generated from only NRP=3 random reference points, sampled from a scenario-dependent distribution and combined as in Eq. (5). This is a very sparse model of an urban scattering environment. The paper attributes the higher UMi miss rates to 'severe clutter,' but with only three reference points the clutter statistics are unlikely to represent dense urban environments, and no sensitivity analysis with respect to NRP or the RP density is provided. The conclusion that the system 'demonstrates feasibility in realistic urban propagation environments' is therefore not yet supported; the authors should either justify the choice of NRP=3 or show that the detection and positioning results are robust to the number and placement of clutter reference points.
minor comments (5)
  1. [Abstract] The sentence '5G NR radars exhibits the highest missed detection rate' has a subject-verb agreement error ('radars exhibits' should be 'radars exhibit').
  2. [Section II-B, Eq. (6)] The phrase 'σM,q is a the mean RCS value' contains a typo ('a the'); it should read 'is the mean RCS value'.
  3. [Section III-C, Eq. (12) and Eq. (15)] The symbol index l is dropped in the PAR definition of Eq. (15); the authors should clarify whether the detection statistic is computed per PRS symbol, averaged over the LPRS symbols in an occasion, or averaged over the NPRS occasions, since this affects the interpretation of the detection probabilities.
  4. [Section IV-A, Table I] The table entry for UMa-AV LOS probability is shown as '+' with no explanation; the authors should spell out the LoS probability formula for UMa-AV or refer explicitly to the relevant TR 36.777 table.
  5. [Fig. 3] The caption says '5G NR Radar operating curves,' but the two panes plot PFA versus η and PD versus η; a conventional operating curve would show PD versus PFA as η varies. The authors should relabel the figure or adjust the caption to avoid confusion.

Circularity Check

1 steps flagged · score 3.0 of 10

The reported miss-detection rates use a PAR threshold chosen from the same 64,000-sample ROC dataset, so the operating point is in-sample rather than independently predicted; the rest of the chain is externally grounded.

  1. other [Section IV-B, 'Detection and Position Error Performance']
    "To determine a suitable detection PAR threshold η, we simulate the detection behavior across all considered configurations. Specifically, we run simulations for each of the 8 combinations of deployment scenario (UMi-AV, UMa-AV) and UAV altitude (25 m, 50 m, 100 m, 200 m), both in the presence and absence of a target, resulting in a total of 64 000 samples. ... A detection threshold of η = 3.4 dB provides a good trade-off between high detection rates and low false alarm probabilities across all tested configurations and is adopted for the subsequent evaluation."

    The detection threshold η = 3.4 dB is selected after computing false-alarm and detection probabilities from the same 64,000 simulated samples that then generate the paper's reported miss-detection statistics. There is no hold-out split, no independent threshold calibration, and no analytic prediction of the operating point; the 'subsequent evaluation' is therefore an in-sample report at a threshold chosen to look good on those very samples. The headline values (about 16% UMi miss rate at 25 m, about 1% UMa miss rate at 200 m) are empirical frequencies at this post-hoc operating point, so part of the claimed feasibility result is a tuned description of the calibration data rather than an out-of-sample prediction.

full rationale

The central simulation chain is not circular: the received-signal model in Eqs. (1)-(3) and (8), the 3GPP TR 38.901 and TR 36.777 channel and path-loss models, the RCS value, and the standard PRS mapping are all external inputs, and the range and angle reconstruction in Eqs. (10)-(14) follows by standard beamforming and DFT operations from those inputs. No uniqueness theorem is imported from the authors' prior work, and the only self-references ([1], [3], [13]) are a survey, a prior resolution-characterization study, and the open-source software release; none of them carries the detection claim. The main circularity-adjacent defect is in Section IV-B: η = 3.4 dB is tuned on the same 64,000-sample ROC dataset that subsequently yields the reported miss rates, so the operating point is post hoc and the miss-rate numbers are in-sample rather than independently predicted. A separate correctness risk, not a circularity, is that Eq. (9)'s temporal-mean subtraction cancels a zero- or low-Doppler target exactly as it cancels static clutter, and the simulation setup never states the UAV velocity or Doppler; this affects validity but does not make the derivation equivalent to its inputs. Overall circularity is therefore mild and confined to threshold and evaluation leakage.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central simulation results depend on several hand-set parameters and domain assumptions. The main free parameters are the detection threshold and the number of background reference points; the main axioms are the stationarity of clutter, the point-scatterer RCS model, and the validity of the selected 3GPP channel models for aerial NR at 30 GHz. No new physical entities are introduced.

free parameters (2)
  • Detection threshold eta = 3.4 dB
    Chosen in Section IV-B as a trade-off from ROC curves computed on the same 64,000 simulated samples (target present/absent) that are then used to report missed-detection rates.
  • Number of background reference points NRP = 3
    Set in Section IV-A without sensitivity analysis; the clutter severity and UMi missed-detection numbers depend on this arbitrary choice.
assumptions (4)
  • domain assumption Static background clutter is perfectly stationary across PRS symbols and is fully removed by subtracting the empirical mean (Eq. 9)
    Section III-A assumes 'static clutter components are temporally stationary' across the PRS symbols; real urban clutter includes moving scatterers, and residual clutter leakage is only treated approximately.
  • domain assumption The UAV is a point scatterer with fixed mean RCS sigma_M = -12.81 dBsm (Eq. 6)
    Section II-B and IV-A; no RCS fluctuation model, micro-Doppler, or target extension; detection and position results are for this idealized target.
  • domain assumption 3GPP TR 38.901 and TR 36.777 path loss, LoS, and shadow-fading models correctly represent UMi and UMa channels at 30 GHz for aerial users
    Section II-B and IV-A; results inherit any biases in these models; TR 36.777 is an LTE-era aerial model being applied to NR.
  • domain assumption Monostatic AoA equals AoD and the array is perfectly calibrated
    Section III-A states 'AoA and AoD are the same'; no array calibration error or mutual coupling is considered.

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Cite this review

Pith. "Pith review of Detecting Airborne Objects with 5G NR Radars." pith.science (2026). https://pith.science/paper/2DSB6XP5

@misc{pith2026250524763,
  author       = {Pith},
  title        = {Pith review of: Detecting Airborne Objects with 5G NR Radars},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2DSB6XP5}},
  note         = {Machine review of arXiv:2505.24763}
}
read the original abstract

The integration of sensing capabilities into 5G New Radio (5G NR) networks offers an opportunity to enable the detection of airborne objects without the need for dedicated radars. This paper investigates the feasibility of using standardized Positioning Reference Signals (PRS) to detect UAVs in Urban Micro (UMi) and Urban Macro (UMa) propagation environments. A full 5G NR radar processing chain is implemented, including clutter suppression, angle and range estimation, and 3D position reconstruction. Simulation results show that performance strongly depends on the propagation environment. 5G NR radars exhibit the highest missed detection rate, up to 16%, in UMi, due to severe clutter. Positioning error increases with target distance, resulting in larger errors in UMa scenarios and at higher UAV altitudes. In particular, the system achieves a position error within 4m in the UMi environment and within 8m in UMa. The simulation platform has been released as open-source software to support reproducible research in integrated sensing and communication (ISAC) systems.

Figures

Figures reproduced from arXiv: 2505.24763 by the authors.

Figure 2
Figure 2. Example PRS resource grid structure, highlighting comb subcarrier [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. 5G NR Radar operating curves. conditions are generated: the LoS or NLoS condition is determined probabilistically, followed by the computation of the path loss and application of SF. The ST RCS is set to σM = −12.81 Decibels relative to a square meter (dBsm), reflecting values proposed in ongoing 3GPP standardization efforts for small UAVs. The background channel has been realized by dropping NRP = 3 RPs. B. Detecti… view at source ↗
Figure 4
Figure 4. Each subplot corresponds to a specific UAV altitude [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Detection and positioning performance across two deployment scenarios (UMi, UMa) at different UAV altitudes (25 m, 50 m, 100 m, 200 m). [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Statistics of detection and position error in UMi-AV and UMa-AV [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

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Reference graph

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