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Full-Duplex OFDM Radar With LTE and 5G NR Waveforms: Challenges, Solutions, and Measurements

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Standard-compliant LTE and 5G NR downlink waveforms can serve as monostatic OFDM radar signals when unused subcarriers are interpolated and transmitter self-interference is actively cancelled.

desk verdict Solid full-duplex OFDM radar demonstration with real measurements, but the measured ROC is computed in a local window around the known target and does not support the system-level false-alarm claim. read the letter →

arxiv 1908.03418 v1 pith:2ZUIXELW submitted 2019-08-09 eess.SP

Carlos Baquero Barneto , Taneli Riihonen , Matias Turunen , Lauri Anttila , Marko Fleischer , Kari Stadius , Jussi Ryynänen , Mikko Valkama This is my paper · ORCID
classification eess.SP
keywords OFDMradar5GNewRadioLTEself-interferencecancellationfull-duplexjointcommunicationsandsensingrange-DopplerestimationRFconvergence
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

This paper argues that standard-compliant LTE and 5G NR downlink waveforms can be used as the transmit signal of a monostatic OFDM radar, so a base station can sense its surroundings with the very signal it is already broadcasting. The two obstacles are the null subcarriers in the LTE/NR resource grid and the self-interference that leaks from the transmitter into the shared receiver. The paper supplies an interpolation step for the empty subcarriers and a cascade of RF and digital cancellation for the self-interference, then validates the combination with RF measurements. If the claim holds, ordinary base stations gain a radar function without a separate radar waveform, enabling environmental sensing from the existing mobile network.

What carries the argument

The engine of the argument is subcarrier-domain radar processing with the channel-estimation-style ratio $\bar{G}^{\mathrm{CH}}_{p,q}=Y_{p,q}/X_{p,q}$ on the LTE/NR transmit-receive grid, followed by a two-stage IFFT/FFT periodogram that maps delay to range and Doppler to velocity. Because LTE and NR leave some passband subcarriers unused, the paper inserts linear interpolation along the OFDM-symbol axis at those frequency positions before forming the periodogram. The companion hardware machinery is a three-tap RF canceller, whose maximum 10 ns delay intentionally keeps true target echoes beyond about 1.5 m intact, and a digital canceller using memory-polynomial basis functions $|x(n)|^{p-1}x(n)$ with a self-orthogonalizing update rule that avoids explicit basis orthogonalization. Together these carry the argument by converting an unreadable self-interference-dominated image into one in which real targets appear.

What would settle it

Take a shared-antenna base-station front end in an environment with a strong reflector 5--10 m from the antenna, such as a mast, building edge, or vehicle, and measure the residual self-interference after the 10 ns three-tap RF canceller and 5+5-tap digital canceller; if a static target at that range is still masked by leakage sidelobes, or if the measured residual coupling exceeds the roughly 100 dB isolation shown here, the central claim fails for realistic multipath. A complementary check is to place a target at 2 m: if the canceller suppresses it along with the leakage, the minimum-detectable-range design trade-off is confirmed.

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Extended reading notes

Core claim

On the paper's own terms, the discovery is that the normal downlink transmission of an LTE or NR base station, processed in the frequency domain by element-wise division of the received grid by the known transmitted grid, yields a clean range-Doppler image once the gaps left by unused subcarriers are filled by linear interpolation across OFDM symbols. The transmitter's own leakage appears in that image as an extremely strong static target at zero range, and its sidelobes mask real static and slow-moving targets. The paper shows that a three-tap RF canceller with delays up to 10 ns, followed by a nonlinear memory-polynomial digital canceller with five precursor and five postcursor taps at 240 MHz, suppresses the leakage by about 100 dB in measurements while preserving echoes from targets beyond roughly 3 m. With this cancellation, a static drone at 40 m and three moving cars at 51--102 m are detected and their ranges and velocities estimated from a 40 MHz NR waveform at 2.44 GHz.

Load-bearing premise

The claim rests on the assumption that the direct transmitter-receiver coupling can be represented by an RF canceller with only three taps and delays up to 10 ns plus a 240 MHz digital canceller with five precursor and five postcursor taps, while echoes from targets closer than about 3 m are sacrificed; if real base-station coupling contains longer-delay multipath, or if the cancellers suppress close-range target returns, the demonstrated static-target detection will not transfer to practical deployments.

Editorial extensions

If this is right

  • At 100 MHz NR bandwidth with 30 kHz subcarrier spacing, the paper's simulations give distance resolution of about 1.5 m and reliable detection at input SNRs below $-30$ dB, implying that sub-6 GHz 5G base stations can support useful short-to-medium-range sensing without new spectrum.
  • Because the transmitter self-interference behaves as a zero-range static target, moving targets are detectable even with weaker isolation, whereas static targets require the full RF-plus-digital cancellation chain; this orders the practical deployment difficulty.
  • With the cancellers active, measured range profiles reveal building reflections at 120 m and 180 m, showing the dynamic range needed for urban sensing is attainable in a real front end.
  • The 10 ns RF canceller delay and 5+5 digital taps set a minimum detectable target distance around 3 m, an explicit design trade-off between cancelling close coupling and preserving close echoes.

Reading between the lines

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

  • If the same cancellation chain scales to the 24--40 GHz NR bands, where 400 MHz contiguous carriers are already standardized, the distance resolution would drop below 0.5 m, making micro-Doppler signatures such as the drone-propeller effect seen in the measurements a practical sensing feature rather than a curiosity.
  • A natural testable extension is to run the radar processing while an uplink signal is received inband; the cancellation chain would then need to separate the uplink user signal from the downlink echoes, which is a different interference geometry than the one measured.
  • The static-drone experiment already shows propeller micro-Doppler sidebands at the target's range, suggesting that the same waveform could support classification of targets by their micro-motion, though the paper does not pursue classification.
  • Deploying this in a live network would require the base station to know the transmitted resource grid exactly and to keep the radar receiver coherent with the transmitter; the measurements validate this on a controlled setup, so the main open engineering question is integration with real schedulers and MIMO antenna arrays.
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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 proposes and evaluates a monostatic OFDM radar concept that reuses standard-compliant LTE and 5G NR downlink transmissions. It formulates frequency-domain radar processing based on division by the transmit grid, introduces linear interpolation across OFDM symbols to handle null subcarriers, and derives target detection and range/velocity estimation via a periodogram. Simulation results for NR bandwidths of 20, 40, and 100 MHz show detection down to very low SNR and estimation RMSE approaching the resolution limits. The paper then addresses self-interference in shared-antenna operation, describing a three-tap RF canceller and a nonlinear digital canceller with a self-orthogonalizing update rule, and reports RF measurements at 2.4 GHz with a 40 MHz NR waveform: about 100 dB total isolation, detection of a static drone at 40 m, and detection of three moving vehicles with range/velocity estimates consistent with the scenario.

Significance. Assuming the central claims hold, this is a valuable end-to-end demonstration of joint communications and sensing using standard-compliant LTE/NR waveforms. The paper combines a standard frequency-domain OFDM radar processing model with a concrete solution to the self-interference problem: a three-tap RF canceller and a nonlinear digital canceller with self-orthogonalizing adaptation, measured to provide about 100 dB total TX-RX isolation. The simulations provide falsifiable predictions for detection and estimation performance across carrier bandwidths, and the outdoor measurements with a drone and vehicles are a rare hardware demonstration in this area. The radar-specific tailoring that preserves target echoes beyond a few meters is a clear distinction from generic full-duplex self-interference cancellation work. The paper is well written and the results are internally consistent; the most important weakness is the mismatch between the detector definition in Section II-B and the empirical ROC evaluation in Section IV-C.

major comments (3)
  1. [Section IV-C, Fig. 8] The measured ROC is not computed with the detector defined in Section II-B. In Section IV-C, the H0 and H1 empirical distributions are built from a 3x3 pixel window at and around the drone location, and the threshold is varied on those local distributions, whereas the detector in Eq. (6) operates on the full search space Omega_A and the text in Section II-B defines PFA,tot = 1 - (1-PFA)^{|Omega_A|}. Consequently, Fig. 8 reports a local per-cell trade-off, not a system-level ROC: a threshold with acceptable local PFA can still produce frequent false alarms elsewhere in the range-Doppler map, e.g., from SI sidelobes or the 10 m reflection visible in Fig. 7(d). The abstract's statement that the cancellation solutions are 'shown through RF measurements' to enable static-target detection at a specified PD/PFA is therefore not quantitatively substantiated. Please recompute the ROC over the full Omega_A or present map-level CFAR statistics over all range-Doppler bins, and adjust the claims accordingly.
  2. [Section II-C, Fig. 2] The simulation curves in Fig. 2 are presented without confidence intervals and without a stated number of Monte Carlo realizations. Because the detection probability and RMSE values are averaged over randomly drawn target distances and velocities (uniform over 20-200 m and -40 to 40 m/s), the reader cannot judge whether the sharp transitions in Fig. 2(a) or the RMSE floors in Fig. 2(b)-(c) are statistically stable. Please report the number of trials per SNR point and add confidence intervals or standard-error bars to the curves.
  3. [Section III-B and IV-B] The cancellation design assumes that the self-interference channel is sufficiently modeled by three RF taps with delays up to 10 ns (Eq. (9)) plus a 5+5 tap digital canceller at 240 MHz (Eq. (11)), and the paper argues that this preserves target echoes beyond roughly 1.5-3 m. The measurements validate this assumption for the particular anechoic-chamber and outdoor coupling environments, but the paper does not characterize the delay spread of the measured coupling channel nor test scenarios with longer-delay multipath in the SI path. Since a base-station deployment may have coupling reflections beyond 10 ns, the general claim in Section V that these cancellers are key ingredients for practical deployments is stronger than the evidence supports. I recommend either adding a coupling-channel delay-spread measurement or explicitly bounding the applicability of the cancellation parameterization.
minor comments (5)
  1. [Section II-C] The word 'probablity' should be 'probability' in the paragraph following Fig. 2.
  2. [Section IV-C] The ROC procedure is underspecified: please state whether the 100 H0 and 100 H1 measurements are independent, how the 3x3 pixel window is selected, and how the threshold sweep is mapped to PFA values. With only 100 images per hypothesis, confidence intervals on the ROC curves would also be informative.
  3. [Eq. (10)] The integral in Eq. (10) is written with dt over sampled I/Q signals; please clarify whether this is a discrete-time accumulation and how the integration window is chosen.
  4. [Eq. (12)] In Eq. (12), the correlation matrix C is said to be precomputed; please state whether regularization is used for the inversion and how C is estimated from the basis function samples.
  5. [Fig. 2 caption] The caption lists only NR cases; the text states that LTE 20 MHz is omitted because it is essentially identical, but the caption should also note this omission for clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the OFDM radar processing chain is self-contained, and the SI-cancellation performance is directly measured rather than derived from the claims it supports.

full rationale

The paper's derivation chain is not circular. The received-signal model in Eq. (1) is the standard OFDM radar input-output relation with attenuation, delay, and Doppler terms, and the channel-estimation-like preprocessing in Eq. (3) is an adopted known method from the OFDM radar literature; the paper does not claim that this model alone predicts the measurements. The interpolation in Eq. (4) is a design choice for filling null subcarriers, and its benefit is evaluated in simulations with randomly placed targets, not fitted to the same target positions. The self-interference cancellation claims are supported by direct measurements: the RF canceller adaptation in Eq. (10) and the digital canceller in Eqs. (11)-(12) are fitted to the TX-RX coupling signal, and the resulting suppression (Section IV-B) and target images (Figs. 7 and 9) are measured with real drone and car targets. Moreover, the targets are deliberately placed outside the canceller memory (about 3 m), so detection of a 40 m drone or 50-110 m cars does not reduce to the cancellation fit. The self-citations to prior full-duplex hardware works [47], [41], [49] are implementation references rather than load-bearing uniqueness theorems or ansatzes; the active cancellation gain is re-measured here. The local-window ROC in Fig. 8 is a validation limitation, because the empirical H0/H1 distributions are taken from a 3x3 pixel region around the drone whereas Eq. (6) defines PFA,tot over the whole search space, but this is an external-validity concern, not a circular reduction of a prediction to its input.

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

The central claims do not depend on any reported fitted constant; the false alarm rate, multipath settings, and canceller tap counts are stated design choices rather than fitted parameters. The main unproven premises are the adequacy of linear interpolation and the sufficiency of the low-order canceller model for real base-station self-interference, plus the point-target model of Eq. (1).

assumptions (4)
  • domain assumption Received grid equals attenuated, delayed, and Doppler-shifted copies of the transmitted grid (Eq. 1).
    Standard OFDM radar model, assumes point targets, constant delay and Doppler over the processing block, and coherent reception. Invoked in Section II-A.
  • domain assumption The transmitter's resource grid X is fully known to the radar processing.
    True for a monostatic base station that generates the downlink waveform; required for the channel-estimation-like division in Eq. (3). Invoked in Section II-A.
  • ad hoc to paper Linear interpolation across OFDM symbols reconstructs the channel response at null subcarriers (Eq. 4).
    Adopted without an analytic error bound; simulations show acceptable performance, but long runs of empty subcarriers or fast-moving targets could degrade it. Introduced in Section II-B.
  • ad hoc to paper Three-tap RF canceller plus 5+5 tap nonlinear digital canceller sufficiently models the SI channel while preserving echoes beyond roughly 1.5 to 3 m.
    This is the implementation premise behind the measured roughly 100 dB isolation and the static target detection; it is not shown to scale to all base-station coupling scenarios and transmit power levels. Introduced in Sections III-B and IV-B.

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Cite this review

Pith. "Pith review of Full-Duplex OFDM Radar With LTE and 5G NR Waveforms: Challenges, Solutions, and Measurements." pith.science (2026). https://pith.science/paper/2ZUIXELW

@misc{pith2026190803418,
  author       = {Pith},
  title        = {Pith review of: Full-Duplex OFDM Radar With LTE and 5G NR Waveforms: Challenges, Solutions, and Measurements},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2ZUIXELW}},
  note         = {Machine review of arXiv:1908.03418}
}
read the original abstract

This paper studies the processing principles, implementation challenges, and performance of OFDM-based radars, with particular focus on the fourth-generation Long-Term Evolution (LTE) and fifth-generation (5G) New Radio (NR) mobile networks' base stations and their utilization for radar/sensing purposes. First, we address the problem stemming from the unused subcarriers within the LTE and NR transmit signal passbands, and their impact on frequency-domain radar processing. Particularly, we formulate and adopt a computationally efficient interpolation approach to mitigate the effects of such empty subcarriers in the radar processing. We evaluate the target detection and the corresponding range and velocity estimation performance through computer simulations, and show that high-quality target detection as well as high-precision range and velocity estimation can be achieved. Especially 5G NR waveforms, through their impressive channel bandwidths and configurable subcarrier spacing, are shown to provide very good radar/sensing performance. Then, a fundamental implementation challenge of transmitter-receiver (TX-RX) isolation in OFDM radars is addressed, with specific emphasis on shared-antenna cases, where the TX-RX isolation challenges are the largest. It is confirmed that from the OFDM radar processing perspective, limited TX-RX isolation is primarily a concern in detection of static targets while moving targets are inherently more robust to transmitter self-interference. Properly tailored analog/RF and digital self-interference cancellation solutions for OFDM radars are also described and implemented, and shown through RF measurements to be key technical ingredients for practical deployments, particularly from static and slowly moving targets' point of view.

Figures

Figures reproduced from arXiv: 1908.03418 by the authors.

Figure 1
Figure 1. Block diagram of the considered OFDM radar building on regular LTE or NR downlink transmission and frequency-domain radar processing. All [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Single-target radar performance at 3.5 GHz as a function of receiver [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Example radar images in a scenario with three true targets located at 60 m, 90 m and 120 m distances and moving with relative velocities of 0 m/s, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: In (a), the RF canceller block-diagram and self-adaptive weight control [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: In (a), the main equipment used in the RF measurements are shown, [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Overall measured TX-RX isolation performance with 40 MHz NR [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Measured radar images with a static airborne drone at a distance of 40 m, (a) without any cancellation, (b) with RF cancellation only, and (c) [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Measured receiver operating characteristic (ROC) curves in the drone [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: (a) Vehicular measurement scenario with three moving cars [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.