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REVIEW 5 major objections 4 minor 27 references

A Framework for Geometry-based Statistical Channel Modeling in ISAC Systems

T0 review · 5 major / 4 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read A hybrid extension of the standard TR38.901 channel model claims to preserve communication performance while adding bistatic sensing evaluation.

desk verdict Useful 3GPP-aligned ISAC channel modeling framework, but the 'full compatibility' claim rests on validation that is partly by construction and statistically thin. read the letter →

arxiv 2511.23201 v3 pith:HEPSDV2R submitted 2025-11-28 eess.SP

Ali Waqar Azim , Ahmad Bazzi , Theodore S. Rappaport , Marwa Chafii This is my paper · ORCID
classification eess.SP
keywords ISACgeometry-basedstochasticchannelmodelTR38.901bistaticsensingradarcross-sectionhybridclusteringspatiotemporalconsistency6G
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 aims to give 6G designers a single channel model that works for both communication and sensing. It splits the radio channel into a target channel (paths that bounce off the object being sensed) and a background channel (everything else), then extends the stochastic cluster model of the TR38.901 standard with deterministic clusters anchored to explicit 3D coordinates and absolute delays. The central claim is that this hybrid model reproduces TR38.901's communication metrics (bit-error rate, channel capacity) across urban macro, urban micro, and indoor factory scenarios while also producing realistic bistatic sensing metrics such as target range error and detection probability. If correct, it would let system-level ISAC simulations run without giving up standardization compatibility.

What carries the argument

The key machinery is hybrid stochastic-deterministic clustering: a subset of the standard stochastic clusters is converted into deterministic clusters by fixing their 3D coordinates from arrival angles and absolute delay using the law of cosines, then recomputing departure angles to restore consistency. This anchors a subset of clusters to explicit positions so delays and angles are physically coherent for sensing, while the remaining stochastic clusters preserve the statistical character of the standard. The target channel is then built by cascading Tx-target and target-Rx links with RCS-weighted path gains, and the background channel is the standard stochastic model; the two are summed wit

What would settle it

Run the proposed hybrid model and TR38.901 with identical scenario parameters over many random seeds and compare the empirical distributions of root-mean-square delay spread, angular spreads, and per-cluster powers; if the deterministic cluster conversion shifts these distributions beyond the standard's calibration variance, the communication-parity and statistical-fidelity claims would be contradicted.

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Extended reading notes

Core claim

The central claim is that a geometry-based statistical channel model can serve both communication and sensing by decomposing the ISAC channel into target and background components, and by converting a subset of stochastic clusters into deterministic ones whose 3D positions, delays, and departure angles are recomputed from geometry via the law of cosines. The authors derive channel coefficients for all four bistatic propagation cases (LoS or NLoS on each leg of the Tx-target and target-Rx links), including Doppler contributions from target and deterministic-cluster motion. They validate that the resulting model stays within roughly a decibel of standard TR38.901 bit-error-rate performance in

Load-bearing premise

The load-bearing premise is that converting a subset of stochastic TR38.901 clusters into deterministic clusters via the law of cosines and recomputed departure angles preserves the small-scale statistical properties (delay, angle, power distributions) that TR38.901 was calibrated to reproduce, and the paper only checks aggregate BER, which is insensitive to such distortions.

Editorial extensions

If this is right

  • If correct, ISAC system simulators can use one standardized model for both link-level communication metrics and sensing KPIs, avoiding separate ray-tracing and stochastic simulation chains.
  • The framework's support for all four 3GPP bistatic modes (TRP-TRP, TRP-UE, UE-TRP, UE-UE) would allow network-level sensing studies under a single modeling framework.
  • Because deterministic clusters carry explicit RCS and velocity parameters, the model can evaluate target-dependent sensing performance (range error, detection probability) for different target types and distances.
  • The observed BER parity in UMa, UMi, and InF suggests the model could serve as a drop-in TR38.901 replacement for communication simulations, preserving backward compatibility.
  • The measured-channel BER comparisons indicate the model tracks real indoor-factory channels within a few dB, supporting use in measurement-calibrated studies.

Reading between the lines

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

  • A natural testable extension would be to compare the delay spread, angular spread, and K-factor distributions of the hybrid model against TR38.901; aggregate BER alone is unlikely to reveal subtle distortions introduced by the deterministic cluster conversion.
  • If the parity result generalizes, the same hybrid-cluster idea could be applied to monostatic ISAC (target co-located with the transmitter) by treating the target channel as a dedicated deterministic cluster inside a unified background channel.
  • The framework assumes a known RCS per scattering point, but in practice RCS is angle- and frequency-dependent; integrating empirical RCS statistics, as the paper does for a UAV target, is likely essential for realistic sensing evaluation.
  • The joint identification problem the authors flag — labeling measured paths as target vs. background — is the practical bottleneck; synthetic CIRs from this model could directly support supervised learning approaches to that labeling task.
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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

5 major / 4 minor

Summary. The paper proposes a two-component GBSM for bistatic ISAC: a background channel (propagation not involving the sensing target) following TR38.901, and a target channel (Tx–target–Rx paths) characterized by target RCS, scattering points, and a hybrid mix of stochastic and deterministic clusters. A step-by-step generation procedure is given (general parameters, small-scale parameters, coefficients) for all four bistatic modes and the four Tx-target/target-Rx LoS/NLoS cases. The claims are that the model preserves communication performance parity with TR38.901 (BER, ergodic capacity) while enabling sensing evaluation (range error, ROC), supported by simulations in UMa/UMi/InF and by 26/28 GHz measurement-based BER comparisons.

Significance. If the model equations are correct and the validation is strengthened, the framework would be a useful, concrete input to 3GPP Release 19 ISAC channel modeling: it provides absolute-delay alignment, explicit target RCS/SP treatment, deterministic cluster mapping, and coverage of all bistatic modes. The measurement-based BER comparison is a real strength, as is the detailed step-by-step procedural structure. However, the central claim of 'full compatibility with TR38.901' is only as strong as the validation of the small-scale statistical properties, and the manuscript currently has several load-bearing equation-level issues that must be resolved before the results can be relied upon.

major comments (5)
  1. [Section III-C, following Eq. (5)] The tap delays for the concatenated target paths are written as differences. For example, τ1 = τ_{l,s} − τ_{n2,m2}, τ3 = τ_{n1,m1} − τ_{u,l}, and τ5 = τ_{n1,m1} − τ_{n2,m2}; similarly τ2, τ4, τ6 are differences of deterministic segment delays. A path that traverses the Tx-target link and then the target-Rx cluster link has a total delay equal to the sum of the segment delays, not their difference. As written, secondary target paths can have non-physical (negative or smaller-than-direct) delays, which directly corrupts the CIR tap placement and the subsequent range-error results. Unless these symbols are explicitly redefined as excess delays relative to some reference (which is not stated), this is a sign error that must be corrected and the sensing simulations re-run.
  2. [Section III-C, Eq. (6) and weight definitions] The K-factor weights for the mixed LoS/NLoS cases are internally inconsistent with the definitions of H_NLoS1–H_NLoS3. When the Tx-target link is LoS and the target-Rx link is NLoS, the text sets γ1=0, γ2=η1, γ3=0, γ4=η̃2; when the roles are reversed it sets γ3=η2, γ4=η̃1. But H_NLoS3 is defined as the case with NLoS on both links, so γ4 should be 0 in both mixed cases, not η̃2 or η̃1. This introduces an unphysical both-NLoS contribution whenever exactly one link is NLoS. The correct weights should follow the same product structure as the all-LoS case (e.g., γ2 = η1η̃2, γ3 = η̃1η2) with the absent combinations set to zero. Please correct the weight table or explicitly justify a different decomposition.
  3. [Section III-B, Step 7S.a; Section IV-A validation] The conversion of a subset of stochastic clusters into deterministic clusters fixes the 3D position from delay and one angle set and then recomputes the complementary angles. This enforces an exact delay-angle coupling that the original TR38.901 random draws do not possess. The paper does not statistically validate that delay spread, angular spreads, or delay-angle correlations of the remaining stochastic clusters and the overall channel are preserved. The BER and capacity comparisons in Figs. 4 and 7 are aggregate metrics that are largely insensitive to such cluster-level distortions, and since the background channel is TR38.901 by construction, equal-or-better BER is expected. Please provide quantitative comparisons of small-scale statistics (delay/angular spreads, MIMO eigenstructure, delay-angle joint distributions) or state explicitly that compatibility is claimed only for aggregate
  4. [Section IV-A, Figs. 4(a), 7] No Monte-Carlo counts, confidence intervals, or error bars are reported for any BER or capacity curve. The parity claim rests on small differences (e.g., 1 dB or less at BER=10^-3), and without uncertainty quantification these differences are not meaningful. Also, the comparison does not state whether total received channel power is normalized between the proposed ISAC channel and the TR38.901 baseline. Because the proposed model adds target paths and deterministic clusters, at the same transmit SNR the extra received energy alone can explain the marginal BER/capacity improvement. Please specify the power normalization and report the number of channel realizations/bits used.
  5. [Section IV-A.2, Fig. 4(b)] The measurement-based validation is limited to BER, and the text's generalization is not supported: the authors state that the measured channel exhibits 'marginally elevated BER' relative to simulation, but at 26 GHz/60° the measured curve requires 37.7 dB versus 40.75 dB for the simulated curve at BER=10^-3, i.e., the measured channel is better, not worse. No confidence intervals are given for the measured BER. Furthermore, the sensing claims (range error, ROC) are not corroborated by the measurements; the real-world comparison validates only the communication component. Please provide a more precise characterization of measurement uncertainty and either remove or qualify the sensing-related validation claim.
minor comments (4)
  1. [Section III-B, formula for |a_p1|] The law-of-cosines expression should be derived explicitly. The sign convention for â_p1 (from target to cluster vs. from cluster to target) and the definition of d in the denominator are easy to misread; a short derivation would remove ambiguity.
  2. [Throughout] Several typographical errors remain, e.g., 'require SNR pf46dB' in Section IV-A.1 and inconsistent use of 'AA'/'AD' vs. 'AoA'/'AoD'. A careful proofread is needed.
  3. [Section IV-A.2] The phrase 'measured ISAC channels' is imprecise: the measurements provide a measured CIR that is then used in an OFDM BER simulation. The degree to which the reported BER is a property of the measured channel should be clarified.
  4. [Table II and Section IV] The number of deterministic clusters (5) and their RCS (0.1 m^2) are chosen without sensitivity analysis. Since these are free parameters of the framework, their influence on both communication parity and sensing results should be discussed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: communication-parity claim is a sanity check of a superset model against an external standard, not a reduction of the prediction to its inputs.

full rationale

The paper is a model-construction paper rather than a derivation of a predictive law from first principles. The background channel is deliberately based on TR38.901, but the central claim is that adding a target channel—with deterministic clusters and target scattering—preserves communication performance. That claim is checked by simulation (BER and ergodic capacity) against the TR38.901 baseline, and the result is an empirical outcome rather than an identity: extra multipath components could in principle degrade or improve BER. The closest candidate for circularity is Step 7S.a, where stochastic clusters are converted to deterministic positions using the cluster delay and one angle set via the law of cosines. This is a construction step that imposes geometric consistency, not a prediction of a quantity that is then compared to the input; the paper does not claim to independently predict TR38.901 delay-angle statistics. Whether aggregate BER/capacity checks are sufficient to validate small-scale statistical preservation is a legitimate validation-strength concern, but it is not circularity. The RCS distribution used in the measurement comparison comes from the authors' prior empirical characterization [27]; this is an external measurement input, not a parameter fitted to the BER being predicted, so it does not make the simulated-vs-measured BER comparison circular. Sensing results (range error, ROC) are demonstrations of the framework's capability rather than claims of independent prediction. No uniqueness theorem or ansatz is imported from self-citations in a load-bearing way. Accordingly, the derivation chain is not circular.

Assumptions & free parameters 3 free parameters · 5 assumptions · 1 invented entities

The framework's central claim rests mostly on standard 3GPP procedures; the paper adds a target channel driven by the user-supplied RCS σ and hand-chosen deterministic-cluster parameters. The RCS statistics used in the measurement comparison come from the authors' own fitted paper [27].

free parameters (3)
  • Target RCS σ = 0.1–1 m² in simulations; log-normal A=-13.57 dBsm, B1=0 dB, B2=3.065 dB for UAV
    σ is the key input controlling target-channel amplitude (Eq. 4 Γ factors). The log-normal parameters were fitted to measurements in the authors' prior paper [27] and reused here for the simulated-vs-measured BER comparison.
  • RCS of deterministic clusters = 0.1 m²
    Set by hand in Table II for all simulations; no measurement or physical justification is provided.
  • Number of deterministic clusters and SPs = 5 deterministic clusters, 5 SPs per cluster, 5 SPs for target
    Arbitrary choices in Table II; sensitivity of results to these counts is not explored.
assumptions (5)
  • domain assumption The bistatic target path gain factorizes as √σ times the Tx-target and target-Rx link coefficients (Eq. 4 and Γ definitions).
    Radar-equation-style factorization assumes the target is a discrete scatterer with no interaction between incoming and outgoing links beyond σ; extended targets are approximated as sums of independent SPs. Standard in radar, but not derived here.
  • domain assumption TR38.901 stochastic cluster generation remains valid when one terminal is a sensing target rather than a BS or UE.
    Section III-B applies TR38.901 cluster generation to Tx-target and target-Rx links, although TR38.901 was calibrated for BS-UE links.
  • ad hoc to paper Converting a subset of stochastic clusters into deterministic clusters preserves the statistical distribution of the remaining stochastic clusters and the overall channel.
    Invoked in Section III-B (Step 7S.a); no statistical validation of delay, angle, or power distributions after the conversion is presented.
  • domain assumption The background and target channels are statistically independent apart from sharing scenario-level LSPs.
    Eq. (1) sums the target and background channels with no cross-correlation term; the paper notes co-propagation creates correlation but does not model it.
  • standard math Standard mathematical tools (law of cosines for cluster positioning, phase/Doppler formulas from TR38.901) are applied correctly.
    Used throughout Section III; no new mathematics is developed.
invented entities (1)
  • Deterministic clusters (Type I environmental objects)
    purpose: Provide spatiotemporally consistent scatterers with assigned RCS and velocity in the target channel to support sensing.
    A modeling construct: clusters are either user-placed or converted from stochastic clusters; no measurement is provided to show the resulting deterministic scatterers correspond to physical objects. It is a simulation device, not an unexplained physical entity.

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Cite this review

Pith. "Pith review of A Framework for Geometry-based Statistical Channel Modeling in ISAC Systems." pith.science (2026). https://pith.science/paper/HEPSDV2R

@misc{pith2026251123201,
  author       = {Pith},
  title        = {Pith review of: A Framework for Geometry-based Statistical Channel Modeling in ISAC Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HEPSDV2R}},
  note         = {Machine review of arXiv:2511.23201}
}
read the original abstract

This paper proposes a comprehensive framework for a {geometry-based statistical model for} integrated sensing and communication (ISAC) tailored for bistatic systems. Our dual-component model decomposes the ISAC channel into a target channel encompassing all multipath components produced by a sensing target {parameterized} by the target's radar cross-section and scattering points, and a background channel comprising all other propagation paths that do not interact with the sensing target. The framework extends TR38.901 via a hybrid clustering approach, integrating spatiotemporally consistent deterministic clusters with stochastic clusters to preserve channel reciprocity and absolute delay alignment for sensing parameter estimation. Extensive simulations across {urban macro, urban micro, and indoor factory} scenarios demonstrate that the model maintains communication performance parity with the standard TR38.901, validated through bit-error rate analysis obtained via simulated and measured ISAC channels and channel capacity assessment, while enabling sensing performance evaluation, such as target ranging error for localization and receiver operating characteristic curves for detection probability.

Figures

Figures reproduced from arXiv: 2511.23201 by the authors.

Figure 1
Figure 1. Propagation conditions for the ISAC channel. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Proposed bistatic GBSM ISAC CM framework. The processing steps for the background channel are assigned a "C" suffix and are represented by [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Spatial mapping of deterministic clusters. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: BER performance comparison: (a) proposed ISAC model versus [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 5
Figure 5. Figure 5: The layout of the InF measurement environment depicting the Tx-Rx [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]
Figure 7
Figure 7. Figure 7: Mean channel capacity comparison between the proposed ISAC model and the TR38.901 [3] baseline across a range of target RCS values for: (a) [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Target range estimation analysis as a function of SNR for a target with [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 9
Figure 9. Figure 9: ROC showing Pd versus Pfa for (a) UMa, (b) UMi, and (c) InF environments. Each subplot evaluates Tx-target-Rx distances of 27 m and 56 m across target RCS values of 0.1 m2 , 0.5 m2 , and 1 m2 . performance evaluation, (2) the development of a hybrid clustering methodol…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

27 extracted references · 1 linked inside Pith

  1. [1]

    Liu et al

    R. Liu et al. ’Beginning of the Journey Toward 6G: Vision and Framework’.IEEE Commun. Mag., 61(10):8–9, 2023

    2023

  2. [2]

    W. Yang, Y . Chen, N. Cardona, Y . Zhang, Z. Yu, M. Zhang, J. Li, Y . Chen, and P. Zhu. ’Integrated Sensing and Communication Channel Modeling and Measurements: Requirements and Methodologies Toward 6G Standardization’.IEEE Veh. Technol. Mag., 2024

    2024

  3. [3]

    ’Study on Channel Model for Frequencies from 0.5 to 100 GHz’

    3GPP. ’Study on Channel Model for Frequencies from 0.5 to 100 GHz’. 3GPP , TR38.901, V17.0.0, April 2022

    2022

  4. [4]

    ’Summary on ISAC channel modelling’

    3GPP TSG RAN WG1. ’Summary on ISAC channel modelling’. R1-2504945, May 2025. TSG RAN WG1 #121, Agenda item 9.7.2, Document for Discussion/Decision

    2025

  5. [5]

    Wymeersch, N

    H. Wymeersch, N. Tervo, S. Wänstedt, S. Saleh, J. Ahlendorf, O. Akgul, V . Tsekenis, S. Barmpounakis, L. Bai, M. Beale, et al. ’Cross- layer Integrated Sensing and Communication: A Joint Industrial and Academic Perspective’.arXiv preprint arXiv:2505.10933, 2025

    arXiv 2025

  6. [6]

    ’TR RAN; Spatial channel model for Multiple Input Multiple Output simulations’

    3GPP. ’TR RAN; Spatial channel model for Multiple Input Multiple Output simulations’. TR 25.996, Mar 2020. V16.0.0

    2020

  7. [7]

    ’TR RAN; Study on 3D channel model for LTE’

    3GPP. ’TR RAN; Study on 3D channel model for LTE’. TR 36.873, Oct 2017. V12.7.0

    2017

  8. [8]

    Nie et al

    S. Nie et al. ’72 GHz millimeter wave indoor measurements for wireless and backhaul communications’.2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), pages 2429–2433, 2013

    2013

Show all 27 references
  1. [9]

    Y . Chen, Z. Yu, J. He, J. Li, and G. Wang. ’A Scatterer-based Hybrid Channel Model for Integrated Sensing and Communications (ISAC)’. In IEEE PIMRC, pages 1–7, 2023

    2023

  2. [10]

    Yang et al

    R. Yang et al. ’A Novel 3D Non-stationary Localization-assisted ISAC Channel Model’. InIEEE WCNC, pages 1–6, 2023

    2023

  3. [11]

    Lou et al

    J. Lou et al. ’A Unified Channel Model for both Communication and Sensing in Integrated Sensing and Communication Systems’. InIEEE VTC, pages 1–6, 2023

    2023

  4. [12]

    C. Luo, A. Tang, F. Gao, J. Liu, and X. Wang. ’Channel Modeling Framework for Both Communications and Bistatic Sensing Under 3GPP Standard’.IEEE J. Sel. Areas Sens., 2024

    2024

  5. [13]

    Ye et al

    Q. Ye et al. ’A General Integrated Sensing and Communication Channel Model Combined with Scattering Clusters’.IEEE Trans. Veh. Technol., 2025

    2025

  6. [14]

    Y . Liu, J. Zhang, Y . Zhang, H. Gong, T. Jiang, and G. Liu. ’How to Extend 3D GBSM to Integrated Sensing and Communication Channel with Sharing Feature?’.IEEE Wireless Commun. Lett., 2024

    2024

  7. [15]

    Zhang, R

    Z. Zhang, R. He, B. Ai, M. Yang, X. Zhang, R. Chen, H. Zhang, and Z. Zhong. ’A Shared Multipath Components Evolution Model for Integrated Sensing and Communication Channels’.IEEE Antennas Wireless Propag. Lett., 22(12):2975–2978, 2023

    2023

  8. [16]

    Chen et al

    Y . Chen et al. ’Multi-scattering Centers Extraction and Modeling for ISAC channel Modeling’. InIEEE EuCAP, pages 1–5, 2024

    2024

  9. [17]

    Zhang, J

    J. Zhang, J. Wang, Y . Zhang, Y . Liu, Z. Chai, G. Liu, and T. Jiang. ’Integrated Sensing and Communication Channel: Measurements, Char- acteristics, and Modeling’.IEEE Commun. Mag., 62(6):98–104, 2023

    2023

  10. [18]

    Kanhere, S

    O. Kanhere, S. Ju, Y . Xing, and T. S. Rappaport. ’Map-Assisted Millimeter Wave Localization for Accurate Position Location’. InIEEE GLOBECOM, pages 1–6. IEEE, 2019

    2019

  11. [19]

    Bazzi et al

    A. Bazzi et al. ’ISAC Imaging by Channel State Information using Ray Tracing for Next Generation 6G’.IEEE J. Sel. Top. Electromagn., Antennas Propag., 2025

    2025

  12. [20]

    Poddar, S

    H. Poddar, S. Ju, D. Shakya, and T. S. Rappaport. ’A tutorial on NYUSIM: Sub-Terahertz and Millimeter-Wave Channel Simulator for 5G, 6G, and Beyond’.IEEE Communications Surveys & Tutorials, 26(2):824–857, 2023

    2023

  13. [21]

    Ying et al

    M. Ying et al. ’Multi-Stage Location Optimization Through Power Delay Profile Alignment Using Site-Specific Wireless Ray Tracing’. pages 1–6, Taipei, Taiwan, December 2025. IEEE

    2025

  14. [22]

    M. Ying, D. Shakya, P. Ma, G. Qian, and T. S. Rappaport. ’Site-Specific Location Calibration and Validation of Ray-Tracing Simulator NYURay at Upper Mid-Band Frequencies’.npj Wireless Technology, 2025. invited paper, to appear

    2025

  15. [23]

    ’Millimeter wave multi-beam antenna combining for 5G cellular link improvement in New York City’

    Shu Sun et al. ’Millimeter wave multi-beam antenna combining for 5G cellular link improvement in New York City’. In2014 IEEE International Conference on Communications (ICC), pages 5468–5473, 2014

    2014

  16. [24]

    Jaeckel et al

    S. Jaeckel et al. ’QuaDRiGa: A 3-D Multi-cell Channel Model with Time Evolution for Enabling Virtual Field Trials’.IEEE Trans. Antennas Propag., 62(6):3242–3256, 2014

    2014

  17. [25]

    Naoumi, A

    S. Naoumi, A. Bazzi, R. Bomfin, and M. Chafii. High-Resolution Sens- ing in Communication-Centric ISAC: Deep Learning and Parametric Methods.arXiv preprint arXiv:2509.02137, 2025

    2025

  18. [26]

    Bomfin and M

    R. Bomfin and M. Chafii. ’On the Performance Analysis of Zero- Padding OFDM for Monostatic ISAC Systems’.IEEE Trans. Commun., 73(3):2103–2117, 2025

    2025

  19. [27]

    A. W. Azim, A. Bazzi, R. Bomfin, and M. Chafii. ’3GPP-Compliant Radar Cross Section Characterization of Indoor Factory Targets’. In IEEE GLOBECOM, 2025. Accepted for publication

    2025

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Reviewed August 3, 2026 · model on record in the stance chip above.