Target Sensing Performance in Disaster-Specific ISAC Networks

Ahmet Burak Ozyurt; John S. Thompson

REVIEW 4 major objections 3 minor 14 references

Target Sensing Performance in Disaster-Specific ISAC Networks

T0 review · 4 major / 3 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper derives a closed-form metric, the dynamic ranging rate, that quantifies how continuously a disaster ISAC network can re-acquire a target as mobile response vehicles move through the disaster zone.

desk verdict The paper's central DRR formula has the wrong dependence on PRI, and it contradicts the manuscript's own conclusions. read the letter →

arxiv 2506.02828 v1 pith:52KQIAR2 submitted 2025-06-03 eess.SY cs.SY

Ahmet Burak Ozyurt , John S. Thompson This is my paper

classification eess.SYcs.SY

keywords integratedsensingandcommunicationdisasterresponsestochasticgeometryPoissonpointprocesscoveragedynamicrangingratecontinuitymobilitymodel

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

Disaster zones are too dynamic for static sensing assumptions, so the paper adds a mobile layer of disaster-response vehicles (DRVs) over a sparse base-station layer in an integrated sensing and communication (ISAC) network. The paper's aim is to quantify sensing service continuity with a closed-form dynamic ranging rate (DRR), which counts how often a target that a base station already detected re-enters a DRV's sensing area and stays there longer than the radar pulse repetition interval. Closed-form sensing footprints for a DRV are derived by approximating the equal-received-power boundary as a circle or an ellipse depending on path-loss exponents, and Monte Carlo simulations are reported to match the analytical DRR curves. If the derivation holds, planners can choose DRV density, vehicle speed, and pulse timing to keep targets visible to the network during rescue operations.

What carries the argument

The central object is the dynamic ranging rate (DRR), $\xi$, defined in Eq. (13) as the product of the dynamic ranging repetition rate $\xi_r$ and the probability $P(\kappa > \tau)$ that a target's dwell time inside a DRV sensing area exceeds the pulse repetition interval. It is carried by three pieces: the equal-received-power contour from Eq. (4), approximated as a circle when the path-loss exponents are equal and as an ellipse when they differ; an improved random waypoint mobility model that turns expected transition length and mean pause time into $\xi_r$; and the exponential dwell-time exceedance probability in Eq. (16).

What would settle it

A Monte Carlo simulation that directly counts, under the paper's Poisson point process and random-waypoint assumptions, the fraction of target-DRV encounters whose dwell time exceeds $\tau$ would settle the metric: if the empirical probability falls as $\tau$ rises while Eq. (16) rises, the DRR formula cannot be correct.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a closed-form dynamic ranging rate, Eq. (17): $$\xi = \frac{\sqrt{W}}{1-W} \cdot \frac{\lambda_v u}{\sqrt{\lambda_b} + 2\sqrt{\lambda_b \lambda_v}\, u\, E[T_s]} \cdot \exp\!\left(-\frac{4\lambda_b}{\pi}\frac{(1-W)^2}{W $u^{2}$ \$tau^{2}$}\right).$$ The paper claims this metric quantifies sensing service continuity, increases with $\lambda_v$ and $u$, decreases with $\tau$, and matches Monte Carlo simulations.

Load-bearing premise

The load-bearing premise is the un-derived exponential formula for the dwell-time probability $P(\kappa\ge\tau)$ in Eq. (16), which has the pulse repetition interval $\tau$ in the denominator and therefore approaches 1 as $\tau$ grows, the opposite of the paper's stated claim that larger $\tau$ degrades the dynamic ranging rate.

Editorial extensions

If this is right

If the DRR derivation is correct, increasing the density of DRVs raises sensing continuity, because more moving nodes create more chances to re-acquire a target.
If the DRR derivation is correct, increasing DRV speed raises the rate at which targets enter DRV sensing areas and therefore raises the DRR.
If the DRR derivation is correct, increasing the pulse repetition interval lowers the DRR, so PRI selection becomes a trade-off between detection range and continuity.
The reported simulations identify a PRI threshold near 0.1 s, beyond which DRR behavior becomes inconsistent and unreliable.
The circular and elliptical sensing-footprint approximations give closed-form coverage regions that can be evaluated without running a full spatial simulation.

Reading between the lines

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

One consequence the paper does not spell out: if Eq. (16) is corrected to be decreasing in $\tau$, the closed-form DRR in Eq. (17) would change shape and the numerical PRI threshold near 0.1 s would need re-estimation.
The same coverage-contour and DRR construction could be lifted to any layered ISAC setting with static infrastructure plus mobile nodes, such as vehicular or UAV-assisted sensing, because the stochastic geometry is not disaster-specific.
The equal-received-power contour approximations connect sensing coverage to handoff-style analysis: the DRV sensing boundary plays the role of a cell edge, so handover metrics from heterogeneous networks could be re-derived for sensing continuity.
A direct experimental check of the dwell-time exceedance probability from field traces of DRV trajectories would separate the mobility-model contribution from the dwell-time probability contribution in the DRR.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The DRV/ISAC problem is worth thinking about, and the paper frames it cleanly: two independent PPPs for BSs and mobile disaster-response vehicles, a simple RSP-based sensing area, and a continuity metric. That application is new as far as I know, and the Case 1 coverage expression with the MMSE-scaled radius is a reasonable first-order approximation.

But the core contribution collapses on inspection. The dwell-time probability in Eq. (16) is simply asserted, and as written it makes the DRR in Eq. (17) increase monotonically with the pulse repetition interval τ. The text, the claimed simulations, and the conclusions all say the opposite. That is not a typo somewhere in the margin; it is the very mechanism the paper claims to capture. A probability of the form P(κ > τ) has to decrease in τ, and the exponent here has τ in the denominator, so the expression is structurally wrong. Without a correct derivation of this probability, the closed-form DRR is unsupported.

The problems do not stop there. The Case 2 ellipse depends on a Taylor expansion at an unspecified point (i,j), so calling it a closed form is generous. The Monte Carlo validation is described only in a sentence; no simulation steps, number of realizations, or confidence intervals are given, so the claimed close match cannot be checked. The reference list looks appropriate and relevant.

So the architecture is plausible and the directions are interesting, but the main result does not hold as written. The internal contradiction with the paper's own conclusion is a load-bearing flaw, not a cosmetic one. I would send this to a serious referee only because the topic is timely and the modeling framework could be corrected and revisited. If the editor asked me, I would recommend major revision at best, and honestly a reject is defensible until the DRR derivation is fixed and the simulations are properly documented.

Referee Report

4 major / 3 minor

Summary. The paper proposes a stochastic-geometry model for disaster-specific ISAC networks in which base stations and mobile disaster response vehicles (DRVs) are modeled as independent Poisson point processes. It derives closed-form approximations for the DRV sensing area, introduces a new metric called the dynamic ranging rate (DRR) to quantify sensing service continuity, and claims Monte Carlo validation of the analytical expressions. The central result is Eq. (17), which expresses the DRR as a product of a dynamic ranging repetition rate and a dwell-time exceedance probability. The paper concludes that the DRR increases with DRV density and velocity and decreases with the pulse repetition interval.

Significance. The problem addressed is timely and practically relevant, and the use of stochastic geometry to obtain closed-form expressions for coverage and continuity in a mobile ISAC disaster scenario is a reasonable goal. The paper has the positive feature of attempting to quantify a service-continuity metric rather than only instantaneous coverage. However, the central claimed result, the closed-form DRR in Eq. (17), is not sound as written: the dwell-time probability in Eq. (16) has the wrong monotonicity in the pulse repetition interval, and no derivation is supplied for that expression. The Monte Carlo validation described in Section IV cannot support a formula whose behavior contradicts the paper's own stated trend. Since the DRR is the main novel contribution, the paper's central claim is not established.

major comments (4)

[Section III, Eq. (16)] The dwell-time exceedance probability is stated as P(κ ≥ τ) = exp(-4λb/π · (1-W)^2/(W u^2 τ^2)). For fixed W, λb, and u, this expression is an increasing function of τ and approaches 1 as τ → ∞. This violates the defining property of a survival function and directly contradicts the manuscript's claim in Section IV and the Conclusion that the DRR declines as τ increases. Since Eq. (17) multiplies the other factors by this term, the central DRR metric has the wrong monotonicity in the pulse repetition interval, and the claimed Monte Carlo validation in Figure 3 cannot hold for Eq. (17) as written.
[Section III, Eq. (16)] No derivation or citation supports this expression. The phrase 'by leveraging the stochastic geometry properties of distance distributions in a PPP' is not a derivation: no dwell-time distribution, no coverage-crossing event, and no PPP distance probability density function are given. This matters because Eq. (16) is the load-bearing factor that converts a repetition rate into a continuity probability. A corrected version must be derived from a mobility and coverage model and must be nonincreasing in τ.
[Section II-C, Eq. (8)] The second-order Taylor expansion of f(x,y) = (x^2+y^2)^αhat is taken at an unspecified point (i,j), and the resulting ellipse parameters in Eqs. (9)-(12) depend on that point. The manuscript never states how (i,j) is chosen, nor gives an error bound or sensitivity analysis. Consequently the Case-2 sensing area and any DRR values built on it are not uniquely determined by the model parameters.
[Section II-C, Eq. (6)] The claimed MMSE solution β = d_v^{2(αhat-1)} is not the minimizer of the stated squared-error integral. Minimizing ∫_0^{d_v} (r^{2αhat} - β r^2)^2 dr gives β = 5 d_v^{2αhat-2}/(2αhat+3), not d_v^{2(αhat-1)}. The discrepancy affects the sensing radius R_c in Eq. (7) and hence the DRR. The derivation needs correction or a different approximation criterion.

minor comments (3)

[Section III, Eq. (14)] The symbol L(X_{k-1}, X_k) is first defined as the number of dynamic ranging events obtained by counting intersections, but in the same equation |L(X_{k-1}, X_k)| is treated as a transition length with expected value 1/(2√λv). Using the same notation for two different quantities makes the derivation difficult to follow.
[Section IV, Figure 3] The text states that analytical results closely match simulation outcomes, but the figure does not show simulation markers and no Monte Carlo procedure is described. The number of network realizations, the mobility sampling method, and the definition of simulated DRR are all absent.
[Section II-C, Eq. (4)] The parameter W is introduced without a clear dimensional or notational explanation; it appears in several different algebraic forms in later equations, and the manuscript would benefit from a consistent definition before first use.

Circularity Check

1 steps flagged · score 2.0 of 10

No classic circularity, but the central DRR rests on an unproved and apparently inverted dwell-time survival formula; self-citations are present but not load-bearing.

other [Section III, Eq. (16) and Eq. (17)]
"Furthermore, by leveraging the stochastic geometry properties of distance distributions in a PPP, the probability that the target’s dwell time κ within the DRV sensing area exceeds the PRI τ is given by P (κ ≥ τ ) = exp{− (4λb/π) (1−W)^2 / (W u^2 τ^2)}."

Equation (17) is exactly Eq. (15) multiplied by this factor, so the DRR prediction is governed by it. The factor is presented as a stochastic-geometry result but has no derivation or citation. With τ in the denominator, P(κ≥τ)→1 as τ grows, contradicting both the physical meaning of a dwell-time survival probability and the paper's own claim that 'DRR declines as τ increases.' The intended derivation from Rc in Eq. (7) (dwell time κ=(π/2)Rc/u and Rc∝dv, with dv the nearest-BS distance in a PPP) would put τ² in the numerator. Hence the central closed-form result is not derived from the stated model; it is an asserted ansatz, and validation by Monte Carlo of the same model does not supply the missing derivation.

full rationale

The derivation of the DRR is largely a self-contained stochastic-geometry calculation: Eq. (14)–(15) follow from the RWP mobility model and PPP distance distributions, and Eq. (17) is the product of Eq. (15) and the asserted Eq. (16). No fitted parameter is later renamed as a prediction, and no external benchmark is claimed. The self-citations ([1], [10]) are used for standard ingredients (clutter modeling, RWP mobility, Taylor expansion) and are not load-bearing in the sense of importing the target result. The main concern is Eq. (16): it is the load-bearing probability for the novel metric, it is stated without derivation, and its τ-dependence appears inverted relative to the physical definition and to the paper's own stated trend. This is a missing-support/correctness issue rather than a reduction of the output to its inputs, so it does not make the paper circular; the circularity score is therefore low (2), reflecting the minor self-citations and the caveat that the central validation is internal to the model.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The model relies on standard stochastic geometry assumptions (independent PPPs, Rayleigh fading, Swerling RCS) and on two ad hoc approximations: the MMSE/linear approximation for the sensing boundary in Case 1, and the Taylor expansion at an unspecified point in Case 2. The dwell-time probability in Eq. (16) is an ad hoc assertion without derivation. No external data are needed if one accepts the modeling choices.

free parameters (1)

Taylor expansion point (i,j) = not specified
Used in Eq. (8b) to derive the sensing-area ellipse in Case 2; the resulting ellipse depends on this arbitrary choice, but the paper never defines how (i,j) is selected.

assumptions (5)

domain assumption Positions of BSs and DRVs are independent homogeneous Poisson point processes with intensities λb and λv.
Stated in Section II-A as the foundation of the stochastic geometry model.
domain assumption Received sensing power follows the radar equation with Rayleigh fading and path loss exponent αi, given in Eq. (1).
Used in Section II-B to define the sensing coverage boundary.
domain assumption Target RCS follows the Swerling type-1 model with exponential PDF in Eq. (2).
Invoked in Section II-B to model target fluctuations.
ad hoc to paper The function f(r) = r^(2α̂) can be approximated by β r² (Case 1) or by a second-order Taylor expansion at an unspecified point (i,j) (Case 2), without a global error bound.
Introduced in Section II-C to obtain closed-form coverage shapes; the approximation error is only locally characterized via MSE in Case 1, and the Taylor point is never specified in Case 2.
ad hoc to paper The dwell-time probability P(κ ≥ τ) has the closed form exp(-4λb/π · (1-W)²/(W u² τ²)) given in Eq. (16).
Stated in Section III without derivation; the reverse dependence on τ contradicts the paper's qualitative claims.

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Cite this review

Pith. "Pith review of Target Sensing Performance in Disaster-Specific ISAC Networks." pith.science (2026). https://pith.science/paper/52KQIAR2

@misc{pith2026250602828,
  author       = {Pith},
  title        = {Pith review of: Target Sensing Performance in Disaster-Specific ISAC Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/52KQIAR2}},
  note         = {Machine review of arXiv:2506.02828}
}

read the original abstract

As sixth-generation (6G) wireless technology emerges, integrated sensing and communication (ISAC) networks offer significant potential for enhancing real-time monitoring in disaster areas. However, existing ISAC approaches often fail to address the unique challenges of dynamic and cluttered disaster areas, resulting in limited sensing coverage and interruptions in sensing service. To address these limitations, this work proposes a mobile ISAC network specifically designed for disaster scenarios. By leveraging stochastic geometry, we derive closed-form expressions for sensing coverage and introduce a novel performance metric to evaluate sensing service continuity. Simulation results validate the analytical derivations and offer key insights into network design.

Figures

Figures reproduced from arXiv: 2506.02828 by the authors.

**Figure 2.** Disaster response vehicle (DRV) sensing coverage [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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Reference graph

Works this paper leans on

14 extracted references · 14 canonical work pages

[1]

Analysis of joint radar and communication in disaster scenarios,

A. B. Ozyurt, S. Mohalik, and J. S. Thompson, “Analysis of joint radar and communication in disaster scenarios,” in 2025 IEEE Wire- less Communications and Networking Conference (WCNC) , pp. 1–6

work page 2025
[2]

Integrated Sensing and Commun. (ISAC); Use Cases and Deployment Scenarios,

ETSI, “Integrated Sensing and Commun. (ISAC); Use Cases and Deployment Scenarios,” Tech. Rep. ETSI GR ISC 001 V1.1.1, Mar. 2025

work page 2025
[3]

Coverage and rate analysis for integrated sensing and communication networks,

X. Gan and et al., “Coverage and rate analysis for integrated sensing and communication networks,” IEEE J. Sel. Areas Commun. , vol. 42, no. 9, pp. 2213–2227, 2024

work page 2024
[4]

A stochastic geometry analysis for joint radar communication system in millimeter-wave band,

Y . Nabil and et al., “A stochastic geometry analysis for joint radar communication system in millimeter-wave band,” in ICC 2023 - IEEE International Conference on Communications , pp. 5849–5854, 2023

work page 2023
[5]

Joint coverage and detection performance metrics for integrated sensing and communication in automotive scenarios,

F. D. S. Moulin, C. Wiame, L. Vandendorpe, and C. Oestges, “Joint coverage and detection performance metrics for integrated sensing and communication in automotive scenarios,” IEEE Trans. V eh. Technol., vol. 73, no. 11, pp. 16758–16773, 2024

work page 2024
[6]

Performance analysis of joint radar and communication enabled ve- hicular ad hoc network,

H. Ma, Z. Wei, X. Chen, Z. Fang, Y . Liu, F. Ning, and Z. Feng, “Performance analysis of joint radar and communication enabled ve- hicular ad hoc network,” in 2019 IEEE/CIC International Conference on Communications in China (ICCC) , pp. 887–892, 2019

work page 2019
[7]

On the performance trade- off of distributed integrated sensing and communication networks,

X. Li, S. Guo, T. Li, X. Zou, and D. Li, “On the performance trade- off of distributed integrated sensing and communication networks,” IEEE Wirel. Commun. Lett. , vol. 12, no. 12, pp. 2033–2037, 2023

work page 2023
[8]

Deep cooperation in isac system,

Z. Wei and et al., “Deep cooperation in isac system,” IEEE Int. of Things Mag., vol. 7, no. 6, pp. 118–125, 2024

work page 2024

Show all 14 references

[9]

Study on 6G Use Cases and Service Require- ments: Stage 1 (Release 20),

3GPP TR 22.870, “Study on 6G Use Cases and Service Require- ments: Stage 1 (Release 20),” V0.2.1 (2025-03)

2025
[10]

Mobility management in multi-tier lifi networks,

A. B. Ozyurt and W. O. Popoola, “Mobility management in multi-tier lifi networks,” J. Opt. Commun. Netw. , vol. 13, no. 9, pp. 204–213, 2021

2021
[11]

B. R. Mahafza, Radar systems analysis and design using MATLAB . Chapman and Hall/CRC, 2022

2022
[12]

On the expanded region of picocells in heterogeneous networks,

D. Lopez-Perez, X. Chu, and I. Guvenc, “On the expanded region of picocells in heterogeneous networks,” IEEE J. Sel. Topics Signal Process., vol. 6, no. 3, pp. 281–294, 2012

2012
[13]

Modeling and analyzing the cross-tier handover in heterogeneous networks,

X. Xu, Z. Sun, X. Dai, T. Svensson, and X. Tao, “Modeling and analyzing the cross-tier handover in heterogeneous networks,” IEEE Trans. Wirel. Commun. , vol. 16, no. 12, pp. 7859–7869, 2017

2017
[14]

Tracking considerations in selection of radar waveform for range and range-rate measurements,

R. Niu, P. Willett, and Y . Bar-Shalom, “Tracking considerations in selection of radar waveform for range and range-rate measurements,” IEEE Trans. Aerosp. Electron. Syst. , vol. 38, no. 2, p. 467, 2002

2002

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

Analysis of joint radar and communication in disaster scenarios,

A. B. Ozyurt, S. Mohalik, and J. S. Thompson, “Analysis of joint radar and communication in disaster scenarios,” in 2025 IEEE Wire- less Communications and Networking Conference (WCNC) , pp. 1–6

work page 2025

[2] [2]

Integrated Sensing and Commun. (ISAC); Use Cases and Deployment Scenarios,

ETSI, “Integrated Sensing and Commun. (ISAC); Use Cases and Deployment Scenarios,” Tech. Rep. ETSI GR ISC 001 V1.1.1, Mar. 2025

work page 2025

[3] [3]

Coverage and rate analysis for integrated sensing and communication networks,

X. Gan and et al., “Coverage and rate analysis for integrated sensing and communication networks,” IEEE J. Sel. Areas Commun. , vol. 42, no. 9, pp. 2213–2227, 2024

work page 2024

[4] [4]

A stochastic geometry analysis for joint radar communication system in millimeter-wave band,

Y . Nabil and et al., “A stochastic geometry analysis for joint radar communication system in millimeter-wave band,” in ICC 2023 - IEEE International Conference on Communications , pp. 5849–5854, 2023

work page 2023

[5] [5]

Joint coverage and detection performance metrics for integrated sensing and communication in automotive scenarios,

F. D. S. Moulin, C. Wiame, L. Vandendorpe, and C. Oestges, “Joint coverage and detection performance metrics for integrated sensing and communication in automotive scenarios,” IEEE Trans. V eh. Technol., vol. 73, no. 11, pp. 16758–16773, 2024

work page 2024

[6] [6]

Performance analysis of joint radar and communication enabled ve- hicular ad hoc network,

H. Ma, Z. Wei, X. Chen, Z. Fang, Y . Liu, F. Ning, and Z. Feng, “Performance analysis of joint radar and communication enabled ve- hicular ad hoc network,” in 2019 IEEE/CIC International Conference on Communications in China (ICCC) , pp. 887–892, 2019

work page 2019

[7] [7]

On the performance trade- off of distributed integrated sensing and communication networks,

X. Li, S. Guo, T. Li, X. Zou, and D. Li, “On the performance trade- off of distributed integrated sensing and communication networks,” IEEE Wirel. Commun. Lett. , vol. 12, no. 12, pp. 2033–2037, 2023

work page 2023

[8] [8]

Deep cooperation in isac system,

Z. Wei and et al., “Deep cooperation in isac system,” IEEE Int. of Things Mag., vol. 7, no. 6, pp. 118–125, 2024

work page 2024