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REVIEW 4 major objections 6 minor 1 cited by

Dual-UAV-Aided Covert Communications for Air-to-Ground ISAC Networks

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Co-designing two drone trajectories and beamformers, with a jammer's artificial noise reused as a bistatic sensing waveform, maximizes average covert rate and sensing performance in an air-to-ground integrated sensing and communication…

desk verdict A genuinely new dual-UAV covert ISAC concept with a coherent model, but the SCA trajectory update has a missing-factor gradient error that undercuts the main simulation claims until fixed. read the letter →

arxiv 2506.00601 v1 pith:SBFTBIOE submitted 2025-05-31 cs.IT eess.SPmath.IT

Jingke Sun , Liang Yang , Alexandros-Apostolos A. Boulogeorgos , Theodoros A. Tsiftsis , Hongwu Liu This is my paper
classification cs.ITeess.SPmath.IT
keywords covertcommunicationsintegratedsensingandcommunicationUAVtrajectoryoptimizationcooperativejamminghybridmonostatic-bistaticradarbeampatterngainsuccessiveinterferencecancellationblockcoordinatedescent
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 proposes a covert air-to-ground integrated sensing and communication network with two drones: one source UAV carries the legitimate link and radar, while a second jamming UAV flies as a cooperative jammer. The paper's central claim is that the jammer's artificial noise can do double duty—confuse the ground warden's detector and illuminate ground targets for the source UAV's hybrid monostatic-bistatic radar—so that jointly optimizing both drones' trajectories and beamformers yields a higher average covert rate and better sensing than single-drone or fly-hover-fly designs. The authors formulate a non-convex average covert rate maximization problem under covertness, sensing, power, and mobility constraints, and solve it by block coordinate descent with trust-region successive convex approximation and semidefinite relaxation. If the claim holds, cooperative jamming in this setting need not cost sensing capability: the same waveform that hides the communication also feeds the radar.

What carries the argument

The load-bearing object is the distance-normalized beampattern sum-gain, which measures the sensing power arriving at each ground target from both the source UAV and the jamming UAV, normalized by squared slant distances. This quantity couples the jamming beamformer into the sensing constraint and turns the jammer's artificial noise into a sensing resource. The covertness requirement is expressed through a Kullback-Leibler divergence bound derived from the warden's Neyman-Pearson detector, rewritten as a linear inequality in the source beamformer, jamming beamformer, and sensing covariance. The optimization is carried by block coordinate descent over the source trajectory, jammer trajectory, and beamformers, with trust-region successive convex approximation handling the non-concave trajectory subproblems and semidefinite relaxation with a nuclear-norm penalty enforcing the rank-one beamformer structure. Sensing-location selection is a greedy weighted-distance minimization that chooses time slots where the drones are close to both the user and the targets.

What would settle it

Run Algorithm 1 on a small instance, say ten time slots and two targets, from many random initial trajectories and check whether the surrogate value at each iteration bounds the true objective and whether all runs converge to the same average covert rate; alternatively, evaluate the inequalities in Eqs. (37) and (42) on a grid of UAV positions to find any point where the alleged lower bound exceeds the true rate or the alleged upper bound falls below the true covertness term.

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Extended reading notes

Core claim

The paper asserts that a dual-UAV configuration, where the source UAV transmits a dual-functional waveform and the jamming UAV transmits artificial noise, can enhance covertness and sensing simultaneously. The artificial noise is included in the sensing beampattern gain, effectively forming a hybrid monostatic-bistatic radar in which the source UAV receives both its own target-echoed waveform and the jammer's scattered noise. The design maximizes the time-averaged covert rate from the source UAV to the ground user while keeping the warden's minimum detection error probability above a required level and the distance-normalized beampattern sum-gain toward each target above a sensing threshold. Imperfect successive interference cancellation at the user is modeled through residual interference levels, which the simulations show degrade the covert rate. The numerical results indicate that the co-designed scheme achieves the highest average covert rate and sensing gain among the compared schemes, and that these gains increase with the number of antennas and with a looser covertness requirement.

Load-bearing premise

The trust-region successive convex approximation assumes that the first-order Taylor expansions in Eqs. (37) and (42) are valid lower and upper bounds for the rate and covertness functions of the UAV positions, but the paper does not establish the needed convexity or concavity; if those bounds fail, the optimized trajectories may not increase the average covert rate and Algorithm 1 may not converge.

Editorial extensions

If this is right

  • Covert integrated sensing and communication can use a second UAV as a jammer without sacrificing sensing, because the artificial noise wave is also a radar illumination signal.
  • Jointly optimizing trajectories and beamformers outperforms fly-hover-fly trajectories even when those trajectories are followed by optimized beams.
  • The proposed scheme maintains a positive average covert rate at high sensing thresholds where the fly-hover-fly benchmarks drop to zero, so sensing constraints need not eliminate covert communication.
  • Imperfect successive interference cancellation is a first-order performance factor: better SIC quality directly increases the achievable covert rate.
  • Increasing the number of antennas on the UAVs improves both the average covert rate and the sensing performance under the co-designed scheme.
  • The simulation results show that the dual-UAV cooperative design achieves a higher average covert rate than the single-UAV scheme across the tested covertness and residual-interference ranges.

Reading between the lines

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

  • A natural extension is to optimize the jammer's artificial noise covariance as a full-rank matrix rather than a single rank-one beam, which could confuse the warden more effectively while adding spatial diversity to the sensing illumination.
  • The greedy sensing-slot selection assumes an equal number of slots per target and static target positions; unequal target priorities or moving targets would require an online variant of the weighted-distance minimization.
  • Because the covertness guarantee is built on a lower bound of the detection error probability rather than the exact minimum, the design is conservative, and the actual detection probability may be even lower than the constraint suggests.
  • The same hybrid monostatic-bistatic idea could apply to passive sensing scenarios where the jamming UAV illuminates targets without receiving echoes, provided synchronization and clock-offset effects are modeled.
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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

4 major / 6 minor

Summary. This paper considers covert communication in an air-to-ground ISAC network with two UAVs: Alice, a dual-functional BS transmitting to Bob while sensing ground targets, and Jack, a cooperative jammer whose artificial noise both confuses Willie and serves as a bistatic sensing illumination. The authors formulate two average-covert-rate maximization problems for the communication-only (CCO) and communication-plus-sensing (CCS) phases, with transmit power, maneuver, KL-divergence-based covertness, and distance-normalized beampattern sum-gain sensing constraints. A BCD method is proposed, alternating between SDR-based beamforming and trust-region SCA trajectory updates for Alice and Jack; the CCS phase selects sensing slots by a weighted-distance greedy algorithm and optimizes the dual-functional beamformers. Simulations compare the proposed scheme with fly-hover-fly and single-UAV benchmarks for different flight times, sensing thresholds, covertness levels, and residual SIC levels. The central claim is that the proposed co-design yields the highest ACR and sensing performance while satisfying all constraints.

Significance. The motivating idea of reusing a cooperative jammer's AN as a bistatic sensing waveform is attractive and is a reasonable extension of the single-UAV ISAC covert-communication literature. The system model is fairly complete: imperfect SIC is incorporated, and the covertness condition is derived from a standard KL-divergence bound rather than imposed ad hoc. The benchmarks and parameter sweeps in Figs. 4-7 provide concrete, falsifiable predictions, and no parameters are fitted to produce the reported curves. However, the algorithmic core of the paper, which generates those predictions, contains demonstrable algebraic errors in the claimed first-order Taylor expansions and provides no convergence proof. The numerical gains are therefore not presently supported. If the derivations are corrected and the simulations regenerated, the paper could be a useful contribution.

major comments (4)
  1. [III-A, Eq. (39)] The coefficient rho_a^(t1)[n] in Eq. (39) is not a valid first-order Taylor expansion of \hat R_b^cco[n] in Eq. (32). With d^2 = A_a^2 + ||u_a - v_b||^2 and zeta2 = eta_ab + d^2 zeta1, the true gradient is \nabla_{u_a}\hat R = (\gamma_ab + 2 zeta1 (u_a - v_b))/(ln2 * zeta2) - 2 (u_a - v_b)/(ln2 * d^2). Eq. (39) simplifies to \gamma_ab/(ln2 * zeta2) + zeta1 (u_a - v_b)/(ln2 * zeta2) - (u_a - v_b)/(ln2 * d^2), i.e., only half of the zeta1-dependent and distance-dependent terms. Hence Eq. (37) is not even a local linearization, and the inequality \hat R >= \tilde R is unsupported. Since Algorithm 1 updates Alice's trajectory by maximizing this surrogate in (P4.t1), the reported trajectory gains in Figs. 4-7 do not follow from the stated derivation.
  2. [III-B, Eq. (53)] Jack's trajectory update has the same defect. For zeta3 and zeta4 defined in Eqs. (49)-(50), a correct first-order expansion of \breve R_b^cco gives gradient terms [\varpi_jb \gamma_jb + 2(\sigma_b^2/\beta + K)(u_j - v_b)]/(ln2 * zeta4) - [\varpi_jb \gamma_jb + 2(\sigma_b^2/\beta)(u_j - v_b)]/(ln2 * zeta3), where K = tr(W_a A(u_a,v_b))/d^2(A_a,u_a,v_b). Eq. (53), as printed, lacks the vector (u_j - v_b) in the second term, and the coefficient structure does not match this gradient. Therefore the surrogate in Eq. (51) is not a first-order lower bound, and subproblem (P5.t2) is not the SCA subproblem claimed.
  3. [III-A, Eqs. (42)-(44)] The linearized covertness constraint (42) is also not the first-order expansion of (36). Expanding (36) at u_a^(t1) yields a linear coefficient \gamma_aw + 2(1-\kappa)(tr(W_j A(u_j,v_w))/d^2(A_j,u_j,v_w) + \sigma_w^2/\beta)(u_a^(t1) - v_w). Eq. (44) instead contains an undefined symbol \gamma_rw and a coefficient without the required factor 2. Thus (42) can admit trajectories that violate the original covertness constraint (31b); the same problem affects Jack's constraint approximation in (57)-(59). Feasibility of the Algorithm 1 output with respect to the covertness constraint is therefore not guaranteed.
  4. [III-D, Algorithm 1] The convergence claim for Algorithm 1 is unsupported. The paper never establishes that \hat R_b^cco or \breve R_b^cco is convex or concave in the UAV positions, so the first-order expansions in (37) and (51) are not proved to be lower bounds; even with a correct gradient, monotonic increase of the surrogate subproblems does not imply monotonic increase of the true ACR. In addition, the trust-region radii \psi_a and \psi_j are reduced geometrically in every iteration without an acceptance/rejection test based on true-objective improvement, so the sentence 'By iteratively updating ... convergence way' at the end of Section III-A has no basis. Because the simulation results in Figs. 4-7 are produced by this algorithm, the central numerical claim is not currently established.
minor comments (6)
  1. [II-C, Eq. (20)] In the definition of the detection error probability, the second term is written as Pr(D1|H1), but it should be Pr(D0|H1).
  2. [II-A, Eq. (19)] Under H1, the jamming signal from Jack should arrive through channel h_jw, but Eq. (19) writes h_aw^H w_j s_j; this is inconsistent with Eq. (26) and with Eq. (18).
  3. [III-A, Eq. (30)] The symbol A(u_m,v) is introduced as a^H(u_m,v)a(u_m,v), which is a scalar; in the trace expressions such as tr(W_a A(u_a,v_b)) it must be the rank-one matrix a(u_m,v)a^H(u_m,v).
  4. [Table I] In Table I, both Alice and Jack final locations are labeled u_F_j; one of the two entries should be Alice's final location u_F_a, and the printed coordinates should be checked.
  5. [III-D, Algorithm 1] Algorithm 1's stopping-criterion indices appear inconsistent: line 5 compares \bar R^{(t3+1)} - \bar R^{(t1)} with a threshold, although t1 is not yet defined in the inner loop; this should be t3.
  6. [IV-B, Figs. 5-7] The sensing performance is measured by the same distance-normalized beampattern sum-gain that is imposed as constraint (28b); the reported sensing gains are therefore partly by construction. An independent sensing metric, such as detection probability or Cramér-Rao bound, would substantially strengthen the claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the covertness and sensing constraints, the ACR objective, and the optimization surrogates are derived from the stated system model without fitting the reported results to their inputs.

full rationale

The central claim of the paper is that co-designing Alice's beamforming, Jack's jamming beamforming, and the dual-UAV trajectories maximizes the average covert rate under covertness and sensing constraints. This claim rests on the optimization problems P1 and P2, which are solved by BCD/SCA/SDR. The covertness constraint (26) follows from the KL-divergence bound (24)-(25) and the MDEP expression (23); the sensing constraint (28b) is exactly the defined beampattern sum-gain (17), but using the same metric as both a constraint and a performance measure is not a circular reduction because no fitted parameters are involved and the ACR comparisons in Figs. 4-7 are made under that constraint. The trajectory surrogates in Eqs. (37)-(42) and (51)-(57) are derived from the rate expressions (30)-(35) and (48)-(50), not from the final simulation outputs; even if the FOT coefficients contained algebraic errors, that would be a correctness issue rather than circularity. The cited prior work [10] supplies an algebraic expansion for η in Eq. (35), but this is an independent external result, not a load-bearing self-citation, and no author-specific uniqueness theorem is invoked. Accordingly, no step in the derivation reduces by construction to its own inputs.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The central design relies on two hand-chosen weighting factors and a hand-set residual SIC level, plus five modeling assumptions, the most fragile being the unproved Taylor-bound premise in the SCA. No new physical entities are introduced.

free parameters (2)
  • weighting factors alpha1, alpha2 = not specified
    Introduced in Eq. (70) to balance covert communication distance and sensing distance when selecting sensing time slots; no values are given in Table I and the result depends on their choice.
  • residual SIC level = -8 dB
    Hand-chosen simulation parameter modeling imperfect SIC in Eqs. (14)-(15); it directly controls the reported average covert rate in Fig. 7.
assumptions (5)
  • domain assumption All UAV-ground links are strong LoS with distance-dependent path loss and no fading (Eq. (9)).
    Used throughout the signal, sensing, and covertness models; optimistic for covert detection in urban or scattered environments.
  • domain assumption Sensing performance is measured by the distance-normalized transmit beampattern sum-gain (Eq. (17)).
    Adopted because the CRB is intractable; the radar receive model and echo processing are not modeled, so sensing claims rest on this proxy.
  • domain assumption Willie uses a Neyman-Pearson energy detector with known noise variance and effectively infinite samples (Eqs. (21)-(23)).
    The covertness constraint is derived from this detector model via a KL-divergence bound; a different warden strategy would change the constraint.
  • domain assumption The exact locations and channels of Willie are known to the optimizer.
    The covertness constraints in (27d) and (28e) use exact h_aw and h_jw; uncertainty or mobility of Willie is not considered.
  • ad hoc to paper The first-order Taylor expansions in Eqs. (37) and (42) are valid lower and upper bounds for the SCA subproblems.
    No convexity or concavity of the rate and covertness functions with respect to UAV positions is established; this is an unproved premise of the optimization algorithm.

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Pith. "Pith review of Dual-UAV-Aided Covert Communications for Air-to-Ground ISAC Networks." pith.science (2026). https://pith.science/paper/SBFTBIOE

@misc{pith2026250600601,
  author       = {Pith},
  title        = {Pith review of: Dual-UAV-Aided Covert Communications for Air-to-Ground ISAC Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SBFTBIOE}},
  note         = {Machine review of arXiv:2506.00601}
}
read the original abstract

To enhance both the sensing and covert communication performance, a dual-unmanned aerial vehicle (UAV)-aided scheme is proposed for integrated sensing and communication networks, in which one UAV maneuvers as the aerial dual-functional base-station (BS), while another UAV flies as the cooperative jammer. Artificial noise (AN) transmitted by the jamming UAV is utilized not only to confuse the ground warden but also to aid the aerial BS to sense multiple ground targets by combing the target-echoed dual-functional waveform and AN components from a perspective of the hybrid monostatitc-bistatic radar. We employ the distance-normalized beampattern sum-gain to measure the sensing performance. To maximize the average covert rate (ACR) from the aerial BS to the ground user, the dual-functional BS beamforming, jamming UAV beamforming, and dual-UAV trajectory are co-designed, subject to transmit power budgets, UAV maneuver constraint, covertness requirement, and sensing performance constraint. The imperfect successive interference cancellation (SIC) effects on the received signal-to-interference-plus-noise ratio are also considered in maximizing the ACR. To tackle the highly complicated non-convex ACR maximization problem, dual-UAV beamforming and dual-UAV trajectory are optimized in a block coordinate descent way using the trust-region successive convex approximation and semidefinite relaxation. To find the dual-UAV maneuver locations suitable for sensing the ground targets, we first optimize the dual-UAV trajectory for the covert communication purpose only and then solve a weighted distance minimization problem for the covert communication and sensing purpose.

Figures

Figures reproduced from arXiv: 2506.00601 by the authors.

Figure 1
Figure 1. The A2G ISAC network model sensing the ground targets. On the other hand, Jack transmits the AN signal to confuse Willie’s detection and aid Alice to sense the ground targets. In particular, Alice detects the ground targets based on the echoed ISAC wave and AN components; Thus, formulating a hybrid monostatic-bistatic radar system [27], [28]. We assume that Alice and Jack are both equipped with a vertically position… view at source ↗
Figure 2
Figure 2. Dual-UAV trajectories achieved by different scheme [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Selected time slot indices for target sensing [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Sum covert rate under different flight time [PITH_FULL_IMAGE:figures/full_fig_p012_4.png]
Figure 7
Figure 7. Figure 7: ACR under different residual inteference levels [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

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Pith tools

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