Trade-off for Secure UAV-ISCC Systems

Congke Jiang; Gaofeng Pan; Hongjiang Lei; Jun He; Ki-Hong Park; Liang Yang; Wenqian Shen

REVIEW 5 major objections 5 minor 35 references

Trade-off for Secure UAV-ISCC Systems

T0 review · 5 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper claims that jointly optimizing a UAV's 3D trajectory, beamforming, scheduling, and computational frequency can trade off secrecy rate, sensing rate, and computing energy efficiency.

desk verdict Competent SCA/AO design paper for a three-way security-sensing-computing trade-off in UAV-ISCC, with a genuine but fixable gap between the stated unknown-eavesdropper model and the optimization that treats E as exactly known. read the letter →

arxiv 2607.21939 v1 pith:66JTULA3 submitted 2026-07-24 cs.IT math.IT

Hongjiang Lei , Jun He , Congke Jiang , Ki-Hong Park , Wenqian Shen , Liang Yang , Gaofeng Pan This is my paper · ORCID

classification cs.ITmath.IT

keywords integratedsensingcommunicationandcomputingUAV-assistedISCC3Dtrajectoryoptimizationphysical-layersecuritybeamformingdesignradarestimationratecomputationalenergyefficiencyperformancetrade-off

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 tries to establish that a single rotorcraft UAV can simultaneously support secure downlink communication, radar sensing, and edge computing, and that all three performances can be steered by a common set of controls: 3D trajectory, transmit beamforming, scheduling of users and targets, and onboard computational frequency. It formulates three optimization problems that maximize average secrecy rate, average radar estimation rate, and average computing energy efficiency, then a normalized weighted-sum problem that trades the three metrics against one another. A sympathetic reader would care because it offers a concrete design recipe for aerial systems that must protect data, sense targets, and process information under one energy budget.

What carries the argument

The load-bearing machinery is an alternating-optimization loop that separates the coupled problem into blocks—scheduling, computation and beamforming, horizontal trajectory, vertical trajectory—and applies successive convex approximation to turn each non-convex block into a convex surrogate. Inside it, the secrecy rate is expressed as the difference of two log-SINR terms under a probabilistic line-of-sight channel; the radar estimation rate is a log function of the sensing SINR; and the computing metric is data processed per weighted energy. The normalized weighted-sum formulation rescales each metric by its own single-objective optimum so the three can be combined despite different units and magnitudes.

What would settle it

Take the optimized trajectory from the weighted design, move the eavesdropper 20 m away from the assumed location, recompute the actual secrecy rate with the true channel, and check whether it still meets the minimum secrecy constraint; a drop below the promised rate would show the security guarantee depends on exact eavesdropper knowledge.

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Extended reading notes

Core claim

The central claim is that the three performance objectives are not mutually exclusive: with the UAV's 3D trajectory, beamforming, user/target scheduling, and computational frequency as joint variables, each of the average secrecy rate (Problem P1), sensing rate (Problem P2), and computational energy efficiency (Problem P3) can be individually maximized, and their normalized weighted sum (Problem P4) yields a family of coordinated designs spanning the three objectives. The paper shows numerically that the optimized flight patterns differ in a recognizable way: communication-centric flight visits users and climbs for line-of-sight, sensing-centric flight stays low and close to targets, computing-centric flight hovers near the base station, and the weighted design interpolates between these behaviors while skipping slots where the UAV passes too close to the eavesdropper.

Load-bearing premise

The optimization treats the eavesdropper's position and steering vector as exactly known, even though the system model calls the eavesdropper unknown and uncertain.

Editorial extensions

If this is right

A system designer can choose weights (ω1, ω2, ω3) to move the UAV's behavior from security-first to sensing-first to computation-first, with uniform weighting giving a middle trajectory.
Communication-centric operation flies the UAV to visit users and climb for line-of-sight, while sensing-centric operation stays low and close to targets because sensing SINR decays with the fourth power of distance.
The scheduler intentionally leaves a time slot empty when the UAV is too close to the eavesdropper, so secrecy constraints shape the schedule as well as the path.
The beamforming solution concentrates normalized gain on the scheduled user and target, with only residual gain toward the base station and eavesdropper.

Reading between the lines

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

If the eavesdropper's location is only known approximately, the same alternating-optimization structure could be wrapped in a robust formulation with an uncertainty set around qE; the paper's current exact-qE treatment is a special case.
The normalized weighted-sum method could be applied to other aerial ISCC variants, for example multi-UAV or RIS-aided systems, as long as each of the three metrics has a well-defined single-objective normalization.
Because the algorithm is alternating and SCA-based, it likely converges to a stationary point rather than a global optimum; the guarantees in the paper are about feasibility and monotonic improvement, not global optimality.

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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

Hi [Name],

Short version: this is a standard but competently executed joint optimization paper for a UAV-ISCC system, and it does something I don't think is in the literature: it maximizes secrecy rate, radar estimation rate, and computation energy efficiency together, with 3D trajectory, beamforming, scheduling, and CPU frequency in one normalized weighted-sum problem. The three single-objective problems are sensible, the SCA machinery is mostly standard, and the simulation trajectories behave the way they should (Com flies close to users, Sen close to targets, Cop close to the BS, CSC in between). The authors know the literature, including their own prior work, and the citation pattern looks fine.

The soft spots are real but not fatal. The biggest one is the eavesdropper. The model says E is "unknown and uncertain," yet every secrecy expression uses an exact steering vector a_E[n] and exact distance bounds, and the simulation fixes E at one point. That means the optimized trajectory and beamforming are tuned to a specific E location, and the secrecy rate is neither worst-case nor outage-constrained. This is fixable by either explicitly assuming E's location/CSI is known (e.g., from prior radar localization) or reformulating with an uncertainty set, but as written the headline security claim overstates what is shown. That's the main thing I'd want addressed before publication.

Two smaller issues. First, there's a units inconsistency in (13): the first term is bits per slot, the second is bits per second; you need a delta_t on f_s/F_s. Second, the paper claims "performance boundaries" from four weighted-sum runs in Fig. 3, and asserts convergence without showing a convergence plot or any baseline comparison. Since P4 is solved by SCA, these are likely feasible designs near a local optimum, not a proven Pareto boundary. The normalized weighted-sum construction is standard enough (normalizing by each objective's own maximum is a normal trick), so I wouldn't call it self-referential in a damaging sense; I'd just ask for humility about what a few weight settings establish.

My verdict matches the reader's: conditional. The core algorithmic idea is coherent and the math is mostly solid. The E-location contradiction cuts the strongest claim, but it doesn't sink the paper once the model is reworded or made robust. The units bug is minor; the missing baselines are a presentation gap.

This paper deserves a serious referee. The readership is the UAV-ISCC / physical-layer-security optimization crowd. It's not a breakthrough, but it's a usable design tool and the formulation is new. I'd send it to review with a request for a robustness or known-E assumption, a convergence curve, and a baseline comparison.

Take care.

Referee Report

5 major / 5 minor

Summary. The paper considers a UAV-assisted integrated sensing, communication, and computing (ISCC) system in which a rotorcraft serves multiple ground users and sensing targets while offloading part of its sensed data to a ground base station, with an eavesdropper attempting to intercept downlink transmissions. The authors formulate three single-objective optimization problems: maximizing the average secrecy rate (P1.0), the average radar estimation rate (P2.0), and the average computing energy efficiency (P3.0), subject to scheduling, beamforming, trajectory, power, and energy constraints. They then propose a normalized weighted-sum problem (P4.0) to balance the three metrics. Each problem is solved by an alternating optimization (AO) framework that successively optimizes scheduling, beamforming/computing resources, and horizontal and vertical UAV trajectory, with successive convex approximation (SCA) used to handle nonconvexity. Numerical results illustrate optimized trajectories, schedules, beamforming gains, and performance under different weighting coefficients.

Significance. If the technical claims were fully supported, the paper would offer a useful algorithmic framework for jointly managing security, sensing, and computation in UAV-aided ISCC systems, and the numerical study provides a rich set of design insights (e.g., altitude/distance trade-offs, the role of eavesdropper proximity in scheduling). The SCA and AO derivations are mostly coherent and follow standard practice in the UAV communications literature. However, the central security claim rests on an internally inconsistent eavesdropper model, the "performance boundaries" claim is not supported because the solved problems are local approximations without convergence or global optimality guarantees, and the trade-off results are based on a self-referential normalization. The paper would benefit from a focused revision that clarifies the eavesdropper assumption and tempers the claims; the underlying optimization methodology itself is sound enough to be salvageable.

major comments (5)

[Section II and Eqs. (6), (28f), (46h); Section VII] The system model states that “there exists an unknown and uncertain eavesdropper (E)”, but every secrecy expression and constraint uses exact knowledge of q_E and a_E[n]. Specifically, Eq. (6) defines the eavesdropper SINR through a_E^H[n] w_K[n], constraint (28f) uses ||q_s[n] − q_E||, constraint (46h) uses d_{h,E}[n], and the numerical section fixes E at [300, 200]^T. The optimized trajectory is visibly shaped by that point (Fig. 4(a) even schedules no user at slot 51 because the UAV is too close to E). With an uncertain E, the quantity R_sec^{U_k}[n] in Eq. (10) is neither a worst-case nor a probabilistically robust secrecy metric, so the claimed “secure” system is not supported. Please either explicitly assume that E’s location and steering vector are known (e.g., via prior radar localization) or reformulate the secrecy constraints over an uncertainty set and solve the resulting robust counterpart.
[Section VII and Algorithms 1–2] The numerical section claims convergence and effectiveness, but no convergence curve is shown, no comparison with baseline schemes is provided, and no multi-start or initialization sensitivity study is reported. Because the AO/SCA method is nonconvex and only finds a local solution (under favorable conditions), the single runs in Figs. 2–4 cannot substantiate the abstract’s claim that the three problems “establish the system’s performance boundaries.” Please add convergence plots, random initialization results, and at least one benchmark (e.g., fixed horizontal trajectory, equal-power beamforming, or a greedy scheduling baseline).
[P1.2b, P2.2, P3.2, P4.2 (Section III.B and later)] The rank-one constraint (17g) is dropped in the SDP relaxations and recovery is deferred to “Gaussian randomization” or similar, but no proof or numerical verification is given that the recovered beamformers satisfy the original rank-one constraints and achieve the reported objective values. Without such a check, the reported secrecy rates, sensing rates, and energy efficiencies may correspond to infeasible designs. Please report the ranks of the SDP solutions and give a comparison of objective values before and after rank recovery, or state clearly that the relaxation is not guaranteed to be tight.
[Section VI, P4.0 and Eq. (71)] The trade-off problem normalizes each metric by the value obtained from this paper’s own approximate solutions P1.0–P3.0 and then maximizes a weighted sum. Maximizing a weighted sum of objectives with positive weights yields a Pareto-optimal point by construction, so the CSC results in Section VII are not an independent verification of a trade-off. Moreover, because P1.0–P3.0 are solved approximately, the normalization denominators λR̃_sec, λR̃_sen, and λΦ̃ are not true bounds; if the weighted problem ever finds a solution exceeding an individual single-objective run, the normalized objective can exceed 1, which is not discussed. The claim that P4.0 provides a “theoretical basis” for performance-coordinated design should be softened accordingly.
[Section V, Eq. (62) and Table I] The “computing energy efficiency” in P3.0 is defined using E_s[n] = α_1 E_cop[n] + α_2 E_tra[n] + α_3 E_fly[n] with α = (10, 5, 1) in Table I. These weighting coefficients are not derived from any physical principle, measurement, or sensitivity analysis, so the computed Φ̄ is not an actual energy efficiency in bits/J and the results for the “computing-centric” scheme are hard to interpret. Please justify the weights or report the physical energy efficiency E_cop/E_tra/E_fly as a secondary metric.

minor comments (5)

[Eqs. (3a) and (3c)] The quantifier in constraints (3a) and (3c) appears to be “∀n”, but the intended meaning is “∀k” and “∀j”; please correct this notation.
[Eq. (12)] The radar estimation rate in Eq. (12) is written as “δ 2μ P_L”, which is ambiguous; it should be typeset as δ/(2μ) so that the subsequent constraints (20) and (36) are immediately transparent.
[Eqs. (10) and (18)] Eq. (18) replaces the secrecy rate in Eq. (10) with an expression that omits the [x]^+ operator, and the text does not explain how nonnegativity is guaranteed; please clarify this simplification.
[Algorithms 1 and 2] The stopping criteria in Algorithms 1 and 2 use R(·) and Ξ(·), but the analogous convergence criteria for P2.0 and P3.0 are not written out; please define these quantities explicitly in the algorithm statements.
[Section VII, Fig. 4 caption] The caption of Fig. 4 contains the typo “Uiniform”; it should read “Uniform”.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the three single-objective optimizations and the normalized weighted-sum trade-off are self-contained formulations; self-citations are attribution, not load-bearing.

full rationale

The derivation chain is self-contained at the level claimed. The system model (Sec. II) supplies channel, sensing, and computing expressions; P1.0-P3.0 maximize the three metrics over the same design variables; P4.0 is the standard weighted-sum scalarization with each metric divided by its own single-objective optimum from P1.0-P3.0. This normalization is a scaling device, not a hidden input: the Pareto-type trade-off is the direct object being optimized, and no quantity is fitted to a subset of data and then re-reported as a prediction. The replacement of R_sec by Eq. (18) is attributed to the authors' prior [20], but the expression follows in-text from Eqs. (9)-(10) plus the scheduling variable theta, so the citation is not load-bearing. Convexity facts cited from [29] are parameter-free mathematical properties that can be checked independently. The 'unknown and uncertain eavesdropper' wording conflicts with the exact q_E and a_E[n] used in Eqs. (5)-(7) and constraints (28f), (31b), (46h); this is a robustness/consistency gap in the security model, but it does not make any derived quantity equal to its own input, so it is a correctness risk rather than circularity. Numerical results are simulations of the proposed algorithm with no external data fit. Therefore no circular step is exhibited, and the score is 0.

Assumptions & free parameters 2 free parameters · 6 assumptions · 0 invented entities

The central result is an optimization recipe. The performance metrics and trade-off are defined by the authors through borrowed channel and sensing models; the only numbers introduced by hand are the energy weighting coefficients and the weighted-sum priorities. No new physical entity is postulated.

free parameters (2)

Energy weighting coefficients alpha1, alpha2, alpha3 = 10, 5, 1
Introduced ad hoc in Section V to balance computing, transmission, and propulsion energy in the computation-centric objective. The reported computing energy efficiency depends on these arbitrary weights.
Weighted-sum coefficients omega1, omega2, omega3 for CSC = 1/3, 1/3, 1/3 and variants such as 0.8, 0.1, 0.1
Chosen by hand in Section VII for the trade-off experiments. The trade-off curves and conclusions depend on these user-defined priorities.

assumptions (6)

domain assumption Probabilistic LoS channel model with environment constants C and D from [22] describes all air-to-ground links.
The entire rate and sensing expressions depend on this model. If the channel statistics differ, all optimized values and conclusions shift.
ad hoc to paper NLoS rate contributions are neglected; achievable rate is approximated by its LoS component (Eq. (9)).
Used for tractability. The paper claims this is better than a worst-case bound, but it drops NLoS terms that can carry legitimate signal power or eavesdropper signal power.
domain assumption Eavesdropper position and channel steering vector are known exactly during optimization.
Eqs. (5) to (7) and constraint (28f) use exact qE and a_E[n], even though the system model calls E unknown and uncertain. No robust or worst-case formulation is included.
domain assumption Radar estimation rate model from [30] accurately quantifies target information extraction with constants sigma2_pre, gamma_hat, B_w, and mu.
The sensing objective is inherited from radar and communications coexistence literature and is not derived or validated for this specific UAV scenario.
ad hoc to paper Binary scheduling variables can be relaxed to [0,1] without changing the optimum.
The paper relaxes constraints (3a)-(3d) to continuous intervals but provides no proof that the relaxed optimum is integral or that a rounding scheme preserves feasibility and performance.
ad hoc to paper SDP rank-one relaxation plus Gaussian randomization recovers a feasible rank-one beamformer.
Constraint (17g) is dropped in P1.2b and similar problems, and standard randomization is invoked without a verification that the recovered beamformers meet all constraints.

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Pith. "Pith review of Trade-off for Secure UAV-ISCC Systems." pith.science (2026). https://pith.science/paper/66JTULA3

@misc{pith2026260721939,
  author       = {Pith},
  title        = {Pith review of: Trade-off for Secure UAV-ISCC Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/66JTULA3}},
  note         = {Machine review of arXiv:2607.21939}
}

read the original abstract

The integrated sensing, communication, and computing (ISCC) system overcomes the limitations of conventional standalone architectures. Through resource sharing and collaborative design, it dynamically optimizes and jointly enhances communication, sensing, and computing performance, thereby significantly improving overall system efficiency. This work investigates the performance trade-off among secure communication rate, radar estimation rate, and computational energy efficiency in an uncrewed aerial vehicle (UAV)-assisted ISCC system. By jointly optimizing the UAV's three-dimensional (3D) trajectory, beamforming, user scheduling, and computational frequency, three optimization problems are formulated to maximize the average secrecy rate, sensing rate, and computational energy efficiency, respectively, thus establishing the system's performance boundaries under diverse scenarios. On this basis, the trade-off among security, sensing, and computation is further explored with the goal of maximizing the normalized weighted sum of the three performance metrics, which provides a theoretical basis for the performance-coordinated design of aerial ISCC systems.

Figures

Figures reproduced from arXiv: 2607.21939 by the authors.

**Figure 2.** Optimized parameters and performance of the four sch [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗

**Figure 3.** Optimized parameters and performance of the CSC sche [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: The scheduling results and beampattern gains for Uni [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

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