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REVIEW 3 major objections 5 minor 39 references

Hybrid RIS-Enhanced ISAC Secure Systems: Joint Optimization in the Presence of an Extended Target

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A hybrid RIS with a few active and many passive units can maximize worst-case sensing SINR while forcing an eavesdropping extended target into a destructive decision zone in an ISAC downlink.

desk verdict A competent engineering paper with a new system combination and a solid optimization pipeline, but the GFP convergence claim is unsupported as written and the baseline set misses the closest hybrid-RIS prior. read the letter →

arxiv 2505.20012 v1 pith:IMLTUJ26 submitted 2025-05-26 physics.ins-det

Yu Yao , Junhao Zhang , Pu Miao , Long Zhang , Gaojie Chen , Feng Shu , Kai-Kit Wong This is my paper · ORCID
classification physics.ins-det
keywords integratedsensingandcommunicationsecurehybridreconfigurableintelligentsurfaceextendedtargetdetectiongeneralizedfractionalprogrammingpenaltydualdecompositionconstructiveinterferencedirectionalmodulation
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 tries to establish that a hybrid reconfigurable intelligent surface—one with a few active amplifying units and many passive phase-shifting units—can simultaneously improve radar detection of an extended eavesdropping target and secure downlink communication in an integrated sensing and communication (ISAC) system. The key move is to design the transmit waveform, the base-station receive filters, the user receive beamformers, and all RIS weights together, maximizing the worst-case sensing SINR over an uncertain target-location set while forcing the eavesdropper's received symbols into a destructive decision zone. The authors argue that their generalized-fractional-programming, penalty-dual-decomposition, and penalty-convex-concave procedure handles the non-convex quartic problem, and simulations show the hybrid-RIS design outperforms passive-RIS, active-RIS, random-RIS, and no-RIS baselines. A reader would care because this addresses a realistic deployment constraint—target location is never known exactly—and quantifies how much sensing margin is lost to that uncertainty.

What carries the argument

The load-bearing machinery is the alternating optimization framework that splits the joint problem into four subproblems: closed-form minimum-variance-distortionless-response receive filters for each candidate target location, feasibility-style receive beamformers at the SCUs, a convex transmit-waveform update obtained via the generalized-fractional-programming quadratic transformation, and a hybrid-RIS update via penalty dual decomposition with an auxiliary copy $\vartheta_1$ of the RIS coefficient vector $\vartheta$. The auxiliary-copy trick turns the quartic sensing SINR into a quadratic form in $\operatorname{vec}\{\vartheta\vartheta_1^T\}$, and the penalty convex-concave procedure handles the unit-modulus constraints; big-M relaxation with binary variables handles the discrete-phase security constraints. The key identity is the quadratic transform $g_p = 2u_p \Re(w_p^H A_p x) - u_p^2 w_p^H(\Pi_c+\Sigma_p+\sigma_R^2 I)w_p$, which converts a max-min ratio into a concave lower bound when the auxiliary variable $u_p$ is fixed.

What would settle it

Run Algorithm 3 from a feasible start on a small instance, for example $N_a=4$, $K=2$, one hybrid RIS with $N_I=8$ and 2-bit phase resolution, and compare its worst-case sensing SINR against an exhaustive grid search over the transmit waveform and RIS phases; if any feasible grid point achieves a higher worst-case SINR, or if the algorithm's objective does not increase monotonically, the claimed optimality and convergence are not supported.

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Extended reading notes

Core claim

The paper claims that by jointly optimizing the transmit signal, the BS receive filter bank, the SCU receive beamformers, and the weights of multiple hybrid RISs, the worst-case sensing SINR for detecting an extended target can be maximized while guaranteeing constructive-interference communication quality and destructive-interference security against a single-antenna eavesdropping target whose location is only imperfectly known. The target is modeled as an extended scatterer through a target impulse response with clutter, and the design is formulated as a non-convex max-min problem with discrete RIS phase constraints and power budgets. The authors solve it with an alternating framework based on generalized fractional programming, penalty dual decomposition, and a penalty convex-concave procedure. Their simulations report that the resulting hybrid-RIS design improves both detection and secure transmission over fully-passive, fully-active, random, and no-RIS benchmarks, with gains that grow as the number of RIS units and the power budget increase.

Load-bearing premise

The load-bearing premise is that the reformulated sensing SINR satisfies a generalized-fractional-programming lemma whose concave-numerator and convex-denominator conditions are never checked, and the raw numerator $|w_p^H A_p x|^2$ is actually a convex quadratic, so if those conditions fail the claimed monotone convergence is not assured and the algorithm may only return a heuristic solution.

Editorial extensions

If this is right

  • In the single-RIS simulations, the proposed hybrid-RIS design outperforms the optimized passive RIS by roughly 77 percent in worst-case sensing SINR at $N_I=20$ and about 110 percent at $N_I=50$, indicating that the hybrid-RIS advantage grows with array size.
  • The design captures most of the sensing gain with only a small number of active units: moving from $A=0$ to $A=1$ gives a larger improvement than moving from $A=9$ to $A=10$, so active-unit count can be chosen near the knee of the performance curve.
  • Worst-case sensing SINR decreases as the target-location uncertainty set grows, and the loss is steeper when the angular separation between the user and the eavesdropper is small, meaning the robust design trades sensing margin for security margin.
  • With the destructive-interference constraint active, the eavesdropper's symbol error rate rises to about 0.8 under the constructive-interference-only design and approaches 1 under the combined CI plus DI design, so the eavesdropper cannot reliably decode.
  • The discrete-phase projection maintains feasibility and approaches the continuous-phase performance as phase resolution increases, whereas random discrete phases do not improve with resolution, showing that careful discrete-phase design is essential.

Reading between the lines

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

  • The same GFP/PDD/PCCP template could be applied to other quartic ISAC objectives, such as STAR-RIS or fully-active RIS variants, but only after the convexity conditions of Lemma III.1 are verified; the paper does not show that its reformulation satisfies the concave-numerator and convex-denominator requirements.
  • The simulations suggest a hardware-sizing rule: because most of the sensing gain comes from the first few active units, an operator could set the active-unit count from the power budget and the target-uncertainty size rather than maximizing active units.
  • The nominal-versus-worst-case SINR gap shown in the convergence plots could be used as a calibration curve: the slope of worst-case SINR versus uncertainty-set size quantifies how much sensing margin a given target-location error costs.
  • A natural testable extension would be to replace the fixed target-location uncertainty set with a Bayesian prior and compare the worst-case design against an expected-SINR design; the paper only treats the worst-case formulation.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The manuscript studies a multi-RIS-aided ISAC system in which a base station communicates with multiple SCUs while sensing an extended target that may act as an eavesdropper, under imperfect target-location knowledge. A joint optimization problem is formulated to maximize the worst-case sensing SINR over a grid of possible target locations, subject to constructive-interference QoS constraints, destructive-interference security constraints, discrete RIS phase-shift constraints, and power budgets. The proposed Algorithm 3 alternates closed-form MVDR receive-filter updates, convex feasibility updates for SCU beamformers, a GFP-based waveform update, and a PDD/PCCP-based RIS update followed by quantization to discrete reflection coefficients. Simulation results compare the method with no-RIS, fully passive, fully active, and random-RIS baselines.

Significance. If the theoretical claims were established, the paper would provide a fairly complete design framework for a relevant and nontrivial scenario: multiple hybrid RISs, an extended target with imperfect location, and both communication-QoS and security constraints. The system model is rich, and the algorithmic pipeline (GFP, PDD, PCCP, DRC projection) is plausible as a heuristic. However, the central optimality claim is not supported because the GFP lemma is applied outside its hypotheses, and the claimed improvement over the state of the art is not directly verified against the most relevant prior hybrid-RIS method. The manuscript is therefore best regarded as a heuristic design study whose comparative claims need both theoretical correction and additional baselines.

major comments (3)
  1. [Section III-A, Eqs. (26)-(30)] Lemma III.1 guarantees monotonic convergence to the GFP optimum only when the numerator functions fp are non-negative concave and the denominator functions gp are positive convex. In the application to problem (26), fp(x, Θ) = |w_p^H A_p x|^2 is a convex quadratic in x for fixed w_p and Θ (Hessian 2A_p^H w_p w_p^H A_p, which is positive semidefinite), and it is quartic in Θ; the paper does not verify the lemma's hypotheses. Moreover, the reformulated constraint gp in (30) replaces |w_p^H A_p x|^2 by 2u_p Re(w_p^H A_p x) - u_p^2 w_p^H(...)w_p, which equals the original numerator only when Im(w_p^H A_p x) = 0 after the u_p update. The MVDR update (22) makes w_p^H A_p x real and positive for the current x, but this property is not preserved during the x-subproblem (34) or the Θ-subproblem, and no constraint enforces it. Consequently, the statement that Algorithm 3 maximizes the worst-case sensing SINR is not established; Figures 3-14 provide only empirical evidence for a feasible-point algorithm. The sentence after (26), 'We prove that problem (26) is solvable,' is not followed by a proof.
  2. [Section IV, baseline selection] The abstract and Section IV claim that the proposed design improves the sensing and secure-transmission performance over 'the state-of-the-art RIS-aided ISAC approaches.' However, the simulations compare only against No-RIS, optimized passive RIS, random passive RIS, and optimized active RIS variants. The closest prior hybrid-RIS ISAC method, reference [20], is not included as a baseline, nor is the secure RIS-ISAC method [27] used in the comparison. As a result, the strong comparative claim in the abstract is not supported by the presented numerical evidence; the authors should either add these baselines or weaken the claim to comparisons against the investigated alternatives.
  3. [Section III-C, Algorithm 3] The convergence guarantees of the overall alternating algorithm are not established. The PDD convergence conditions from [36] are not checked for the augmented Lagrangian problem (41), the BCD inner iteration is not shown to converge to a stationary point of (41), and the DRC projection step (47)-(48) together with the acceptance rule in Algorithm 3 makes the iteration discontinuous. The statement in Section III-C that 'the objective value of (41) converges to the limit value' is asserted without proof. If the authors intend to claim only monotone improvement of the computed objective, this should be stated explicitly and the theoretical claims adjusted accordingly.
minor comments (5)
  1. [Eq. (21d)] The discrete reflection-coefficient constraint mixes the phase index and the reflection-unit index: 'e^{j2πn/2^b}, n ∈ {0,...,2^b-1}, n ∈ NI' should be rewritten to state that each ϑ_{s,n} takes one of the 2^b phase values for each s and n.
  2. [Section II-B, around Eq. (15)] The notation '{θ_p, φ_p} ∈ card(χ̄, χ̂)' is unclear; the grid of P possible target locations should be defined explicitly, for example as a Cartesian product of the two angular intervals, with P denoting the number of grid points.
  3. [Fig. 3] The caption and legend of Fig. 3 do not clearly indicate which curves correspond to the CI-only case versus the CI+DI case and which uncertainty sets are used; the text should be more explicit so the reader can map the curves to the described scenarios.
  4. [Section III-C, complexity expression] The complexity expression for Algorithm 2 contains terms such as P((3N+KN)SNI)^{1/2}2KN(SN_I^4) whose variables are not all defined in the surrounding text; please clarify the notation.
  5. [Throughout] There are several typographical and formatting errors (e.g., 'optmization' in Section III-C, inconsistent use of 'reconfigurable' vs 'reconfigurable') that should be corrected in a final revision.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the claimed optimization result is derived within the paper's own model and is not equivalent to its inputs by construction.

full rationale

The paper's central claim is algorithmic: joint design of the transmit waveform, receive filters, SCU beamformers, and hybrid RIS weights to maximize worst-case sensing SINR under CI-type QoS and DI-type security constraints, with the result evaluated in simulation. No parameter is fitted to external data, and the reported 'improvement' is the output of the authors' own simulator against their own baseline implementations; it is not a prediction constrained by fitted inputs. The GFP/PDD/PCCP derivation starts from the stated objective (21) and applies standard transformations: the reformulation of (26) into (29) uses an auxiliary variable and the quadratic-transform structure from Lemma III.1, and the equivalence between (29) and (36) is established algebraically in Appendix A. Self-citations to co-authored works (e.g., [9], [12], [23], [25]-[27]) are used only for standard path-loss settings, contextual motivation, and prior-art discussion; they are not load-bearing for the paper's optimality or uniqueness claims. The one substantive weakness is mathematical: Lemma III.1 requires fp to be non-negative concave, whereas fp(x) = |w_p^H A_p x|^2 is convex in x, and the paper replaces sqrt(fp) with Re(w_p^H A_p x) in gp without fully proving equivalence except under a real-positive MVDR condition. This threatens the GFP optimality guarantee and the abstract's 'maximizes' wording, but it is a correctness risk, not a circular reduction: the derivation does not assume the conclusion it claims to prove. The comparison also omits the closest hybrid-RIS ISAC baseline [20], weakening the 'state-of-the-art improvement' phrasing; however, baseline selection and validation gaps are not circularity. The derivation chain is self-contained against the stated model, so no circular step is identified.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a standard wireless channel model, the extended-target TIR model from prior work, and the assumption that the GFP reformulation satisfies the lemma's convexity conditions. The latter is the most fragile, as the actual numerator is convex in x.

free parameters (3)
  • Big-M factor Υ = not specified
    Introduced in (25) to relax the DI security constraint; a sufficiently large constant, chosen by hand, and its value affects the feasible region.
  • Penalty coefficients (ω, ρ, κ) = not specified
    Used in the objective (26) and PDD/PCCP updates to enforce equality and unit-modulus constraints; values are tuning parameters.
  • Grid resolution P for target locations = not specified (P varied 1,5,10 in Fig. 8)
    The continuous TL uncertainty set is approximated by P discrete points; the worst-case SINR depends on this grid.
assumptions (5)
  • domain assumption Rician fading channel model with LoS and NLoS components for BS-RIS, BS-SCU, RIS-SCU links
    Equations (2), (5), (6) in Section II-A; this is a standard wireless channel model but unverified for the specific scenario.
  • domain assumption Extended target is modeled by a target impulse response (TIR) obtained from the toolbox in [34]
    Section II-B; the detection performance is evaluated via SINR, relying on the prior result that higher SINR improves detection under Gaussian noise.
  • domain assumption Noise from active RIS reflections in the radar echo is neglected
    Footnote 3: the RIS-Eve-BS and RIS-Eve-RIS-BS paths suffer multiple attenuations, so their noise contribution is small. This is an approximation.
  • domain assumption The Eve is co-located with the target and is a single-antenna receiver
    Section II: the extended target is viewed as a single-antenna Eve; the security design assumes this threat model.
  • ad hoc to paper GFP lemma conditions hold for the reformulated worst-case SINR problem
    Lemma III.1 and the reformulation (29) assume fp concave and gp convex; the paper does not validate these for the actual SINR expression.

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Cite this review

Pith. "Pith review of Hybrid RIS-Enhanced ISAC Secure Systems: Joint Optimization in the Presence of an Extended Target." pith.science (2026). https://pith.science/paper/IMLTUJ26

@misc{pith2026250520012,
  author       = {Pith},
  title        = {Pith review of: Hybrid RIS-Enhanced ISAC Secure Systems: Joint Optimization in the Presence of an Extended Target},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IMLTUJ26}},
  note         = {Machine review of arXiv:2505.20012}
}
read the original abstract

Unlike the conventional fully-passive and fully-active reconfigurable intelligent surfaces (RISs), a hybrid RIS consisting of active and passive reflection units has recently been concerned, which can exploit their integrated advantages to alleviate the RIS-induced path loss. In this paper, we investigate a novel security strategy where the multiple hybrid RIS-aided integrated sensing and communication (ISAC) system communicates with downlink users and senses an extended target synchronously. Assuming imperfectly known target location (TL), we consider the joint design of the transmit signal and receive filter bank of the base station (BS), the receive beamformers of all users and the weights of the hybrid RIS. An optimization problem is formulated for maximizing the worst-case sensing signal-to-interference-plus-noise-ratio (SINR) subject to secure communication and system power budget constraints. To address this non-convex problem, we leverage generalized fractional programming (GFP) and penalty-dual-decomposition (PDD), and propose a security solution that efficiently optimizes all variables by employing convex optimization approaches. Simulation results show that by incorporating the multiple hybrid RIS into the optimization design, the extended target detection and secure transmission performance of ISAC systems are improved over the state-of-the-art RIS-aided ISAC approaches.

Figures

Figures reproduced from arXiv: 2505.20012 by the authors.

Figure 1
Figure 1. A description of the studied hybrid RIS-enhanced ISAC [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Depiction of the CI using QPSK as an example, where the [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Convergence of devised scheme under the constraints [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Worst-case sensing SINR versus the number of hybrid R [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 6
Figure 6. Figure 6: Worst-case sensing SINR versus the number of active u [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Worst-case sensing SINR versus SCU’s SINR with vario [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Worst-case sensing SINR versus the number of SCUs wit [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 11
Figure 11. Figure 11: Worst-case sensing SINR versus the maximum power bu [PITH_FULL_IMAGE:figures/full_fig_p013_11.png]
Figure 12
Figure 12. Figure 12: Worst-case sensing SINR versus angular interval of [PITH_FULL_IMAGE:figures/full_fig_p013_12.png]

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Pith tools

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