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REVIEW 4 major objections 7 minor 36 references

Movable-Element STARS-Aided Secure Communications

T0 review · 4 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that letting the elements of a simultaneously transmitting and reflecting surface move inside a small region measurably improves secrecy rates against full-space eavesdropping, with the gain saturating once the movable…

desk verdict Competent incremental optimization paper; the 25% ME-STARS secrecy gain is an upper bound until the perfect-eavesdropper-CSI assumption is disclosed and tested. read the letter →

arxiv 2507.14804 v1 pith:7BFHZ6O5 submitted 2025-07-20 eess.SP

Jingjing Zhao , Qian Xu , Kaiquan Cai , Yanbo Zhu , Xidong Mu , Yuanwei Liu This is my paper · ORCID
classification eess.SP
keywords movableelementsSTARSphysicallayersecuritysumsecrecyratepositionoptimizationpassivebeamformingfull-spaceeavesdroppingalternating
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 allowing the elements of a simultaneously transmitting and reflecting surface (STARS) to move inside a small region materially improves physical-layer security in a full-space eavesdropping scenario, and that the gain saturates once the movable region reaches a modest size. The authors formulate a sum secrecy rate maximization problem that jointly decides where the surface elements sit, how the surface splits and phase-shifts transmitted and reflected signals, and how the base station beams to two legitimate users, while constraining information leakage to two eavesdroppers on both sides of the surface. They solve it by alternating a gradient-ascent position update with successive-convex-approximation beamforming updates. If the claim holds, the practical message is that movable elements are worth deploying for secrecy even though the elements only travel a few wavelengths, and that enlarging the aperture beyond a few wavelengths yields little additional secrecy benefit.

What carries the argument

The carrying mechanism is the element position matrix $\mathbf{R}$ together with a field-response channel model in which only the phases of the BS-STARS and STARS-user/eavesdropper channels change as elements move, while angles and gains stay constant. The algorithm optimizes $\mathbf{R}$ by a gradient ascent that maps unconstrained variables through a hyperbolic tangent into the confined region, handles minimum-distance and rate constraints by a penalty method with log-sum-exp smoothing, and alternates this with SCA-based updates of the STARS transmission/reflection coefficients and the base station beamforming. This decomposition turns the intractable coupled nonconvex problem into three solvable blocks whose successive improvement drives the overall secrecy rate upward.

What would settle it

Simulate the proposed optimization with eavesdropper channels known only up to an estimation error, or with only their statistical distribution available, and compare the resulting achievable secrecy rate against the fixed-position STARS baseline. If the ME-STARS advantage disappears or reverses under imperfect eavesdropper channel knowledge, the paper's central claim of a significant implementable secrecy gain would be falsified.

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Extended reading notes

Core claim

The paper's central claim is that a STARS whose elements can be repositioned within a confined region ('ME-STARS') significantly outperforms a conventional STARS with fixed-position elements in secrecy rate, and that the sum secrecy rate saturates within a limited movable-region size. In the simulated setup, ME-STARS beats fixed-position STARS by about 25 percent at the same transmit power, also outperforming two separate movable-element RISs and a random-position STARS. The saturation is explained by the limited number of locally optimal element positions available inside a small region: once the region is large enough to accommodate those positions, extra aperture gives no further gain. The paper therefore argues both for the value of position optimization at the surface and for a design rule on how large the movable region needs to be.

Load-bearing premise

The load-bearing premise is that the transmitter knows the eavesdroppers' channels exactly when optimizing; without exact eavesdropper channel state information, the computed secure beamforming and the claimed movable-element gain are an upper bound rather than an achievable system performance.

Editorial extensions

If this is right

  • A fixed number of surface elements can yield a secrecy-rate gain without adding RF chains, simply by repositioning elements within a few wavelengths.
  • The movable region can be designed small: once its size reaches a few wavelengths, further expansion adds little secrecy rate, which guides aperture sizing in practice.
  • The ME gain grows with the number of propagation paths, because richer scattering creates more local optima for element positions to exploit.
  • Optimized positions, not just mobility, are what matter: ME-STARS beats random-position STARS, so the gradient-ascent position update carries real value.

Reading between the lines

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

  • The exact-eavesdropper-CSI premise is likely the main gap between the computed rates and a deployable scheme; a statistical or robust formulation over the eavesdropper channel distribution is a natural extension.
  • The saturation result suggests a rule of thumb for aperture sizing that could be tested in other MA/STARS settings: choose the region size where the marginal gain flattens rather than maximizing aperture.
  • Because the gain is tied to small-scale fading richness, the advantage over fixed-position STARS may shrink in strongly line-of-sight environments; a LoS-only simulation would clarify how much of the gain is positional diversity.
  • The same alternating framework could be extended to secrecy outage probability as the objective, which would convert the claimed rate gain into a reliability statement without assuming eavesdropper CSI.
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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 / 7 minor

Summary. This paper considers a downlink MISO secure communication system in which a simultaneously transmitting and reflecting surface (STARS) is equipped with movable elements (MEs). The authors formulate a sum secrecy rate maximization problem that jointly optimizes the ME positions, the STARS transmission/reflection coefficients, and the BS active beamforming, subject to user rate and eavesdropper leakage constraints. They propose an alternating optimization (AO) algorithm: a penalty-based gradient ascent method for ME positions, and successive convex approximation (SCA) / semi-definite relaxation (SDR) methods for the passive and active beamforming subproblems. Convergence and complexity analyses are provided, and numerical results compare the proposed ME-STARS with fixed-position STARS, ME-RIS, and random-position STARS baselines.

Significance. If the central claims hold, the paper provides a useful quantification of the physical-layer security gains offered by moving STARS elements and, importantly, shows that the secrecy-rate benefit saturates as the movable region grows, which is practically relevant for aperture sizing. The system model and optimization machinery are mostly standard and carefully derived; in particular, the gradient derivation in Appendix A and the complexity accounting are explicit, and the comparison baselines are appropriate. The paper does not suffer from the circularity concern that sometimes arises in optimization papers: it optimizes the same secrecy-rate objective that it evaluates, which is the normal procedure for benchmarking an algorithm. However, the quantitative claims are currently under-supported because the threat model and numerical methodology are incompletely specified.

major comments (4)
  1. [Section II-A and II-B, Eqs. (10) and (12)] The optimization problem (12) requires exact eavesdropper channels: constraints (12g)-(12h) and the objective (12a) depend on the eavesdropper rates R^e_{ke,kl} defined in (10), which in turn require exact knowledge of g_{ke}(R). The manuscript never states whether eavesdropper CSI is available, nor does it discuss how a passive adversary's channel would be obtained. Without this premise, the computed beamformers and ME positions are not implementable as a secure communication scheme, and the simulated secrecy rates are upper bounds; the claimed ME-STARS gain over FPE-STARS is therefore not established for the intended threat model. Please state the CSI/threat-model assumption explicitly and either justify it (e.g., an active or untrusted eavesdropper whose channel can be estimated) or add a robust formulation and clearly interpret the current results as benchmark upper bounds.
  2. [Section IV, Figs. 3-7] The numerical results are presented as single curves without any statement of the number of random channel realizations, Monte Carlo averaging, or confidence intervals. Since the users and eavesdroppers are 'randomly scattered' and the channel path responses are random, the quantitative claim in Fig. 4 of 'around 25%' improvement over FPE-STARS is not statistically supported. Please provide the averaging procedure, error bars or standard deviations, and results over multiple random initializations of the non-convex algorithm so that the abstract claims can be assessed.
  3. [Section III-D, Eq. (50)] The convergence argument for Algorithm 4 is incomplete. Inequality (a) in (50) asserts that the ME-position update increases the original sum secrecy rate R_sum, but Algorithm 1 performs gradient ascent on the penalized surrogate G in (21), which contains nonzero penalty and smoothing terms whenever constraints are active; maximizing G need not increase R_sum. The outer loop in Algorithm 1 terminates upon feasibility, not necessarily with a monotone increase of R_sum. Please provide a rigorous monotonicity argument, or alternatively state convergence to a stationary point of a penalized problem, and verify the monotonic behavior empirically.
  4. [Section III-C, Theorem 1] Theorem 1 is load-bearing for the claim that the SDR relaxation of problem (48) is tight, since it justifies recovering rank-one beamforming vectors through Cholesky decomposition. However, the proof is omitted with only a pointer to [14, Appendix A]. For a self-contained journal paper, please include the proof or state explicitly that this is an adaptation of a known result; without this, the rank-recovery step is not verified within the manuscript.
minor comments (7)
  1. [Abstract and Section III] The phrase 'the the active' appears in the abstract and again in the introduction to Section III; it should read 'the active'.
  2. [Section II-B, Eq. (12f) and Section IV] The text says constraint (12f) ensures that distances 'do not exceed D0', but the inequality in (12f) is a minimum-separation constraint. Please reword as 'are no smaller than d0' and unify the notation d0 (used in the equations) with D0 (used in the numerical section).
  3. [Section IV, baseline list] The baseline list jumps from 'Baseline scheme 2 (ME-RIS)' to 'Baseline scheme 4 (RPE-STARS)'; there is no Baseline scheme 3. Please renumber the baselines.
  4. [Algorithm 3 title] The title of Algorithm 3 reads 'BE active beamforming subproblem'; it should be 'BS active beamforming subproblem'.
  5. [Section III-B, Eqs. (25)-(32)] The dependence of the mode index \vartheta on the link side is not made explicit: for a given legitimate user and a given eavesdropper, the correct coefficient matrix is \Phi_t or \Phi_r depending on which side of the STARS the terminal lies. Please make this dependence explicit in (25)-(32) so that the formulation is unambiguous.
  6. [Appendix A, Eq. (A.7)] The noise variance appears as \sigma_l in the denominator of (A.7) but is defined as \sigma_l^2 in Section II-A. Please use the squared notation consistently.
  7. [Eq. (13)] In the third row of the matrix in (13), the subscript 'S,p' should presumably be 'S,k' for consistency with the other rows.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the ME-STARS secrecy-rate comparison is a standard optimization study, with no fitted parameter renamed as a prediction and no central claim defined into existence.

full rationale

The paper's claims are generated by solving the same sum-secrecy-rate objective (12a) that is used to evaluate all schemes, which is the normal setup for an optimization comparison rather than a circular derivation. No parameter is fitted to a subset of data and then reported as a prediction: the ME positions, passive beamforming coefficients, and active beamforming vectors are all optimization variables, and the baselines are evaluated under identical channel models and realizations. The movable-region saturation finding in Figs. 5-6 is an observed behavior of the optimized solutions, not an input assumption or a normalized artifact. The only externally imported result is Theorem 1 in Section III-C, whose proof is delegated to ref. [14] by the same research group; however, that rank bound is a parameter-free mathematical statement about SDR tightness and does not contain the secrecy-rate target, so it is independent technical support rather than a circular premise. The more substantive concern is a modeling gap, not circularity: constraints (12g)-(12h) and the objective in (12a) require exact eavesdropper channels g_{k_e}(R), which is unrealistic for passive adversaries, making the reported rates an upper bound. That missing assumption affects implementability and correctness risk, but it does not make the derivation equivalent to its inputs.

Assumptions & free parameters 1 free parameters · 6 assumptions · 0 invented entities

No scientific parameters are fitted to external data; the recorded free parameter is the hand-set algorithm hyperparameters. The central claim rests on standard field-response and physical-layer-security assumptions, the strongest being perfect eavesdropper CSI. No new particles, mediators, forces, or physical entities are introduced.

free parameters (1)
  • Algorithm hyperparameters (initial step alpha=10, shrink omega_alpha=0.9; penalty and smoothing factors rho=eta=1e-6… = As listed in Table I
    These values are hand-set for the gradient-ascent and SCA penalty loops. They affect the convergence path and the local optima reached, but they are not fitted to external data. The central performance comparison could shift with different hyperparameters, so they are recorded as free choices.
assumptions (6)
  • domain assumption Plane-wave propagation: within the movable region, path angles and gain amplitudes are constant and only phases vary with element position.
    Used throughout Section II-A1, eqs (1)-(4), to express field-response vectors as phase-only functions of r_n. If near-field effects or position-dependent angle variation are significant, the channel model and the numerical gains change.
  • domain assumption Direct links between the BS and legitimate users/eavesdroppers are blocked.
    Stated in Section II-A1 before eq (1). This removes any non-STARS signal path and simplifies the secrecy-rate expressions; in a real deployment with direct links the performance comparison could differ.
  • domain assumption Perfect knowledge of all channels, including eavesdropper channels g_{k_e}(R).
    Required by the objective (11) and constraints (12g)-(12h). The paper never states this assumption explicitly, and for a passive eavesdropper the channel is generally unknown, making the computed secrecy rate an upper bound.
  • domain assumption STARS transmission and reflection coefficients have independent phases and energy-splitting amplitudes with beta_t + beta_r = 1.
    Defined independently via Theta_t and Theta_r in Section II-A. Practical STARS hardware may impose coupling between theta_t and theta_r, as studied in ref [14], which would alter the passive beamforming subproblem.
  • domain assumption Theorem 1 rank bound rank(W_kl) <= rank(Phi_varrho) is taken from ref [14, Appendix A] without proof.
    The SDR relaxation in Algorithm 3 relies on this theorem. The proof is deferred entirely; unless the conditions in ref [14] exactly match this problem, the rank-one recovery is an unverified input.
  • domain assumption Simulation channel statistics: AoDs and AoAs i.i.d. uniform in [-pi/2, pi/2], Rician path responses, users and eavesdroppers randomly scattered.
    Defined in Section IV before Table I. The curves appear to be for a single random deployment rather than an average over realizations, so the numerical claims inherit this specific statistical draw.

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Pith. "Pith review of Movable-Element STARS-Aided Secure Communications." pith.science (2026). https://pith.science/paper/7BFHZ6O5

@misc{pith2026250714804,
  author       = {Pith},
  title        = {Pith review of: Movable-Element STARS-Aided Secure Communications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7BFHZ6O5}},
  note         = {Machine review of arXiv:2507.14804}
}
read the original abstract

A novel movable-element (ME) enabled simultaneously transmitting and reflecting surface (ME-STARS)-aided secure communication system is investigated. Against the full-space eavesdropping, MEs are deployed at the STARS for enhancing the physical layer security by exploiting higher spatial degrees of freedom. Specifically, a sum secrecy rate maximization problem is formulated, which jointly optimizes the passive beamforming and the MEs positions at the ME-STARS, as well as the active beamforming at the base station. To solve the resultant non-convex optimization problem involving highly-coupled variables, an alternating optimization-based iterative algorithm is developed, decomposing the original problem into three subproblems. In particular, for the MEs position optimization subproblem, a gradient ascent algorithm is employed to iteratively refine the MEs' locations within the confined region. Moreover, the the active and passive beamforming subproblems are solved by employing successive convex approximation. Numerical results unveil that: 1) ME-STARS significantly improves the secrecy performance compared to the conventional STARS with fixed-position elements; and 2) The secrecy rate achieved by the ME-STARS gets saturated within limited movable region size.

Figures

Figures reproduced from arXiv: 2507.14804 by the authors.

Figure 1
Figure 1. Illustration of the ME-STARS-aided downlink MISO commu [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. The spatial angles at the BS and the STARS. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Convergence behavior of the proposed algorithms. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (2 more)
Figure 6
Figure 6. Figure 6: Sum secrecy rate versus normalized STARS region size. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 5
Figure 5. Figure 5: Sum secrecy rate versus number of STARS MEs. [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

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