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REVIEW 4 major objections 5 minor 31 references

Securing RIS-Aided Wireless Networks Against Full Duplex Active Eavesdropping

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A proposed algorithm jointly tunes a base station's beamformer and an RIS's phase shifts to maximize the secrecy rate against a full-duplex attacker that both eavesdrops and jams.

desk verdict A legitimate new scenario with standard tools, but the printed derivations have load-bearing errors—most importantly Eq. (37a)—so the central optimality claim is not supported as written; it is fixable and deserves a serious referee. read the letter →

arxiv 2411.17830 v1 pith:4UFJSFJ3 submitted 2024-11-26 cs.IT cs.ETcs.SYeess.SYmath.IT

Atefeh Zakeri , S. Mohammad Razavizadeh This is my paper · ORCID
classification cs.ITcs.ETcs.SYeess.SYmath.IT
keywords physicallayersecurityreconfigurableintelligentsurfacefull-duplexactiveeavesdroppingsecrecyratealternatingoptimizationbeamformingdesignjamming
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 studies a base station with multiple antennas serving a single user with the help of a reconfigurable intelligent surface (RIS), while a full-duplex active attacker both listens and transmits jamming signals. It claims that jointly optimizing the base station's beamforming vector and the RIS's phase shifts maximizes the secrecy rate, defined as the difference between the user's achievable rate and the attacker's wiretap rate. The proposed method splits this non-convex problem into two sub-problems solved iteratively by alternating optimization, with the active beamforming step handled through a weighted MMSE reformulation and the passive phase-shift step through semidefinite relaxation. A sympathetic reader would care because an attacker that both eavesdrops and jams is a harsher and more realistic threat than a passive eavesdropper, and the numerical results quantify both the damage done by jamming and the value of the RIS in mitigating it.

What carries the argument

The central object is the effective channel that bundles the direct and RIS-reflected links at each receiver: at the user, $\mathbf{h}_{Bu}^H + \mathbf{h}_{Iu}^H \boldsymbol{\theta} \mathbf{H}_{BI}$; at the eavesdropper, $\mathbf{H}_{Be} + \mathbf{H}_{Ie} \boldsymbol{\theta} \mathbf{H}_{BI}$. The attacker's full-duplex jamming signal enters as extra interference terms, $|(\mathbf{g}_{eu}^H + \mathbf{h}_{Iu}^H \boldsymbol{\theta} \mathbf{G}_{eI})\mathbf{v}|^2$ at the user and $\|\mathbf{H}_{Ie} \boldsymbol{\theta} \mathbf{G}_{eI} \mathbf{v}\|^2$ at the eavesdropper. Two reformulation tools carry the optimization: Lemma 1, the identity $-\log \det(\mathbf{E}) = \max_{\mathbf{S} \succeq 0} -\operatorname{Tr}(\mathbf{S}\mathbf{E}) + \log|\mathbf{S}| + N$ with optimum $\mathbf{S} = \mathbf{E}^{-1}$, which turns the log-ratio secrecy objective into a tractable weighted mean-square-error form; and the variable change $\boldsymbol{\phi} = [1, \varphi]^H$ that rewrites every RIS-coupled quadratic term as $\operatorname{Tr}(\mathbf{E}_{\cdot} \boldsymbol{\Theta})$ with $\boldsymbol{\Theta} = \boldsymbol{\phi}\boldsymbol{\phi}^H$, allowing the unit-modulus phase constraints to be handled by semidefinite relaxation and Gaussian randomization.

What would settle it

A concrete observation that would settle the central claim: run the proposed alternating-optimization algorithm with imperfect channel estimates for the attacker's links (or with an attacker that changes $\mathbf{v}$ during transmission) and check whether the predicted secrecy rate still holds; if the rate collapses, the perfect-CSI and fixed-$\mathbf{v}$ assumptions are the load-bearing part of the design.

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Extended reading notes

Core claim

The central claim is that the secrecy rate $R_s = [R_u - R_e]^+$ can be maximized by an alternating optimization algorithm that jointly designs the base station transmit beamforming vector $\mathbf{w}$ and the RIS phase-shift matrix $\boldsymbol{\theta}$. The algorithm converts the rate expressions into a weighted MMSE form, alternates between updating auxiliary weights and the beamformer for fixed RIS phases, and then solves the RIS phase sub-problem by semidefinite relaxation with Gaussian randomization. Under the assumption of perfect channel state information for every link, including the attacker's channels, and with the attacker's jamming beamforming vector $\mathbf{v}$ treated as fixed and known, the procedure yields a design that increases the secrecy rate as the BS power, the number of BS antennas, and the number of RIS elements grow, and decreases as the number of attacker antennas grows.

Load-bearing premise

The scheme requires the legitimate system to know perfectly every channel in the network, including the channels to and from the attacker, and to know the attacker's jamming beamforming vector $\mathbf{v}$, which is treated as fixed and known throughout.

Editorial extensions

If this is right

  • If the algorithm is correct, it gives a concrete design procedure for securing an RIS-aided link against an attacker that simultaneously eavesdrops and jams, under the perfect-CSI assumption.
  • The wider secrecy-rate gap between jammed and non-jammed scenarios as RIS elements increase means that the value of deploying an RIS grows precisely in the presence of active attacks.
  • Secrecy rate scales with BS transmit power and the number of BS antennas, so conventional spatial and power resources remain effective defenses under jamming.
  • Each additional attacker antenna lowers the secrecy rate, so the legitimate network must account for the attacker's hardware scale when sizing its own array.

Reading between the lines

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

  • Beyond the paper: the perfect-CSI and fixed-attacker-beamforming assumptions are the load-bearing premises; a natural extension is a robust formulation that only knows the attacker's channels within an error ball, or a game-theoretic version where the attacker adapts $\mathbf{v}$ to the legitimate beamformer.
  • Beyond the paper: the WMMSE-plus-SDR machinery could be transplanted to neighboring problems such as multi-user RIS-aided secrecy, weighted sum secrecy rate with artificial noise, or RIS-aided covert communication, where similar log-ratio objectives appear.
  • Beyond the paper: because the numerical results rest on a simulated Rician channel model, a testable extension is to verify the reported trends (secrecy rate versus RIS elements and attacker antennas) under different channel distributions or in a measurement campaign.
  • Beyond the paper: the paper assumes perfect self-interference cancellation at the full-duplex attacker; relaxing that to realistic residual self-interference could change the effective jamming power and is worth checking.
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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 / 5 minor

Summary. The paper studies an RIS-aided downlink in which a full-duplex active eavesdropper simultaneously overhears the legitimate transmission and sends jamming signals. The authors formulate a secrecy-rate maximization over the BS beamforming vector and the RIS phase shifts, and they propose an alternating optimization algorithm that combines WMMSE-based active beamforming with semidefinite relaxation for the RIS phase design. Numerical results are provided for secrecy rate versus transmit power, numbers of antennas, and RIS elements.

Significance. If the derivation and algorithm were correct, the paper would offer a useful design procedure for a timely scenario: physical-layer security against active eavesdropping in RIS-assisted networks. The use of AO with WMMSE and SDR is a standard and reasonable approach, and the inclusion of direct channels between BS and user/eavesdropper is more realistic than in some related work. However, the central claim that Algorithm 1 maximizes the secrecy rate is not supported as printed because the passive-beamforming subproblem is derived with incorrect signs and optimization direction, and the active-beamforming reformulation has a related equivalence error. The paper does not provide code or machine-checked proofs, and its numerical claims rest on the flawed derivations.

major comments (4)
  1. [III-B, Eq. (37a)] The passive-beamforming subproblem is not equivalent to the secrecy-rate objective. Starting from (34), Rs = log(T1) - log(T2) - log(T3) + log(T4), and applying Lemma 1 to -log(T2) and -log(T3) gives a maximization over ε4, ε5 of log(T1) - ε4 T2 + log ε4 + log(T4) - ε5 T3 + log ε5. In contrast, (37a) is written as a minimization, the first log term has a negative sign and a duplicated σ_u^2, the matrix EBIu appears instead of EBID, the ε4 and ε5 terms have signs opposite to those required by Lemma 1, and -log(ε4) - log(ε5) should be +log(ε4) + log(ε5). Consequently, step 2d of Algorithm 1 solves a different problem, and the printed iteration is not guaranteed to increase Rs. This is a load-bearing error for the paper's central claim.
  2. [III-A, Eq. (16)] The WMMSE reformulation of A3 drops the identity matrix. The expression in (16), A3 = max_{ε3>0} log(ε3) - Tr[ε3((HIeI vv^H H_IeI^H) + (HBeI ww^H H_BeI^H))], corresponds to -log det of the sum without the identity, whereas the actual eavesdropper rate term is log det(I + HIeI vv^H H_IeI^H + HBeI ww^H H_BeI^H). Although the update for ε3 in (24) correctly includes I, the objective used in the w-subproblem (25)-(26) is not equivalent to Re, so the active-beamforming update does not maximize the original secrecy rate either.
  3. [II, Eqs. (2) and (4)] The assumption of perfect self-interference cancellation at the full-duplex eavesdropper is inconsistent with the eavesdropper rate expression. If the eavesdropper perfectly cancels its own jamming signal as stated before (2), then the term HIe θ GeI v a in (2) should be removed before defining Re, and the denominator in (4) should not contain ∥(HIe θ GeI)v∥^2. As printed, the jamming signal appears as self-interference in the eavesdropper's received signal, contradicting the stated assumption and biasing the numerical results. The model should be corrected, e.g., by removing that term or by explicitly modeling residual self-interference.
  4. [III-C, Algorithm 1 and Abstract] The paper's abstract and conclusion state that the proposed algorithm maximizes the secrecy rate, but no global optimality proof is given and the alternating algorithm is at best a local method. Moreover, the convergence criterion in Algorithm 1 divides by Rs^{t-1}, which can be zero, and monotonic increase of Rs is not established and does not follow from the incorrect subproblem (37a). The authors should either prove convergence of the corrected iteration or soften the optimality claim.
minor comments (5)
  1. [II, Eqs. (1)-(4)] The symbol θ is used both for the diagonal reflection matrix and for the vector of phase shifts; the notation should be made consistent, e.g., by writing θ = diag(φ) and using a distinct symbol for the augmented vector in (29b).
  2. [III-B, Eq. (38)] There is a misplaced parenthesis in the expression for ε4: it should read ε4 = 1 / (Tr(EeIeI Θ) + Tr(EBIe Θ) + σ_e^2), not Tr(EBIe Θ + σ_e^2).
  3. [III-A, Eq. (10a)] The displayed equation for the active beamforming subproblem contains corrupted placeholder tokens and should be typeset cleanly; the readable portions omit the '+1' terms in the logarithms that are needed for the WMMSE equalities.
  4. [III-C, complexity analysis] The complexity expression O(T2(((K^2 + 2Nr^3) + T1(L+1)^4.5))log(ϵ)) mixes an inner iteration count T1 and an outer iteration count T2 in a way that should be clarified, and the use of log(ϵ) inside the big-O is unusual.
  5. [IV, Fig. 4 description] The sentence 'without reflective elements, the secrecy rate remains constant regardless of the number of such elements added' is confusing; it presumably means the 'without RIS' benchmark curve is flat, which should be stated more clearly.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the optimization derivation is self-contained and does not reduce to its own inputs.

full rationale

The paper's central claim is that the proposed alternating optimization algorithm maximizes the secrecy rate Rs defined in Eq. (5) by jointly optimizing the BS beamformer w and the RIS phase shifts θ. The derivation chain is self-contained: Rs is defined directly from the channel model in Eqs. (3)-(4), the active-beamforming subproblem is reformulated through the standard WMMSE/Lemma 1 machinery in Eqs. (11)-(25), and the passive-beamforming subproblem is expressed in terms of the reparameterized traces in Eqs. (28)-(34) before applying the same logarithmic transformation. There are no fitted parameters renamed as predictions, no externally calibrated inputs, and no benchmark quantities that the method is tuned to reproduce. The numerical results in Section IV evaluate the proposed algorithm itself and compare it against benchmark schemes, so those curves are outputs of the derivation rather than predictions forced by construction. The reference list contains no self-citations by the authors, and the external tools cited (WMMSE in [28], Lemma 1 in [29], Gaussian randomization in [22], [30]) are standard results that do not depend on the present paper's conclusions. The skeptic's concern that Eq. (37a) does not correctly match the reformulated secrecy rate is an internal correctness or derivational-accuracy issue, not a circularity issue: flagging a sign error or inconsistency does not show that the claimed result is equivalent to its inputs by definition. Applying the hard rules, none of the seven circularity patterns is present, so the appropriate finding is no significant circularity with score 0.

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

The central claim rests on standard assumptions about perfect channel knowledge at the legitimate system, perfect self-interference cancellation at the attacker, and a fixed known jamming strategy; the SDR rank-recovery step is a heuristic. There are no fitted free parameters: all simulation parameters (path-loss exponents, Rician factor, noise variances) are conventional choices, not quantities estimated from data.

assumptions (6)
  • domain assumption Perfect CSI of all channels, including channel state information of the eavesdropper's receive and transmit channels.
    Invoked in Section II, 'we assume that the Channel State Information (CSI) of all the channels are perfectly known.' This is load-bearing because the algorithm needs exact H_Be, H_BI, h_Bu, h_Iu, H_Ie, g_eu, G_eI to compute the beamformers.
  • domain assumption Perfect self-interference cancellation at the full-duplex active eavesdropper.
    Stated in Section II: 'assumed to have perfect self-interference cancellation, as described in [24], [25].' This removes the attacker's own jamming signal from its received signal, except for reflections via the RIS.
  • domain assumption The attacker's jamming beamforming vector v is fixed and known to the legitimate system.
    Implicit in problem (6), where only w and θ are optimized and v appears as a constant in Ru and Re. The legitimate system must know v to evaluate the secrecy rate, but the paper does not discuss how this knowledge is obtained.
  • domain assumption Semidefinite relaxation followed by Gaussian randomization yields a feasible, near-optimal rank-one RIS phase solution.
    In Section III-B, the rank-one constraint in (37c) is relaxed and Gaussian randomization is applied 'if the rank-1 constraint is not satisfied'. This is a heuristic with no performance guarantee for this specific problem.
  • standard math The WMMSE equivalence (Lemma 1, cited from [29]) holds for the scalar and matrix rate expressions used in the reformulation.
    The derivation in (14)-(16) relies on the max-log equivalence from [29]; the paper uses it without proof, which is acceptable as a standard result.
  • standard math The problem is NP-hard, so the proposed alternating optimization is a heuristic rather than an optimal solver.
    Section III states 'this is a Non-deterministic Polynomial-time hard (NP-hard) problem' and therefore proposes a low-complexity AO algorithm; no proof of global optimality or even stationarity is given.

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Pith. "Pith review of Securing RIS-Aided Wireless Networks Against Full Duplex Active Eavesdropping." pith.science (2026). https://pith.science/paper/4UFJSFJ3

@misc{pith2026241117830,
  author       = {Pith},
  title        = {Pith review of: Securing RIS-Aided Wireless Networks Against Full Duplex Active Eavesdropping},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4UFJSFJ3}},
  note         = {Machine review of arXiv:2411.17830}
}
read the original abstract

This paper investigates the physical layer security of a Reconfigurable Intelligent Surface (RIS)-aided wireless network in the presence of full-duplex active eavesdropping. In this scenario, the RIS cooperates with the Base Station (BS) to transfer information to the intended user while an active attacker attempts to intercept the information through a wiretap channel. In addition, the attacker sends jamming signals to interfere with the legitimate user's reception of the signal and increase the eavesdropping rate. Our objective is to maximize the secrecy rate by jointly optimizing the active and passive beamformers at the BS and RIS, respectively. To solve the resulting non-convex optimization problem, we propose a solution that decomposes it into two disjoint beamforming design sub-problems solved iteratively using Alternating Optimization (AO) techniques. Numerical analysis is conducted to evaluate the impacts of varying the number of active attacking antennas and elements of the RIS on the secrecy performance of the considered systems under the presence of jamming signals sent by the attacker. The results demonstrate the importance of considering the impact of jamming signals on physical layer security in RIS-aided wireless networks. Overall, our work contributes to the growing body of literature on RIS-aided wireless networks and highlights the need to address the effects of jamming and active eavesdropping signals in such systems.

Figures

Figures reproduced from arXiv: 2411.17830 by the authors.

Figure 1
Figure 1. System Model of the RIS-assisted Communication Network Against an Active Eavesdropper. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Achievable Secrecy Rate vs. the Transmit Power, [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figure 3
Figure 3. Achievable Secrecy Rate vs. the number of BS’s Antennas, [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Achievable Secrecy Rate vs. the number of Reflecting Elements of the RIS, [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]
Figure 5
Figure 5. Figure 5: Convergence of the Proposed BCD Algorithm. [PITH_FULL_IMAGE:figures/full_fig_p017_5.png]

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