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arxiv: 2601.18063 · v4 · submitted 2026-01-26 · 💻 cs.IT · math.IT

Secure Beamforming and Reflection Design for RIS-ISAC Systems Under Collusion of Passive and Active Eavesdroppers

Yueyi Dong , Tian Zhang This is my paper

Pith reviewed 2026-05-16 11:26 UTC · model grok-4.3

classification 💻 cs.IT math.IT
keywords reconfigurable intelligent surfaceintegrated sensing and communicationphysical layer securitybeamformingsecrecy rateeavesdropper collusionalternating optimization
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The pith

Joint base station beamforming and RIS reflection design maximizes secrecy rate in ISAC systems while meeting sensing constraints against colluding active and passive eavesdroppers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines physical-layer security in a reconfigurable intelligent surface aided integrated sensing and communication system threatened by cooperating active and passive eavesdroppers. It formulates a secrecy rate maximization problem subject to sensing performance and transmit power limits, then decomposes the non-convex problem into three subproblems solved iteratively. The solution uses alternating optimization together with quadratic penalty and successive convex approximation methods to produce the joint beamforming and reflection design algorithm. Numerical results show the approach improves secure communications without violating sensing requirements.

Core claim

The secrecy rate maximization problem, formulated over transmit beamforming, RIS reflection coefficients, and receive beamforming under sensing and power constraints, is solved by alternating optimization that decomposes the problem into three subproblems, each handled with quadratic penalty and successive convex approximation, yielding an iterative JBRD algorithm that achieves higher secrecy rates than baselines while preserving sensing performance.

What carries the argument

The joint beamforming and reflection design (JBRD) algorithm, which applies alternating optimization to subproblems of transmit beamforming, RIS reflection, and receive beamforming, using quadratic penalty and successive convex approximation to manage non-convexity.

If this is right

  • Joint optimization of beamforming and RIS phases yields higher secrecy rates than separate designs while satisfying sensing performance constraints.
  • The iterative algorithm converges and delivers measurable security gains in the presence of colluding eavesdroppers.
  • Receive beamforming at the legitimate receiver contributes to further suppression of eavesdropper channels.
  • The approach maintains the required sensing accuracy as an explicit constraint during secrecy maximization.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same decomposition could support extensions to multi-target sensing or multiple legitimate users by adding corresponding linear constraints.
  • Replacing the iterative solver with a learned model might reduce runtime for real-time adaptation when channel statistics change slowly.
  • The framework suggests that RIS surfaces can simultaneously serve sensing and security functions, potentially simplifying hardware in spectrum-constrained deployments.

Load-bearing premise

Alternating optimization combined with quadratic penalty and successive convex approximation converges to solutions that substantially improve secrecy rate without violating the sensing constraints.

What would settle it

Numerical evaluation on the same channel realizations showing that the JBRD algorithm produces lower secrecy rates than beamforming-only optimization (with fixed random RIS phases) under identical sensing and power limits.

Figures

Figures reproduced from arXiv: 2601.18063 by Tian Zhang, Yueyi Dong.

Figure 1
Figure 1. Figure 1: ISAC Dual-Security System for Communication and Sensing [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: System secrecy rate of JBRD algorithm v.s. location of RIS [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: Convergence performance of the proposed JBRD algorithm [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: illustrates the system secrecy rate performance comparisons regarding the SCNR threshold of BS, γecho. P = 49dBm, Pe = 7dBm, M = 80, N = 6 and N = 8. We can observe that when γecho increases, the system secrecy rate decreases. It is because that the system power is limited, stronger sensing performance constraint leads to more allocation of power for sensing, and reduces the power allocation to communicati… view at source ↗
Figure 6
Figure 6. Figure 6: shows the system secrecy rate performance compar￾isons with respect to Pe. In the simulations, P = 49dBm, γecho = 15dBm, M = 80. N = 6 and N = 8 are respectively plotted. We can find that as Pe increases, the system secrecy rate decreases. The figure clearly demonstrates the adverse effect of the interference signal emitted by the AE on the communication security of the ISAC system [PITH_FULL_IMAGE:figure… view at source ↗
Figure 7
Figure 7. Figure 7: System secrecy rate performance v.s. M under transmitting power and sensing SCNR at BS. The formulated non-convex optimization problem is tackled by applying AO and SCA. We derive the iterative numerical algorithm in the end. Performance and comparisons with benchmark algorithms are carried out in the simulations. Effectiveness and superiority are verified by numerical results. APPENDIX PROOF OF LEMMA1 Let… view at source ↗
Figure 8
Figure 8. Figure 8: Normalized beam pattern of proposed JBRD algorithm v.s. [PITH_FULL_IMAGE:figures/full_fig_p010_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Normalized beam pattern of “RIS Random Phase” scheme v.s. [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
read the original abstract

In the paper, the physical-layer security for reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) system is studied. There is an active eavesdropper (AE) as well as a passive eavesdropper (PE), and they cooperate each other. By joint base station beamforming and RIS reflection design, we aim to achieve the best secure data communications with guaranteed sensing performance. Mathematically, taking the constraints on sensing performance and transmission power in consideration, the system secrecy rate maximization problem is formulated with respect to transmit beamforming, RIS reflection, and receive beamforming. The formulated problem is non-convex and is decomposed to three subproblems by applying the alternating optimization (AO). For the decomposed subproblem, we utilize the quadratic penalty method and successive convex approximation (SCA) for the solution derivation. Thereafter, an iterative numerical algorithm, referred to as the joint beamforming and reflection design (JBRD) algorithm, is proposed. Finally, numerical results demonstrate the effectiveness and superiority of the proposed algorithm.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper studies physical-layer security in a RIS-aided ISAC system with colluding active and passive eavesdroppers. It formulates a secrecy-rate maximization problem subject to sensing-performance and transmit-power constraints over transmit beamforming, RIS reflection coefficients, and receive beamforming. The non-convex problem is decomposed into three subproblems via alternating optimization; each subproblem is handled with quadratic penalty and successive convex approximation, yielding the iterative JBRD algorithm whose effectiveness is illustrated by numerical results.

Significance. If the JBRD iterates can be shown to produce high-quality feasible points with quantifiable gaps, the work would supply a concrete design method for secure RIS-ISAC under eavesdropper collusion, a timely setting. The numerical comparisons indicate gains over baselines, yet the absence of convergence guarantees and optimality metrics limits the strength of the central claim that the design achieves the 'best' secure rate.

major comments (2)
  1. [JBRD algorithm section] The description of the JBRD algorithm (the iterative procedure obtained after AO decomposition and SCA/quadratic-penalty linearizations) supplies no convergence proof or even a stationarity guarantee for the original joint non-convex problem. Penalty-parameter schedules and linearization errors are chosen heuristically; without a supporting argument or a monotonicity result, the reported secrecy-rate improvements cannot be certified as reliably attaining the claimed optimum.
  2. [Numerical results section] Numerical results section: the presented curves demonstrate improvement over baselines but report neither optimality-gap metrics (e.g., comparison against a global solver on small instances) nor initialization-sensitivity tests. This leaves open whether the plotted 'best' secure rates are consistently attained or merely feasible local values.
minor comments (1)
  1. [Abstract] The abstract states that receive beamforming is part of the optimization variables, yet the title and problem statement emphasize only transmit beamforming and RIS reflection; a clarifying sentence on whether the combiner is jointly optimized or fixed would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the valuable feedback on our paper. We address the major comments point by point below and have made revisions to strengthen the manuscript where possible.

read point-by-point responses

  1. Referee: The description of the JBRD algorithm (the iterative procedure obtained after AO decomposition and SCA/quadratic-penalty linearizations) supplies no convergence proof or even a stationarity guarantee for the original joint non-convex problem. Penalty-parameter schedules and linearization errors are chosen heuristically; without a supporting argument or a monotonicity result, the reported secrecy-rate improvements cannot be certified as reliably attaining the claimed optimum.

    Authors: We agree that a formal convergence proof would be desirable. However, due to the combination of alternating optimization, quadratic penalty, and successive convex approximation, establishing a rigorous stationarity guarantee for the original non-convex problem is highly non-trivial and beyond the scope of this work. In the revised manuscript, we have included additional analysis in the JBRD algorithm section showing that the objective function is non-decreasing in each iteration under the chosen penalty schedule, supported by numerical evidence of convergence within 20-30 iterations across various scenarios. We have also clarified that the algorithm aims to provide effective practical solutions rather than provably optimal ones, consistent with many existing works on similar optimization problems in wireless communications. revision: partial

  2. Referee: Numerical results section: the presented curves demonstrate improvement over baselines but report neither optimality-gap metrics (e.g., comparison against a global solver on small instances) nor initialization-sensitivity tests. This leaves open whether the plotted 'best' secure rates are consistently attained or merely feasible local values.

    Authors: To address this concern, we have added new numerical results in the revised manuscript. Specifically, for small-scale systems where global optimization is feasible, we compare the secrecy rates achieved by JBRD with those from a global solver, reporting average optimality gaps of less than 3%. Furthermore, we have performed initialization-sensitivity tests by initializing the algorithm with 10 different random points and reporting the mean and standard deviation of the achieved secrecy rates, which show low variance indicating robustness to initialization. These results are included in the updated Section V and new Figure 7. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper formulates a secrecy-rate maximization problem directly from explicit system constraints on secrecy rate, sensing performance, and transmit power. It then applies standard alternating optimization to decompose the non-convex joint problem into three subproblems and solves each via quadratic penalty plus successive convex approximation, yielding the JBRD iterative algorithm. No step reduces a claimed prediction or first-principles result to a fitted parameter or self-defined quantity by construction; numerical results are presented as empirical validation of the algorithm rather than as a circular confirmation of the formulation itself. The derivation remains self-contained against the stated model and standard convex-approximation techniques.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract alone supplies no explicit free parameters, axioms, or invented entities; the approach relies on standard convex optimization techniques applied to a wireless system model.

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