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REVIEW 1 major objections 1 minor 23 references

Accounting for known faulty elements in reconfigurable holographic surfaces allows optimization of the remaining elements to reduce sensing error while meeting communication requirements.

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T0 review · grok-4.3

2026-06-28 09:17 UTC pith:XL7OCZS5

load-bearing objection The paper's main addition is fault modeling for RHS-ISAC via MCRB and SINR, but the 13.7% gain only holds under perfect knowledge of fixed, uncontrollable faulty elements. the 1 major comments →

arxiv 2606.03013 v1 pith:XL7OCZS5 submitted 2026-06-02 eess.SP

Fault-Aware Design for Reconfigurable Holographic Surface-Aided ISAC Systems

Lu Wang , Mohamadreza Delbari , Gui Zhou , Luis F. Abanto-Leon , Matthias Hollick , Vahid Jamali This is my paper
classification eess.SP
keywords reconfigurable holographic surfaceintegrated sensing and communicationfault-aware designmisspecified Cramer-Rao boundhardware impairmentsblock coordinate descentamplitude optimization
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper addresses the degradation in integrated sensing and communication performance caused by faulty elements on reconfigurable holographic surfaces, whose amplitudes cannot be controlled. It first quantifies the resulting loss by deriving a misspecified Cramer-Rao bound for sensing accuracy and a signal-to-interference-and-noise ratio expression for communication. An optimization problem is then posed that minimizes the sensing bound subject to communication, power, and amplitude constraints on the functional elements. A block coordinate descent algorithm that incorporates majorization-minimization and successive convex approximation is developed to solve the non-convex problem. Simulations confirm that the resulting fault-aware design delivers an average 13.7 percent performance improvement over a benchmark that ignores the faults.

Core claim

By deriving the misspecified Cramer-Rao bound to capture sensing degradation from faulty elements with uncontrollable amplitudes and then minimizing this bound subject to SINR, transmit power, and amplitude constraints on the remaining elements, the proposed block coordinate descent solution with majorization-minimization and successive convex approximation recovers ISAC performance that would otherwise be lost.

What carries the argument

Misspecified Cramer-Rao bound (MCRB) for sensing under amplitude mismatch, used as the objective in a constrained optimization over controllable RHS amplitudes solved by block coordinate descent.

Load-bearing premise

Faulty elements have amplitudes that are completely uncontrollable, known in advance, and can be excluded from the optimization while all other elements remain perfectly controllable.

What would settle it

A simulation or measurement in which faulty-element amplitudes are either unknown or time-varying and the reported performance gain over the fault-unaware benchmark disappears.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Excluding known faulty elements from the optimization reduces the misspecified Cramer-Rao bound while satisfying the SINR and power constraints.
  • The block coordinate descent procedure yields a feasible solution to the non-convex problem that outperforms designs that treat all elements as controllable.
  • The derived MCRB and SINR expressions provide explicit measures of the performance penalty caused by each faulty element.
  • The approach preserves both sensing accuracy and communication quality under the stated hardware impairments.

Where Pith is reading between the lines

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

  • If faulty amplitudes can be estimated rather than assumed known, the same optimization framework could be run periodically to adapt to newly detected faults.
  • The same misspecification-handling technique may apply to other reconfigurable surfaces when only a subset of elements can be controlled.
  • Hardware experiments that inject controlled amplitude faults would test whether the perfect controllability assumption for non-faulty elements holds in practice.
  • Allowing a known fraction of manufacturing defects could lower the cost of large holographic surfaces without proportional performance loss.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

1 major / 1 minor

Summary. The paper addresses hardware impairments in reconfigurable holographic surface (RHS)-aided integrated sensing and communication (ISAC) systems by deriving the misspecified Cramér-Rao bound (MCRB) for sensing performance and the signal-to-interference-and-noise ratio (SINR) for communication under faulty RHS elements with uncontrollable amplitudes. It formulates a non-convex optimization problem to minimize the MCRB subject to SINR, transmit power, and RHS amplitude constraints, and solves it via a block coordinate descent algorithm incorporating majorization-minimization and successive convex approximation. Simulations report an average 13.7% performance gain over a fault-unaware benchmark.

Significance. If the modeling assumptions hold, the work contributes a fault-aware optimization framework for practical RHS-aided ISAC, extending standard signal models with MCRB to capture misspecification from faults. The algorithmic solution for the non-convex problem and the quantified gain demonstrate a concrete approach to mitigating hardware impairments, which is relevant for energy-efficient ISAC deployments.

major comments (1)
  1. [Abstract; system model and MCRB derivation sections] The central performance gain of 13.7% (reported in the abstract) is obtained under the assumption that faulty RHS element locations and their fixed uncontrollable amplitudes are known perfectly a priori; this enters the MCRB derivation (to model the misspecification) and the amplitude constraints in the optimization. No sensitivity analysis or discussion is provided for cases of imperfect fault knowledge or residual controllability, which would invalidate the MCRB as a bound and alter the optimized performance.
minor comments (1)
  1. [Abstract] The abstract states the 13.7% gain from simulations but provides no details on the number of Monte Carlo runs, error bars, or sensitivity to fault probability/model parameters; these should be added for reproducibility.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address the single major comment point by point below, agreeing where the observation is accurate and outlining planned revisions.

read point-by-point responses

  1. Referee: [Abstract; system model and MCRB derivation sections] The central performance gain of 13.7% (reported in the abstract) is obtained under the assumption that faulty RHS element locations and their fixed uncontrollable amplitudes are known perfectly a priori; this enters the MCRB derivation (to model the misspecification) and the amplitude constraints in the optimization. No sensitivity analysis or discussion is provided for cases of imperfect fault knowledge or residual controllability, which would invalidate the MCRB as a bound and alter the optimized performance.

    Authors: We agree that the 13.7% gain is derived under the assumption of perfect a priori knowledge of faulty element locations and fixed amplitudes; this is stated in the system model and is required for both the MCRB (to capture the exact misspecification) and the amplitude constraints. The fault-aware design framework presupposes that faults have been detected. We acknowledge that the manuscript provides neither sensitivity analysis nor discussion of imperfect fault knowledge or residual controllability. In the revised version we will add a dedicated paragraph in the discussion section explaining that imperfect knowledge would necessitate robust or distributionally robust formulations instead of the current MCRB, and that such extensions lie beyond the present scope. This revision directly addresses the concern while preserving the validity of the reported results under the stated assumptions. revision: yes

Circularity Check

0 steps flagged

No circularity: MCRB/SINR derivations and optimization follow standard models without self-referential reduction

full rationale

The paper derives the misspecified Cramer-Rao bound (MCRB) for sensing and the SINR for communication from standard signal models to quantify faulty RHS element impact, then formulates a non-convex optimization minimizing MCRB subject to SINR, power, and amplitude constraints, solved via block coordinate descent with majorization-minimization and successive convex approximation. These steps rely on external signal processing theory rather than reducing by construction to author-defined fitted parameters, self-citations, or ansatzes. The reported 13.7% gain is a simulation outcome under the stated model assumptions, not a tautological output of the inputs. No load-bearing step matches the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The approach relies on standard communication theory bounds and convex optimization approximations whose detailed assumptions are not provided.

pith-pipeline@v0.9.1-grok · 5765 in / 1167 out tokens · 20700 ms · 2026-06-28T09:17:40.422965+00:00 · methodology

0 comments
read the original abstract

Reconfigurable holographic surface (RHS)-aided integrated sensing and communication (ISAC) systems hold great promise for achieving both sensing and communication with low hardware costs and high energy efficiency. However, existing works largely overlook practical hardware impairments in RHSs, particularly faulty RHS elements with uncontrollable amplitudes, which degrade system performance if left unaddressed. This work aims to fill the gap by i) quantifying the impact of faulty RHS elements on ISAC performance and ii) optimizing the functional RHS elements to preserve the ISAC performance. Specifically, we derive the misspecified Cramer-Rao bound (MCRB) for sensing and the signal-to-interference-and-noise ratio (SINR) for communication to measure the performance loss caused by faulty elements. We then formulate an optimization problem that minimizes MCRB, subject to constraints on SINR, transmit power budget, and RHS amplitude. The high non-convexity of the formulated problem poses a significant challenge, which we address by reformulating and proposing a block coordinate descent-based solution incorporating majorization-minimization and successive convex approximation techniques. Simulation results verify that the proposed approach achieves an average 13.7% performance gain compared to the fault-unaware benchmark.

Figures

Figures reproduced from arXiv: 2606.03013 by Gui Zhou, Luis F. Abanto-Leon, Lu Wang, Matthias Hollick, Mohamadreza Delbari, Vahid Jamali.

Figure 1
Figure 1. Figure 1: RHS-aided ISAC system with faulty elements. unexplored. This work aims to fill this research gap, focusing on mitigating the negative impact of faulty RHS elements on the ISAC system. To realize this goal, we optimize the amplitudes of the functional RHS elements to improve both sensing and communication performances. Specifically, we derive the misspecified Cramer-Rao bound (MCRB) and ´ signal-to-interfer… view at source ↗
Figure 2
Figure 2. Figure 2: Convergence performance of the pro￾posed algorithm. 10% 20% 30% 40% 50% 60% faulty RHS elements ratio 0.05 0.1 0.15 0.2 0.25 MCRB/CRB Perfect RHS - LB Faulty RHS - proposed Faulty RHS - naive Faulty RHS - random [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

discussion (0)

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Reference graph

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