Temperature-aware Optimization of Liquid Crystal Reconfigurable Intelligent Surfaces: Physics-based Modeling and Robust Design

Arash Asadi; Bowu Wang; Mohamadreza Delbari; Vahid Jamali

REVIEW 3 major objections 5 minor 41 references

Temperature-aware Optimization of Liquid Crystal Reconfigurable Intelligent Surfaces: Physics-based Modeling and Robust Design

T0 review · 3 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read The paper claims that LC-RIS phase drift under temperature follows one multiplicative power law, and that optimizing against it—over location zones, not full CSI—keeps secrecy rates temperature-blind designs lose.

desk verdict Competent extension of the authors' ICC 2025 work — the temperature-robust static design is a genuinely useful idea — but the simulations validate the algorithms against the same temperature model they assume, so the secrecy-rate gains are not yet tested against hardware. read the letter →

arxiv 2607.22141 v1 pith:UJIYZQ2H submitted 2026-07-24 eess.SP

Mohamadreza Delbari , Bowu Wang , Arash Asadi , Vahid Jamali This is my paper · ORCID

classification eess.SP

keywords liquidcrystalreconfigurableintelligentsurfacetemperaturedependencephysicallayersecuritysecrecyratephase-shiftoptimizationmmWaverobustdesign

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 sets out to show that the main failure mode of liquid-crystal reconfigurable intelligent surfaces in secure wireless links is thermal: as temperature rises, the achievable phase-shift range compresses below 2π, and any phase configuration computed at a reference temperature silently degrades. It proposes a physics-based model in which the entire voltage-to-phase response at any temperature is the reference-temperature response scaled by a single power-law factor, ((Tc−T)/(Tc−Tr))^β. On top of that model it builds two phase-shift designs—one that adapts to a known temperature and one that requires no temperature knowledge at all—optimized over the possible location zones of the legitimate user and eavesdropper rather than full channel state information. The central claim is that these designs keep the worst-case secrecy rate (the excess of the legitimate user's rate over the eavesdropper's) nearly flat across temperature, while designs that ignore temperature leak signal to eavesdroppers as the temperature deviates from the reference. A sympathetic reader would care because this gives LC-RIS hardware—otherwise energy-efficient and scalable—a tractable thermal behavior that can be engineered around in secure mmWave systems.

What carries the argument

The central object is the multiplicative temperature-scaling law of Eq. (16), obtained from the standard empirical power-law approximation to the mean-field order parameter of nematic liquid crystals; it does the work of turning the physics of thermal disorder into a parameter-free engineering rule: take the reference-temperature phase response and scale it by ((Tc−T)/(Tc−Tr))^β. On the algorithmic side, the key mechanism is the low-complexity design's log-sum-exp (LSE) surrogate for the worst-case secrecy rate over the joint spatial/temperature uncertainty set, together with a piecewise wrapping function that projects unconstrained phase angles back into the temperature-limited range. This

What would settle it

Measure the phase shift of a real LC-RIS unit cell across several bias voltages at multiple temperatures (for example −20, 0, 10, 25, and 40 °C) and check whether the ratio ω(V,T)/ω(V,Tr) equals ((Tc−T)/(Tc−Tr))^β independent of V. A single fitted exponent across voltages would support the design; a V-dependent ratio, or a ratio that changes shape near the clearing temperature, would falsify the multiplicative model that the secrecy-rate gains rest on.

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Extended reading notes

Core claim

The load-bearing result is the paper's thermal model, Eq. (16): the phase shift of an LC-RIS element at bias voltage V and temperature T is ω(V,T) = ω(V,Tr)·((Tc−T)/(Tc−Tr))^β, where Tr is the reference temperature at which a full 2π range is available and Tc is the clearing temperature at which the liquid crystal becomes isotropic. The model says thermal change does not add a constant phase error; it multiplicatively compresses the whole phase-shift profile, so the maximum tunable range falls below 2π as soon as the temperature exceeds the reference. The paper then formulates secure communication as a worst-case optimization over the possible locations of the legitimate user and the eavesdr

Load-bearing premise

The entire numerical demonstration assumes that real LC-RIS hardware follows the empirical power-law temperature model of Eq. (16) with the same exponent and clearing temperature used by both the algorithm and the 'ground-truth' simulator; no measured phase-versus-temperature data from a real cell are presented, so if the thermal response differs—especially if the phase profile does not scale multiplicatively across all bias voltages—the reported secrecy-rate gains are not de

Editorial extensions

If this is right

Designing LC-RIS phase shifts with the Eq. (16) model keeps the worst-case secrecy rate roughly constant as the operating temperature moves from −20 °C to 40 °C, while temperature-blind designs lose secrecy at both extremes (Figs. 9–11).
The temperature-robust static configuration, computed without any live temperature reading, still achieves a nearly flat secrecy rate across the whole tested range—so thermal sensing hardware and feedback overhead are not mandatory.
The scalable low-complexity algorithm runs in time linear in the number of RIS elements (seconds for N=400) versus cubic for the SDP benchmark (hours), making real-time reconfiguration of very large surfaces feasible.
Coverage by zone rather than by point means the same phase configuration serves a moving legitimate user and resists eavesdropper location uncertainty, which directly reduces CSI acquisition overhead in mmWave systems.
The requirement π<ωmax<2π for the convex reformulation of the phase-range constraint is satisfied by the experimental temperature range considered, so the math aligns with the physics in the operating regime.

Reading between the lines

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

An extension the paper leaves implicit: replacing the uniform worst-case treatment of the user/eavesdropper zones with a probability distribution would let the same LSE surrogate weight high-threat locations more heavily.
The multiplicative form of Eq. (16) suggests temperature compensation could be applied as a post-hoc remapping of any existing LC-RIS phase configuration, rather than a full re-optimization; the paper does not test this transfer.
If the model is confirmed by hardware measurements, the temperature-robust configuration implies an architectural shortcut: configure the RIS once for the local climate range and let base-station beamforming handle fast adaptation, eliminating per-element thermal sensing.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this is a solid, honest engineering paper. The genuinely new pieces are the temperature-robust static design in Section V — one nominal phase profile that keeps secrecy rate nearly flat across a temperature range without live thermal sensing — and the linear-complexity scalable algorithm that makes the adaptive design practical for large RIS. The SDP benchmark is standard but fine, and they ship MATLAB code, which earns real reproducibility credit.

The paper does well by being clear about what it assumes. Eq. (16) is stated as a model, baseline min-phase calibration is flagged, and Lemma 1's uniform-phase assumption is conceded rather than hidden. The complexity analysis is honest, and the LSE-style smoothing over the joint location–temperature uncertainty set is a sensible way to handle the non-smooth worst-case objective.

The load-bearing caveat is exactly what the stress-test note says: the simulated "actual" temperature response is generated from Eq. (16) — the same Haller/Maier-Saupe scaling, the same beta, Tc, Tr — that the algorithms use. So Figs. 8–11 show self-consistency, not hardware accuracy. If a real LC-RIS phase profile does not compress multiplicatively with temperature, or if the ordinary-index baseline drifts, the reported secrecy gains are not demonstrated. That is a validation gap, not a proof error. The fix is either measured phase-vs-temperature data or a clearly framed "performance under the Haller model" claim plus sensitivity analysis to beta, Tc, and non-multiplicative mismatch.

A smaller soft spot: the C2-to-cC2 reformulation for the SDP benchmark rests on a phase-uniformity assumption that is not guaranteed. Since the scalable method does not use that reformulation, it mainly weakens the "upper bound" status of the SDP comparison. Minor to moderate. The relationship to [14] (same group, temperature-resilient LC-RIS) also needs clarification; as written it is unclear how much the robust idea overlaps.

Who should read it: people working on RIS-assisted physical layer security and anyone prototyping LC-RIS control. It deserves a serious referee — the direction is plausible and the code is available. My recommendation: send to peer review; in revision, require at least one out-of-sample check (synthetic model mismatch or existing measured data from [33]/[34] at another temperature) and explicit error bars. With that, it becomes a solid contribution.

Referee Report

3 major / 5 minor

Summary. This paper addresses physical-layer security in an LC-RIS-aided mmWave downlink, where the RIS phase response depends on temperature. The authors first derive a temperature-dependent phase-shift model from the Maier–Saupe mean-field theory and the Haller empirical power law, yielding the multiplicative scaling in Eq. (16). They then formulate a worst-case secrecy-rate maximization problem in which only the spatial zones of the legitimate user and eavesdropper are known, not full CSI. Two phase-shift designs are proposed: a temperature-adaptive design (an SDP benchmark and a low-complexity scalable algorithm) and a temperature-robust design that operates without real-time temperature feedback. Simulations show that the temperature-aware and robust designs maintain a high worst-case secrecy rate as temperature varies, while the temperature-neglecting benchmark degrades away from the reference temperature.

Significance. If the underlying temperature model is trustworthy, the paper makes a useful contribution: it is, to my knowledge, the first treatment of temperature effects on LC-RIS phase shifts in a physical-layer-security context, and it addresses a practical scalability bottleneck by avoiding full CSI and by using a low-complexity, O(N) design. The public release of MATLAB code is a clear strength, as is the use of an SDP benchmark against which the scalable heuristic is compared. The physics-based derivation from the Maier–Saupe/Haller model is plausible and clearly presented. However, the quantitative central claim — that the proposed designs achieve large secrecy-rate gains over temperature-neglecting designs — is currently demonstrated only under the very model the algorithms assume. The paper would be significantly strengthened by measured LC-RIS phase-versus-temperature data or, failing that, by a systematic sensitivity analysis against alternative thermal laws. In its present form, the contribution is best read as a model-based design study whose experimental validation is still required.

major comments (3)

[Section III-B, Eq. (16); Section VI-A] The central load-bearing assumption is Eq. (16): ω(V,T)=ω(V,Tr)·((Tc−T)/(Tc−Tr))^β. In the simulations, the temperature-dependent phase response used as ground truth is generated from this same equation with the same β, Tc, and Tr that the algorithms assume. Consequently, Figs. 8–11 demonstrate in-sample performance: the compensator and the simulated environment share the same multiplicative law. The paper does not report any measured LC-RIS phase-versus-temperature curves, and the assumption that the normalized voltage-phase profile is temperature-invariant is not verified. A real LC cell may exhibit baseline (ordinary-index) drift, element-to-element variation, or a non-multiplicative temperature law. Please provide experimental validation, or at minimum a sensitivity analysis over alternative thermal models and over the stated parameter ranges (β=0.2–0.25, Tc around 95°C), before clai
[Section IV-B1, Lemma 1 and cC2] The SDP method replaces the phase-range constraint C2 with the convex constraint cC2 using Lemma 1, which relies on the phases being approximately uniformly distributed over [0,ωmax] so that a law-of-large-numbers approximation holds. The authors concede in the text that "a uniform distribution is not generally guaranteed" and instead state, based on observations, that N≥50 is sufficient. Because the SDP solution is used as the high-performance benchmark against which the scalable algorithm and the neglected-temperature baseline are compared (Fig. 8), the validity of P5 is load-bearing. If the uniformity assumption is violated, the SDP solution need not satisfy the true constraint C2, and the benchmark may be invalid. Please provide a formal justification under near-field area illumination, or empirically verify the original C2 for the SDP solutions over many random channel realizations
[Section VI-B, Figs. 6 and 11] The performance of the low-complexity algorithm and of the robust design is presented without a convergence guarantee. The authors note that because of the projection in Eq. (45), a strictly monotonic increase is not guaranteed; Fig. 6 shows that most initializations increase the secrecy rate but one should report the fraction of initializations that fail and the variance of the final rate. For the temperature-robust design in Section V, the number M of sampled temperatures is not reported, and no stopping rule or complexity analysis is given for the joint-spatial-thermal LSE iteration. Since the robust design is one of the paper’s two main contributions, please specify M, the convergence criterion, and the robust algorithm’s sensitivity to M.

minor comments (5)

[Algorithm 2, line 20] The output statement writes "s⋆(T)=s(T)"; this should be s⋆(T)←s(T) or similar, to avoid confusion between the optimal output and the current iterate.
[Fig. 11] The legend labels are difficult to parse in the caption ("Optimized Neglected Robust, Scenario 1"). Please use distinct markers/colors and a clearer legend entry for the robust design.
[Section VI-A] The number of discretization points M for the temperature set in the robust design is not stated; please report it for reproducibility.
[Eq. (45)] The piecewise wrapping function is described as a projection, but it is not a Euclidean projection onto the feasible set. Please clarify that it is a heuristic feasibility-restoring map, not an optimal projection.
[Section II-C] The text says the direct link is neglected in algorithm design but included in numerical evaluations, yet Section VI does not clearly state how the blocked-direct-link model is reconciled with the path-loss parameters for the BS-MU link. Please clarify.

Circularity Check

0 steps flagged · score 0.0 of 10

No definitional circularity: the temperature model is imported from external LC physics, and the optimization derivation is self-contained given that model.

full rationale

The central temperature model, Eq. (16), is not defined in terms of the secrecy-rate objective or the optimization output. It is derived from an externally sourced thermodynamic scaling: the Haller power-law approximation S(T)=(1-T/Tc)^β in Eq. (14), the Maier–Saupe self-consistent order parameter in Eq. (13), and the experimental voltage-dependent baseline ω(V,Tr) adapted from Refs. [33] and [34]. The material parameters β=0.25, Tc=95°C, and Tr=10°C used in Section VI are fixed from LC physics, not fitted to the secrecy-rate results. The optimization problems in Sections IV and V use Eq. (16) to constrain phase shifts, and the simulations use the same model as a stand-in for the real temperature response. This is an in-sample validation and a legitimate empirical risk if real LC cells violate the multiplicative scaling, but it is not a circular reduction: no parameter is fitted to the predicted secrecy rate, and the claimed gains are logical consequences of optimizing under the stated model rather than being equivalent to the model by construction. The self-citations for Lemmas 3 and 4 refer to standard parameter-free bounding inequalities that do not encode the paper's target result, so they do not smuggle in the conclusion. Thus, under the strict definition of circularity, the derivation chain is self-contained and no specific circular step can be identified.

Assumptions & free parameters 3 free parameters · 9 assumptions · 0 invented entities

The paper introduces no new physical entities. Its central claim rests on semi-empirical LC material parameters (β, Tc, Tr), the Haller approximation, the multiplicative scaling of the phase profile, and several engineering assumptions (blocked direct links, phase-uniformity, LOS dominance, temperature discretization). These are mostly imported from prior literature but are not experimentally validated in this paper.

free parameters (3)

Haller exponent β = 0.25 (used in simulations; literature typical 0.2–0.25)
Empirical power-law exponent in S(T)=(1−T/Tc)^β, Eq. (14). It determines how fast the phase-shift range shrinks with temperature; not derived from first principles in this paper.
Clearing temperature Tc = 95 °C
Material transition temperature used in Eqs. (15)–(16) and Figs. 3–4. Chosen as a simulation parameter, not measured in this paper.
Reference temperature Tr = 10 °C
Calibration temperature at which Δωmax=2π; the temperature model is anchored here and all phase-shift scaling is relative to it.

assumptions (9)

domain assumption Haller empirical power law S(T)=(1−T/Tc)^β accurately approximates the nematic order parameter.
Invoked in Eq. (14) to convert the self-consistent Maier-Saupe equation (13) into a closed-form temperature scaling; Fig. 3 compares it only with the numerical Maier-Saupe solution, not with measured S(T) for the LC mixture.
domain assumption The full voltage-to-phase-shift profile at any temperature scales multiplicatively as ω(V,T)=ω(V,Tr)·((Tc−T)/(Tc−Tr))^β, with minimum phase calibrated to zero.
Eq. (16) assumes the entire tuning curve compresses linearly with the order parameter; the experimental baseline is adapted from refs. [33],[34], not measured in this paper.
domain assumption At design time the direct BS-user links are fully blocked and only LOS RIS paths are used for optimization.
P1/P8 set hd,g≈0 and rely on LOS-dominated mmWave propagation; the direct link is reintroduced only in the Section VI evaluations.
ad hoc to paper RIS phase shifts are approximately uniformly distributed over [0, ωmax] for large N, so Lemma 1's law-of-large-numbers sum approximation holds.
Section IV-B1, Eqs. (26)–(28): the SDP constraint cC2 is equivalent to C2 only under this distributional assumption; the authors state it is 'not generally guaranteed' and rely on NF beam randomness.
domain assumption ωmax remains in (π, 2π) over the operating temperature range.
Used in Lemma 2's case split; justified by experimental ref. [34] rather than measurements in this paper.
standard math The surrogate lower bound of Lemma 3 and the LSE smoothing of Lemma 4 are valid.
Proofs are referenced to the authors' prior work [13, Lemmas 3 and 4]; these are standard convex surrogate techniques.
domain assumption The continuous temperature uncertainty set can be replaced by M discretized samples Td without significant loss.
Section V discretizes T into M representative temperatures and applies LSE weights over the joint spatial-thermal grid; no bound on discretization error is provided.
domain assumption LC element reflection amplitude is approximately unity, with losses absorbed in the BS transmit power.
Section II-A and footnote 2, following refs. [20],[21]; this simplifies the reflection matrix to phase-only.
domain assumption Maier-Saupe mean-field potential U(θ)=−αi S P2(cosθ) describes the intermolecular alignment energy.
Section III-B, Eq. (12); this is a standard mean-field approximation in LC physics, though not independently validated for the specific material here.

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Cite this review

Pith. "Pith review of Temperature-aware Optimization of Liquid Crystal Reconfigurable Intelligent Surfaces: Physics-based Modeling and Robust Design." pith.science (2026). https://pith.science/paper/UJIYZQ2H

@misc{pith2026260722141,
  author       = {Pith},
  title        = {Pith review of: Temperature-aware Optimization of Liquid Crystal Reconfigurable Intelligent Surfaces: Physics-based Modeling and Robust Design},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UJIYZQ2H}},
  note         = {Machine review of arXiv:2607.22141}
}

read the original abstract

While LC technology facilitates the realization of energy-efficient and scalable RISs, their phase shift response is inherently temperature-dependent. Neglecting this thermal dependency can lead to performance degradation, which is particularly detrimental in secure wireless systems where phase-shift inaccuracies may result in unintended information leakage. To address this challenge, we investigate secure communication in LC-RIS-aided systems and develop a temperature-adaptive phase-shift design. Beyond thermal sensitivity, the massive number of elements at mmWave frequencies is required to compensate for high path loss. This large-scale deployment of LC-RISs can lead to significant overhead challenges due to the acquisition of CSI. To ensure practical feasibility, this work proposes a phase-shift design that does not rely on the full CSI; instead, it employs only the possible locations of legitimate users and potential eavesdroppers. By illuminating a spatial zone rather than a single target location, the proposed temperature-adaptive algorithm enhances robustness against both thermally induced phase errors and positioning inaccuracies. To solve the resulting optimization problem, we present an SDP-based approach to serve as a high-performance benchmark, as well as a low-complexity heuristic method. The latter demonstrates superior scalability as the number of RIS elements increases, which makes it highly effective for deploying extremely large surfaces in dynamic, real-time environments. Based on this scalable framework, we further design a temperature-robust algorithm that maintains high security without requiring real-time temperature data. Extensive simulation results confirm that our temperature-adaptive and temperature-robust approaches yield a superior secrecy rate compared to conventional designs that neglect temperature impacts.

Figures

Figures reproduced from arXiv: 2607.22141 by the authors.

**Figure 2.** Schematic representation of LC molecular alignment profiles across [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Validation of the nematic order parameter [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (6 more)

**Figure 4.** Figure 4: Phase shift versus applied voltage for an LC element across different [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: The percentage of ∥S∥∗ − ∥S (i)∥2 and Nfalse out of total number of N at each iteration, when T = 40◦C. 1,000 2,000 3,000 0 1 2 3 4 5 6 7 Iteration number min ∀pu∈Pu, ∀pe∈Pe SR ( bits symbols ) [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

**Figure 6.** Figure 6: Convergence behavior of Algorithm 2 with different initializations versus the iteration number, when T = 40◦C. The analysis considers two scenarios based on the location of the eavesdropper w.r.t. the legitimate user area. These scenarios are critical because the eaves…

**Figure 9.** Figure 9: SNR (dB) for two cases in scenario 1. The first row includes results when the RIS phase shifts are designed without caring about the temperature, [PITH_FULL_IMAGE:figures/full_fig_p012_9.png]

**Figure 10.** Figure 10: SNR (dB) for two cases in scenario 2. The first row includes results when the RIS phase shifts are designed without caring about the temperature, [PITH_FULL_IMAGE:figures/full_fig_p012_10.png]

**Figure 11.** Figure 11: The minimum secrecy rate (bits/sec/Hz) over all possible locations [PITH_FULL_IMAGE:figures/full_fig_p012_11.png]

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