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arxiv: 2605.21760 · v1 · pith:SEXZR22Hnew · submitted 2026-05-20 · 📡 eess.SP

Rethinking Passive RIS: Finite Blocklength Reliability Analysis Under Thermal Noise

Farjam Karim , Deepak Kumar , Prathapasinghe Dharmawansa , Nurul Huda Mahmood , Arthur Sousa de Sena , Matti Latva-aho This is my paper

Pith reviewed 2026-05-22 08:23 UTC · model grok-4.3

classification 📡 eess.SP
keywords reconfigurable intelligent surfacefinite blocklengththermal noiseblock error rateshort packet communicationswireless reliabilitypassive RISgoodput
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The pith

Incorporating thermal noise from passive RIS elements shows overestimation of reliability in finite blocklength communications and that larger surfaces do not always help.

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

This paper establishes that conventional analyses of reconfigurable intelligent surface assisted systems in the finite blocklength regime overestimate reliability because they neglect thermal noise generated by the passive elements. The authors develop expressions for block error rate that include this noise and show the overestimation becomes more severe as the number of elements increases. They further demonstrate that in low transmit power conditions the accumulated noise can make adding more reflecting elements counterproductive. A sympathetic reader would care because short packet transmissions are key to latency-sensitive applications like industrial control and vehicle communications, and inaccurate models could lead to unreliable deployments.

Core claim

The paper claims that when thermal noise from passive RIS elements is modeled as additive white Gaussian noise with variance scaling with the number of elements, the resulting block error rate analysis reveals both an overestimation of reliability when noise is ignored and the existence of regimes where increasing the RIS size degrades performance.

What carries the argument

Unified analytical framework deriving block-error-rate expressions for uniform and non-uniform RIS reflection coefficients while accounting for accumulated thermal noise.

If this is right

  • Block error rates must be recalculated with thermal noise included to avoid optimistic reliability estimates.
  • Performance does not improve monotonically with RIS size in low transmit power scenarios.
  • Goodput in short-packet systems is lower than previously predicted when noise is considered.
  • Designs assuming infinite blocklength need adjustment for accurate finite blocklength evaluations.

Where Pith is reading between the lines

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

  • System designers might consider active RIS elements or noise mitigation techniques to counteract the thermal effects.
  • The findings could extend to other passive amplification devices where thermal noise accumulates.
  • Optimizing the distribution of reflection coefficients might reduce the impact of the noise beyond uniform or non-uniform cases studied.

Load-bearing premise

The thermal noise from each passive RIS element can be accurately modeled as additive white Gaussian noise whose total variance grows with the number of elements and does not depend on the desired signal.

What would settle it

An experiment measuring actual block error rates using a physical RIS with increasing numbers of elements at low transmit powers, compared against theoretical predictions that include versus exclude the thermal noise term.

Figures

Figures reproduced from arXiv: 2605.21760 by Arthur Sousa de Sena, Deepak Kumar, Farjam Karim, Matti Latva-aho, Nurul Huda Mahmood, Prathapasinghe Dharmawansa.

Figure 1
Figure 1. Figure 1: BLER for uniform βn. -60 -55 -50 -45 -40 -35 -30 10-6 10-4 10-2 100 [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 4
Figure 4. Figure 4: Sensitivity analysis. degradation. Since receiver noise depends only on B, whereas RIS-induced noise scales with both B and N, the figure shows that for B = 10 MHz and N = 5, σ 2 d dominates σ 2 r when βn ≤ 0.4, allowing σ 2 r to be neglected in this region. For larger βn, however, σ 2 r becomes significant, and its impact grows with increasing B and N. This highlights the tradeoff between RIS size and ban… view at source ↗
read the original abstract

Short-packet communication alters the fundamental performance limits of reconfigurable intelligent surface (RIS)-assisted systems, making conventional analyses based on the infinite blocklength regime insufficient. This work investigates RIS-assisted transmission in the finite blocklength (FBL) regime while explicitly incorporating thermal noise generated by passive RIS elements, an effect commonly neglected in existing models. A unified analytical framework is developed to characterize the block-error rate (BLER), its asymptotic behavior, and the resulting goodput under both uniform and non-uniform RIS reflection coefficients. Our results show that ignoring RIS thermal noise leads to a pronounced overestimation of reliability with the mismatch increasing as the number of reflecting elements grows. Furthermore, increasing the RIS size does not always improve performance, particularly in the low transmit power regime where accumulated noise becomes dominant. Overall, the results highlight fundamental limitations of idealized RIS models and demonstrate the need for incorporating thermal noise for accurate system evaluation.

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

1 major / 2 minor

Summary. The paper develops a unified analytical framework for the block-error rate (BLER) in RIS-assisted short-packet communications in the finite blocklength regime, explicitly including thermal noise generated by passive RIS elements. It derives BLER expressions and asymptotic results under uniform and non-uniform reflection coefficients, evaluates goodput, and concludes that neglecting RIS thermal noise overestimates reliability (with mismatch growing in N) while showing that increasing RIS size does not always improve performance, especially at low transmit power where accumulated noise dominates.

Significance. If the thermal noise modeling is accurate, the work is significant for challenging idealized RIS assumptions in the FBL regime and providing analytical tools to assess reliability and goodput in practical short-packet scenarios relevant to IoT and URLLC. The unified framework, asymptotic analysis, and explicit treatment of reflection coefficient strategies constitute clear strengths that could guide more realistic system evaluations.

major comments (1)
  1. [Noise model and effective SNR derivation] The central claims on overestimation of reliability and the non-monotonic effect of N on performance rest on modeling per-element thermal noise as AWGN whose variance scales linearly with N and remains statistically independent of the desired reflected signal after phase alignment. This enters the effective SNR used for the FBL BLER approximation; the manuscript should supply a detailed physical derivation or reference justifying the scaling and independence (including whether noise is generated before or after the reflection coefficient), as any induced correlation would alter the noise power and potentially reverse the low-power conclusions.
minor comments (2)
  1. [Abstract] The abstract could briefly note the specific finite-blocklength approximation (e.g., normal approximation) employed for the BLER to improve self-containment.
  2. [Notation and definitions] Notation for noise variances and reflection coefficients should be checked for consistency across the analytical framework and numerical sections.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive and insightful feedback. The comment on the noise model is well-taken and has prompted us to strengthen the physical justification in the revised manuscript. We address the point below and believe the changes improve the clarity and rigor of the work.

read point-by-point responses

  1. Referee: The central claims on overestimation of reliability and the non-monotonic effect of N on performance rest on modeling per-element thermal noise as AWGN whose variance scales linearly with N and remains statistically independent of the desired reflected signal after phase alignment. This enters the effective SNR used for the FBL BLER approximation; the manuscript should supply a detailed physical derivation or reference justifying the scaling and independence (including whether noise is generated before or after the reflection coefficient), as any induced correlation would alter the noise power and potentially reverse the low-power conclusions.

    Authors: We agree that a more explicit physical derivation is valuable. In the revised manuscript we have expanded Section II with a step-by-step derivation of the per-element thermal noise. The noise is generated inside each passive RIS element (thermal fluctuations in the varactor or resistive phase-shifting circuitry) prior to multiplication by the reflection coefficient. Each element contributes independent circularly symmetric complex Gaussian noise with variance σ² = kTB (k Boltzmann’s constant, T temperature, B bandwidth). Because the elements are physically distinct and their driving circuits are independent, the noise realizations remain uncorrelated across elements. After the deterministic phase shifts are applied for signal alignment, the desired reflected paths combine coherently while the noise terms, being zero-mean and uncorrelated, combine in power, yielding total noise variance Nσ². We have added references to hardware-level RIS noise models that support this independence. A new Appendix A now derives the effective SNR explicitly, confirming that the noise term is statistically independent of the aligned signal. To address possible correlation concerns, we have included a brief sensitivity study (new Fig. 8) showing that even with moderate correlation coefficients the qualitative conclusions—overestimation of reliability when noise is ignored and non-monotonic behavior of BLER/goodput versus N at low transmit power—remain unchanged. These revisions are marked in blue in the resubmitted version. revision: yes

Circularity Check

0 steps flagged

No load-bearing circularity; thermal noise model is an explicit input to independent BLER derivation

full rationale

The paper introduces an explicit model for per-element thermal noise as AWGN whose variance scales linearly with N and remains independent of the reflected signal after phase alignment. It then derives closed-form BLER expressions, asymptotic approximations, and goodput using standard finite-blocklength information theory tools (e.g., normal approximation or saddlepoint methods) applied to the resulting effective SNR. No equation reduces a claimed prediction back to a fitted parameter by construction, no uniqueness theorem is imported from self-citation to force the model, and the central claims about overestimation and non-monotonicity in N follow directly from the noise term rather than from re-labeling inputs. The modeling choice is an assumption open to external verification rather than a self-referential loop.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on standard finite-blocklength error-rate approximations and an additive thermal-noise model for passive RIS elements; no new particles or forces are postulated.

axioms (2)
  • domain assumption Finite-blocklength block-error-rate approximations remain accurate for the SNR and packet-length regimes considered
    Invoked when characterizing BLER and its asymptotic behavior.
  • domain assumption Thermal noise from each RIS element is additive, white, Gaussian, and scales linearly with the number of elements
    Central modeling choice that enables the unified framework under uniform and non-uniform reflection coefficients.

pith-pipeline@v0.9.0 · 5708 in / 1361 out tokens · 50613 ms · 2026-05-22T08:23:41.006604+00:00 · methodology

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Reference graph

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