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arxiv: 2605.16907 · v1 · pith:WXIY6VQBnew · submitted 2026-05-16 · 📡 eess.SP

Performance Analysis of Movable Antenna Arrays

Gayani Siriwardana , Peter J. Smith , Himal A. Suraweera , Rajitha Senanayake This is my paper

Pith reviewed 2026-05-19 20:05 UTC · model grok-4.3

classification 📡 eess.SP
keywords movable antenna arraysperformance analysissignal-to-noise ratiolevel crossing ratespatial correlationantenna positioningwireless communications
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The pith

Continuous movable antenna arrays outperform fixed and single fluid antenna systems by improving SNR tail behavior and level crossing rates.

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

This paper analyzes a linear array of multiple fixed antenna elements that moves continuously along a line under a full spatially coherent correlation model. It derives asymptotically exact approximations for the upper tail of the SNR cumulative distribution function in both correlated and uncorrelated cases, plus a closed-form level crossing rate expression for non-separable two-dimensional correlation. Simulations confirm the approximations and show performance gains over single fluid antennas and fixed arrays, with smaller inter-element spacing yielding further improvements. A sympathetic reader would care because these results point to practical ways to enhance wireless link reliability through antenna movement rather than added hardware.

Core claim

Under a full spatially coherent correlation model with continuous positioning along a line, asymptotically exact approximations to the upper tail of the SNR CDF hold for correlated and uncorrelated elements, and a novel closed-form expression for the SNR level crossing rate exists under non-separable two-dimensional correlation; numerical results establish that the movable array outperforms single fluid antenna and fixed array systems, with reduced inter-element spacing providing additional gains.

What carries the argument

Linear array of multiple fixed elements with continuous positioning along a line under full spatial correlation, used to obtain SNR CDF tail approximations and level crossing rate expressions.

If this is right

  • The movable array achieves lower outage probability through improved upper-tail SNR behavior compared to fixed systems.
  • Reduced inter-element spacing produces measurable additional performance gains in the SNR statistics.
  • The closed-form LCR enables direct analysis of SNR fluctuation rates in correlated movable-array scenarios.
  • Overall system performance exceeds that of both single fluid antennas and conventional fixed arrays.

Where Pith is reading between the lines

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

  • The approach could support dynamic position adjustment in mobile devices to track favorable channel conditions.
  • Similar analysis might apply to two-dimensional surface movements for larger coverage areas.
  • The correlation model suggests that movable arrays could reduce the need for frequent handoffs in dense networks.

Load-bearing premise

The analysis assumes a full spatially coherent correlation model together with continuous positioning of the array along a line.

What would settle it

Real channel measurements of SNR distribution and level crossing rate for a multi-element movable array would falsify the claims if they deviate from the derived upper-tail approximations or closed-form LCR under the assumed correlation.

Figures

Figures reproduced from arXiv: 2605.16907 by Gayani Siriwardana, Himal A. Suraweera, Peter J. Smith, Rajitha Senanayake.

Figure 1
Figure 1. Figure 1: An illustration of a movable antenna array. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The LCR of the SNR of moving arrays with [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: The cdf of the SNR for moving arrays with different [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Performance comparison of (a) SA, (b) SFA, (c) [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
read the original abstract

This paper provides a thorough mathematical analysis of continuous movable antenna (MA) arrays. Focusing on the multiple antenna case, we consider a linear antenna array with multiple fixed antenna elements that moves along a line. We assume a full, spatially coherent correlation model and continuous positioning of the array. We provide asymptotically exact approximations to the upper tail of the cumulative distribution function (cdf) of the signal-to-noise ratio (SNR), considering both correlated and uncorrelated antenna elements in the array. We also obtain a novel closed-form expression for the level crossing rate (LCR) of the SNR under correlated array elements, where a non-separable two-dimensional correlation is present. The analysis is validated through simulations, confirming both the accuracy of the LCR expressions and the tightness of the cdf bounds in the upper tail. Numerical results show that the proposed MA array outperforms single fluid antenna and fixed array systems, with reduced inter-element spacing providing further performance gains.

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 / 2 minor

Summary. The paper analyzes the performance of continuous movable antenna (MA) arrays consisting of a linear array with fixed inter-element spacing whose collective position varies continuously along a line. It derives asymptotically exact approximations to the upper tail of the CDF of the received SNR for both correlated and uncorrelated elements, obtains a novel closed-form expression for the level crossing rate (LCR) of the SNR under a non-separable two-dimensional spatial correlation model, validates the expressions via Monte Carlo simulations, and presents numerical results claiming that MA arrays outperform both single fluid-antenna systems and conventional fixed arrays, with further gains from reduced inter-element spacing.

Significance. If the derivations are correct, the work supplies useful analytical tools (CDF tail approximations and a closed-form LCR) for evaluating MA-array performance in correlated channels. The numerical comparisons provide concrete evidence of potential gains over fixed and fluid-antenna baselines, which could inform system design for future wireless links that exploit antenna mobility.

major comments (2)
  1. [Section IV, Eq. (22)] The LCR derivation (Section IV, Eq. (22) and surrounding text) invokes a non-separable two-dimensional correlation function R(Δx, Δy) yet the system model restricts collective movement to a one-dimensional line trajectory. No explicit projection, velocity-vector reduction, or substitution that reduces the 2D function to the scalar displacement along the line is provided; without this step the closed-form expression rests on an implicit assumption whose validity is not obvious from the model description in Section II.
  2. [Section III, Eqs. (15)–(18)] The tightness claims for the CDF upper-tail approximations (Section III, Eqs. (15)–(18)) are supported only by simulation curves; the manuscript does not report the precise range of SNR values, number of Monte Carlo realizations, or any data-exclusion criteria used to generate the plotted points, making it impossible to assess whether post-hoc choices affect the reported accuracy in the tail region.
minor comments (2)
  1. [Section II] Notation for the correlation function is introduced inconsistently between the abstract (non-separable 2D) and the system model (Section II); a single, unambiguous definition should be used throughout.
  2. [Figure 3] Figure 3 caption does not state the exact values of the correlation parameters or the number of array elements used in the plotted curves.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and constructive comments. We address each major comment below and will revise the manuscript to improve clarity and reproducibility.

read point-by-point responses

  1. Referee: [Section IV, Eq. (22)] The LCR derivation (Section IV, Eq. (22) and surrounding text) invokes a non-separable two-dimensional correlation function R(Δx, Δy) yet the system model restricts collective movement to a one-dimensional line trajectory. No explicit projection, velocity-vector reduction, or substitution that reduces the 2D function to the scalar displacement along the line is provided; without this step the closed-form expression rests on an implicit assumption whose validity is not obvious from the model description in Section II.

    Authors: We agree that an explicit reduction step would strengthen the presentation. In Section II the collective movement of the linear array is restricted to a scalar position parameter s along a fixed line (taken without loss of generality as the x-axis). Consequently the two-dimensional correlation is evaluated at transverse displacement Δy = 0, yielding the effective one-dimensional function R(s, 0). The level-crossing-rate derivation in Section IV then proceeds with respect to the time derivative of the SNR, where the velocity vector is aligned with the line of motion. We will insert a short paragraph and the corresponding substitution immediately before Eq. (22) to make this reduction explicit and to connect the 2D model description with the 1D trajectory. revision: yes

  2. Referee: [Section III, Eqs. (15)–(18)] The tightness claims for the CDF upper-tail approximations (Section III, Eqs. (15)–(18)) are supported only by simulation curves; the manuscript does not report the precise range of SNR values, number of Monte Carlo realizations, or any data-exclusion criteria used to generate the plotted points, making it impossible to assess whether post-hoc choices affect the reported accuracy in the tail region.

    Authors: We acknowledge that additional simulation details are necessary for readers to evaluate the reported tightness. The Monte Carlo results were generated with a large number of independent realizations chosen to ensure reliable tail statistics, and no post-hoc exclusion of data points was performed. In the revised manuscript we will add a concise description of the simulation parameters, including the exact number of realizations, the SNR range over which the tail approximations are compared, and a statement confirming the absence of selective data filtering. revision: yes

Circularity Check

0 steps flagged

No circularity: derivations are independent mathematical approximations validated externally

full rationale

The paper derives asymptotically exact CDF tail approximations and a closed-form LCR expression directly from the linear MA array model with continuous 1D positioning and a full spatially coherent correlation function. These steps are presented as analytical results (not fits to performance metrics), with validation via separate simulations. No self-definitional reductions, fitted parameters renamed as predictions, or load-bearing self-citations appear in the derivation chain. The 1D-vs-2D correlation detail is an assumption validity question rather than a circularity reduction, and the central claims remain self-contained against the stated model and external numerical checks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on a full spatially coherent correlation model and continuous linear positioning; these are domain assumptions rather than derived quantities. No free parameters or invented entities are explicitly introduced in the abstract.

axioms (2)
  • domain assumption Full, spatially coherent correlation model for the movable array
    Invoked to enable the asymptotic CDF and LCR derivations under both correlated and uncorrelated elements.
  • domain assumption Continuous positioning of the linear antenna array along a line
    Required for the continuous-movement model that distinguishes the analysis from discrete or fixed arrays.

pith-pipeline@v0.9.0 · 5696 in / 1268 out tokens · 27443 ms · 2026-05-19T20:05:29.213150+00:00 · methodology

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Reference graph

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