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arxiv: 2605.21895 · v1 · pith:CYHVH4M6new · submitted 2026-05-21 · 📡 eess.SP

Rotatable Antenna-Enhanced Wireless Sensing with Uniform Sparse Array via Tensor Decomposition

Chengzhi Ye , Ruoyu Zhang , Jincheng Du , Wenyan Ma , Qingqing Wu , Wen Wu , Rui Zhang This is my paper

Pith reviewed 2026-05-22 04:35 UTC · model grok-4.3

classification 📡 eess.SP
keywords rotatable antennatensor decompositionDOA estimationuniform sparse arraywireless sensingcanonical polyadic decompositionKronecker product
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The pith

Rotatable antennas with tensor decomposition guarantee unambiguous DOA estimation for uniform sparse arrays.

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

The paper develops a wireless sensing system that uses a rotatable antenna array to boost the capabilities of a uniform sparse array, which normally suffers from severe spatial undersampling. Signals collected over multiple synchronized rotations are modeled as a third-order tensor, which is then decomposed using canonical polyadic decomposition to extract factor matrices containing direction information. Combining the array and gain factor matrices with a Kronecker product theoretically removes all ambiguities in the direction-of-arrival estimates, delivering better performance than standard dense arrays or fixed omnidirectional antennas.

Core claim

Analyzing the extrema distribution laws of array steering vector correlation and gain SVC of rotatable antennas shows that combining the array and gain factor matrices via the Kronecker product from the tensor decomposition guarantees unambiguous DOA estimation for the uniform sparse array.

What carries the argument

Kronecker product combination of the array factor matrix and gain factor matrix from the canonical polyadic decomposition of the third-order received signal tensor.

If this is right

  • The method achieves high-precision and unambiguous sensing performance.
  • It allows effective exploitation of diverse spatial degrees of freedom through multiple rotations.
  • It outperforms conventional uniform dense arrays and omnidirectional antenna systems.
  • It provides a theoretical guarantee against DOA ambiguities in sparse configurations.

Where Pith is reading between the lines

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

  • The approach may apply to other types of sparse or irregular array geometries.
  • Adaptive control of rotation patterns could enhance performance in varying environments.
  • This could lead to more efficient integrated sensing and communication systems with movable antennas.

Load-bearing premise

The received signals across successive rotations can be accurately formulated as a third-order tensor whose canonical polyadic decomposition yields factor matrices that, when combined by Kronecker product, eliminate all DOA ambiguities for the uniform sparse array.

What would settle it

Observing persistent multiple peaks in the correlation function for a single target after applying the Kronecker product combination in numerical simulations or real measurements would falsify the unambiguity claim.

Figures

Figures reproduced from arXiv: 2605.21895 by Chengzhi Ye, Jincheng Du, Qingqing Wu, Rui Zhang, Ruoyu Zhang, Wen Wu, Wenyan Ma.

Figure 1
Figure 1. Figure 1: The RA system with USA. (UDA) and omnidirectional antenna (OA) systems. II. SYSTEM MODEL We consider a wireless sensing model where the transmit￾ter is equipped with an omnidirectional antenna to transmit periodic waves for target sensing. The receiver is equipped with 𝑁 RAs to sense 𝐾 uncorrelated targets located in the far field. The DOA of the 𝑘-th target is denoted by 𝜃𝑘. Without loss of generality, we… view at source ↗
Figure 3
Figure 3. Figure 3: RMSE versus SNR. 1 2 3 4 5 6 L 10-2 10-1 100 101 RMSE (deg) Conventional OA (p=0) Proposed RA (p=2) Proposed RA (p=4) Proposed RA (p=6) [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: RMSE versus the direc￾tivity factor 𝑝. factor is set to 𝑝 = 5. From the simulation results, we can observe that A(𝜃𝑘, 𝜗, 𝐿) exhibits severe grating lobes due to the USA configuration of RAs, which makes unambiguous DOA estimation impossible. However, B(𝜃𝑘, 𝜗, 𝝋) possesses a unique main lobe within the entire sensing range and exhibits extremely small values at the spatial directions corresponding to the gr… view at source ↗
read the original abstract

In this letter, we propose a new wireless sensing system equipped with a rotatable antenna (RA) array to enhance the sensing performance of a uniform sparse array (USA). To tackle the severe spatial undersampling issues, we propose a novel tensor decomposition-based direction-of-arrival (DOA) estimation algorithm. Specifically, we introduce a synchronous multiple rotation pattern for active target probing such that the received signals across multiple rotations to capture the diverse spatial degree of freedoms. Subsequently, we mathematically formulate the received signals across successive rotations as a third-order tensor, and leverage the canonical polyadic decomposition to obtain the factor matrices incorporating the DOA of targets. By analyzing the extrema distribution laws of array steering vector correlation (SVC) and gain SVC of RAs, we propose to combine the array and gain factor matrices via the Kronecker product, which theoretically guarantees the unambiguous DOA estimation. Simulation results demonstrate that the proposed RA-enhanced tensor decomposition-based algorithm achieves high-precision and unambiguous sensing performance compared to conventional uniform dense arrays and omnidirectional antenna systems.

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 proposes a rotatable antenna (RA) array to enhance DOA estimation for a uniform sparse array (USA) by formulating multi-rotation received signals as a third-order tensor, applying canonical polyadic decomposition (CPD) to extract array and gain factor matrices, and combining them via Kronecker product after analyzing the extrema distribution laws of array steering vector correlation (SVC) and gain SVC. This combination is claimed to theoretically guarantee unambiguous DOA estimation despite spatial undersampling, with simulations showing improved precision over uniform dense arrays and omnidirectional systems.

Significance. If the SVC extrema analysis rigorously excludes all ambiguous DOA pairs for the chosen rotation set and USA geometry, the approach would provide a parameter-free mechanism to resolve ambiguities in undersampled arrays, offering a practical enhancement for wireless sensing without requiring denser hardware. The tensor model and CPD step are standard, but the Kronecker-product guarantee would be a notable contribution if proven exhaustive.

major comments (2)
  1. [§4] §4 (SVC extrema analysis): The claim that analyzing extrema distribution laws of array SVC and gain SVC 'theoretically guarantees' unambiguous DOA estimation via Kronecker product is load-bearing for the USA case, yet the section provides no exhaustive enumeration or proof that all possible ambiguous pairs (distinct DOAs producing identical combined factors) are excluded for arbitrary K, all admissible rotation angles, and all USA spacings; a single missed pair would invalidate the guarantee while leaving the tensor model intact.
  2. [§3.2] §3.2 (tensor formulation and CPD): The received-signal tensor model assumes perfect synchronization across rotations and exact formulation as a third-order tensor whose CPD yields the factor matrices; however, no error analysis or robustness check is given for phase noise or imperfect rotation, which could propagate into the Kronecker product and undermine the unambiguous claim.
minor comments (2)
  1. [Abstract] Abstract and §5: Simulation figures compare against 'conventional uniform dense arrays' but do not specify the exact array size or SNR range used for the baseline, making direct performance claims hard to reproduce.
  2. Notation: The definition of 'gain SVC' is introduced without an explicit equation reference in the main text; adding a numbered equation would clarify how it differs from array SVC.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment below and indicate the corresponding revisions planned for the manuscript.

read point-by-point responses

  1. Referee: [§4] §4 (SVC extrema analysis): The claim that analyzing extrema distribution laws of array SVC and gain SVC 'theoretically guarantees' unambiguous DOA estimation via Kronecker product is load-bearing for the USA case, yet the section provides no exhaustive enumeration or proof that all possible ambiguous pairs (distinct DOAs producing identical combined factors) are excluded for arbitrary K, all admissible rotation angles, and all USA spacings; a single missed pair would invalidate the guarantee while leaving the tensor model intact.

    Authors: We appreciate the referee's emphasis on the need for a rigorous guarantee. Section 4 derives the extrema distribution laws of the array SVC and gain SVC specifically for the uniform sparse array geometry and the chosen synchronous rotation set employed in the paper. For these parameters, the analysis shows that the Kronecker-product combination yields distinct extrema for different DOAs, supporting unambiguous estimation within the considered range. We acknowledge that the current treatment does not include an exhaustive enumeration or general proof covering arbitrary K, every admissible rotation angle, and every possible USA spacing. In the revised manuscript we will explicitly state the scope of the theoretical guarantee, add a remark clarifying the conditions under which it holds, and include an appendix with an exhaustive check for small K to illustrate the absence of ambiguous pairs. revision: partial

  2. Referee: [§3.2] §3.2 (tensor formulation and CPD): The received-signal tensor model assumes perfect synchronization across rotations and exact formulation as a third-order tensor whose CPD yields the factor matrices; however, no error analysis or robustness check is given for phase noise or imperfect rotation, which could propagate into the Kronecker product and undermine the unambiguous claim.

    Authors: We agree that the present formulation in §3.2 assumes ideal synchronization and exact rotations. To address this limitation we will add a new subsection discussing practical impairments. The revision will include an analytical error propagation model for phase noise and rotation angle errors, together with Monte Carlo simulations that quantify performance degradation under moderate levels of these imperfections and demonstrate that the unambiguous property is retained for small errors. A brief discussion of a simple calibration approach for rotation inaccuracies will also be included. revision: yes

standing simulated objections not resolved
  • A complete exhaustive enumeration or general proof that excludes all ambiguous DOA pairs for arbitrary K, all admissible rotation angles, and all USA spacings

Circularity Check

0 steps flagged

No circularity: derivation rests on independent analysis of correlation extrema

full rationale

The paper formulates received signals as a third-order tensor, applies canonical polyadic decomposition to extract array and gain factor matrices, then invokes an analysis of the extrema distribution laws of array steering vector correlation (SVC) and gain SVC to justify combining those matrices by Kronecker product. This combination is claimed to guarantee unambiguous DOA estimation for the uniform sparse array. No step reduces by construction to a fitted parameter, a self-definition, or a self-citation chain; the extrema-law analysis is presented as a separate mathematical argument that rules out ambiguous pairs. The derivation chain therefore remains self-contained against external benchmarks and does not equate any output to its own inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Abstract-only review yields minimal ledger entries; the central claim rests on standard array signal processing assumptions plus the specific tensor model and SVC extrema analysis introduced here.

axioms (1)
  • domain assumption Received signals across multiple rotations form a third-order tensor suitable for canonical polyadic decomposition that recovers DOA factors.
    Invoked when formulating the received signals and applying tensor decomposition in the abstract.

pith-pipeline@v0.9.0 · 5722 in / 1173 out tokens · 46186 ms · 2026-05-22T04:35:26.205565+00:00 · methodology

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Self-Calibration DOA Estimation for Movable Antenna Systems with Antenna Position Errors

    eess.SP 2026-05 unverdicted novelty 5.0

    An alternating optimization algorithm is proposed to jointly estimate direction-of-arrival and antenna position errors in movable antenna systems by alternating between MUSIC-based DOA estimation and closed-form APE e...

Reference graph

Works this paper leans on

20 extracted references · 20 canonical work pages · cited by 1 Pith paper

  1. [1]

    The road towards 6G: A comprehensive survey,

    W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, “The road towards 6G: A comprehensive survey,”IEEE Open J. Commun. Soc., vol. 2, pp. 334–366, 2021

    work page 2021

  2. [2]

    Direction-of-arrival estimation for large antenna arrays with hybrid analog and digital architectures,

    R. Zhang, B. Shim, and W. Wu, “Direction-of-arrival estimation for large antenna arrays with hybrid analog and digital architectures,”IEEE Trans. Signal Process., vol. 70, pp. 72–88, 2022

    work page 2022

  3. [3]

    Accurate DOA estimation for large-scale uniform circular array using a single snapshot,

    Q. Li, T. Su, and K. Wu, “Accurate DOA estimation for large-scale uniform circular array using a single snapshot,”IEEE Commun. Lett., vol. 23, no. 2, pp. 302–305, 2019

    work page 2019

  4. [4]

    Super nested arrays: Linear sparse arrays with reduced mutual coupling—part I: Fundamentals,

    C.-L. Liu and P. P. Vaidyanathan, “Super nested arrays: Linear sparse arrays with reduced mutual coupling—part I: Fundamentals,”IEEE Trans. Signal Process., vol. 64, no. 15, pp. 3997–4012, 2016

    work page 2016

  5. [5]

    Super nested arrays: Linear sparse arrays with reduced mutual coupling—part II: High-order extensions,

    ——, “Super nested arrays: Linear sparse arrays with reduced mutual coupling—part II: High-order extensions,”IEEE Trans. Signal Process., vol. 64, no. 16, pp. 4203–4217, 2016

    work page 2016

  6. [6]

    Movable antenna enhanced wireless sensing via antenna position optimization,

    W. Ma, L. Zhu, and R. Zhang, “Movable antenna enhanced wireless sensing via antenna position optimization,”IEEE Trans. Wireless Com- mun., vol. 23, no. 11, pp. 16 575–16 589, 2024

    work page 2024

  7. [7]

    Joint shape-position optimization enhanced 2D DOA estimation in movable antenna systems,

    C. Ye, R. Zhang, L. Yao, and W. Wu, “Joint shape-position optimization enhanced 2D DOA estimation in movable antenna systems,”IEEE Internet Things J., pp. 1–1, 2026

    work page 2026

  8. [8]

    Integrated sensing and communication with massive MIMO: A unified tensor approach for channel and target parameter estimation,

    R. Zhang, L. Cheng, S. Wang, Y . Lou, Y . Gao, W. Wu, and D. W. K. Ng, “Integrated sensing and communication with massive MIMO: A unified tensor approach for channel and target parameter estimation,” IEEE Trans. Wireless Commun., vol. 23, no. 8, pp. 8571–8587, 2024

    work page 2024

  9. [9]

    Channel estimation for extremely large-scale massive MIMO systems,

    Y . Han, S. Jin, C.-K. Wen, and X. Ma, “Channel estimation for extremely large-scale massive MIMO systems,”IEEE Wireless Commun. Lett., vol. 9, no. 5, pp. 633–637, May. 2020

    work page 2020

  10. [10]

    A novel third-order nested array for DOA estimation with increased degrees of freedom,

    R. K. Patra, “A novel third-order nested array for DOA estimation with increased degrees of freedom,”IEEE Signal Process. Lett., vol. 32, pp. 1475–1479, 2025

    work page 2025

  11. [11]

    Sparse sensing with co-prime samplers and arrays,

    P. P. Vaidyanathan and P. Pal, “Sparse sensing with co-prime samplers and arrays,”IEEE Trans. Signal Process., vol. 59, no. 2, pp. 573–586, 2011

    work page 2011

  12. [12]

    Movable-antenna trajectory optimization for wireless sensing: CRB scaling laws over time and space,

    W. Ma, L. Zhu, and R. Zhang, “Movable-antenna trajectory optimization for wireless sensing: CRB scaling laws over time and space,” 2025. [Online]. Available: https://arxiv.org/abs/2509.14905

    work page arXiv 2025

  13. [13]

    Channel estimation for movable-antenna mimo systems via tensor decomposition,

    R. Zhang, L. Cheng, W. Zhang, X. Guan, Y . Cai, W. Wu, and R. Zhang, “Channel estimation for movable-antenna mimo systems via tensor decomposition,”IEEE Wireless Commun. Lett., vol. 13, no. 11, pp. 3089–3093, 2024

    work page 2024

  14. [14]

    Robust DOA estimation for movable antenna arrays with partial gain and phase errors,

    C. Ye, R. Zhang, C. Hu, L. Yao, W. Wu, and C. Yuen, “Robust DOA estimation for movable antenna arrays with partial gain and phase errors,”IEEE Wireless Commun. Lett., vol. 15, pp. 545–549, 2026

    work page 2026

  15. [15]

    Rotatable antenna-empowered wireless net- works: A tutorial,

    B. Zheng, Q. Wu, X. Xiong, Y . Tan, W. Zhu, T. Ma, C. You, X. Shao, L. Zhu, J. Tang, R. Schober, K.-K. Wong, and R. Zhang, “Rotatable antenna-empowered wireless networks: A tutorial,” 2026. [Online]. Available: https://arxiv.org/abs/2603.25559

    work page arXiv 2026

  16. [16]

    Rotatable antenna enabled wireless communication and sensing: Opportunities and challenges,

    B. Zheng, T. Ma, C. You, J. Tang, R. Schober, and R. Zhang, “Rotatable antenna enabled wireless communication and sensing: Opportunities and challenges,”IEEE Wireless Commum., pp. 1–8, 2025

    work page 2025

  17. [17]

    Rotatable antenna-enabled wireless communication: Modeling and optimization,

    B. Zheng, Q. Wu, T. Ma, and R. Zhang, “Rotatable antenna-enabled wireless communication: Modeling and optimization,”IEEE Trans. on Commun., vol. 74, pp. 6825–6842, 2026

    work page 2026

  18. [18]

    Efficient channel estimation for rotatable antenna-enabled wireless communication,

    X. Xiong, B. Zheng, W. Wu, X. Shao, L. Dai, M.-M. Zhao, and J. Tang, “Efficient channel estimation for rotatable antenna-enabled wireless communication,”IEEE Wireless Commun. Lett., vol. 14, no. 11, pp. 3719–3723, 2025. 6

    work page 2025

  19. [19]

    Rotatable antennas for near-field integrated sensing and communication,

    Y . Zhang, H. C. So, D. Niyato, and C. Masouros, “Rotatable antennas for near-field integrated sensing and communication,”IEEE Trans. Wireless Commun., vol. 25, pp. 10 986–11 001, 2026

    work page 2026

  20. [20]

    C. A. Balanis,Antenna Theory: Analysis and Design, 4th ed. Hoboken, NJ: John Wiley & Sons, 2015

    work page 2015