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REVIEW 4 major objections 5 minor 38 references

On-Site Beam Calibration for RIS-Aided Positioning Systems

T0 review · 4 major / 5 minor · reviewed 2026-07-31 · deepseek-v4-flash

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Pith's one-line read On-site RIS beam calibration can cut positioning error floor by replacing ideal beam models with a realistic model fit from a moving agent's measurements.

desk verdict The two-stage calibration framework is sensible and the measured data are a real asset, but the validation has a load-bearing contradiction about whether the headline numbers come from measured patterns or the paper's own generative model. read the letter →

arxiv 2607.24080 v1 pith:RPB5N3VW submitted 2026-07-27 eess.SP

Mengting Li , Hui Chen , Sigurd S. Petersen , Alireza Pourafzal , Huiping Huang , Ming Shen , Mikko Valkama , Henk Wymeersch This is my paper · ORCID
classification eess.SP
keywords RIS-aidedpositioningbeamcalibrationreconfigurableintelligentsurfacemodelmismatcherrorfloordelay-domainsparserecoveryabsolutelowerbound3Dpatternmeasurement
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

This paper tries to establish that a practical 3D reconfigurable-intelligent-surface (RIS) beam response can be estimated on site, in the deployment environment, well enough to remove most of the positioning error caused by using ideal beam models. The authors argue that hardware impairments, mutual coupling, and non-ideal phase tuning distort real RIS beams, and that these distortions can be captured by a product-form beam model whose parameters are fitted from signals received by a calibration agent at known locations. They propose a two-stage algorithm: first extract the RIS-reflected path from the received signal while suppressing line-of-sight and multipath components, then fit the model with alternating gradient-based optimization. Using measured 3D beam patterns from a 16x16 RIS prototype under 66 phase configurations, they report that calibration raises average beam-response similarity from 43.7% (ideal model) to 88.5%, and raises the probability that the absolute lower bound on positioning error stays below 0.5 m from 0.52 to 0.74. If correct, this would let deployed RIS-aided positioning systems operate near their information-theoretic accuracy without anechoic-chamber calibration.

What carries the argument

The load-bearing object is the separable product-form beam model b(phi)=g(phi) W^H a(phi), where a(phi) is the array steering vector, W is the effective codebook matrix absorbing mutual coupling and non-ideal phase tuning, and g(phi) is a common scalar element pattern. For estimation, the model is written as B=W^H A(Phi) Gamma, with Gamma a diagonal matrix absorbing the RIS-path channel gain and element-pattern response. The argument runs through two stages: a delay-domain sparse-recovery stage that uses noncoherent aggregation over codewords, high-resolution delay refinement, and known geometry to isolate the RIS-reflected path from the line-of-sight path and multipath; then an alternating

What would settle it

Measure a real RIS with strong per-element mutual coupling, fit the proposed model on a 1-degree angular grid, then evaluate the beam-response similarity on a held-out dense angular grid at 0.2-degree resolution; if the average similarity on held-out angles falls well below the reported 88.5% (or the ALB improvement vanishes), the separable model is the limiting factor rather than the estimation algorithm.

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Extended reading notes

Core claim

The central claim is that the mismatch between ideal and true RIS beams—not noise or geometry—is the dominant source of the positioning error floor, and that this mismatch can be largely removed by a calibration procedure that estimates the practical beam response directly. Concretely, the paper shows that fitting the model b(phi)=g(phi) W^H a(phi) (with the RIS-path gain absorbed into a diagonal matrix Gamma) to on-site measurements yields a beam representation that agrees with ground truth at 88.5% average beam-response similarity, compared with 43.7% for the ideal model. When the calibrated model is inserted into the positioning estimator, the probability that the absolute lower bound (AL

Load-bearing premise

The true beam is assumed to factor as a common scalar element pattern times a codebook-weighted steering vector, so any per-element coupling or edge effect that breaks this separability will leave the calibrated model unable to represent the low-power sidelobes.

Editorial extensions

If this is right

  • Positioning estimators can replace the ideal RIS beam model with the calibrated model, removing the systematic bias that otherwise caps accuracy.
  • The calibration works with the RIS's default codebook and standard OFDM signals, so no dedicated phase-tuning settings are needed during calibration.
  • Reducing the angular sampling step from 1 deg to 2 or 4 deg retains most of the positioning benefit (ALB below 0.5 m with probability around 0.72).
  • Wider signal bandwidth improves the accuracy of extracting the RIS-reflected path, which feeds directly into the calibrated beam model.
  • The method scales linearly with the number of calibration samples, codewords, and RIS elements, making it applicable to large RIS deployments.

Reading between the lines

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

  • The paper evaluates beam-response similarity on the same angular sampling grid used for fitting; a stronger test would hold out angles or randomize CA positions to measure generalization to off-grid directions, where low-power sidelobes are hardest to reproduce.
  • If per-element mutual coupling or edge effects break the scalar-element-pattern assumption, the product-form model cannot represent angle-dependent element responses; a testable extension is to fit separate element patterns per row/column or add a residual correction term and compare BRS on dense off-grid measurements.
  • The measured BRS already shows residual discrepancies in low-power sidelobes, so the method's practical gain for positioning may be concentrated in the main lobe and strong sidelobes; applications relying on weak reflections (e.g., long-range sensing) may need denser or higher-fidelity calibration.
  • A natural next step, noted implicitly by the far-field scope, is near-field calibration: at close range the plane-wave steering vector fails, and the fitted model would need a near-field correction; the same two-stage extraction and alternating fitting could be adapted to that regime.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes an on-site calibration framework for RIS beam responses in RIS-aided positioning systems. The method is two-stage: (i) a delay-domain sparse-recovery stage extracts the RIS-reflected channel response from signals collected by a calibration agent, using noncoherent aggregation across codewords, delay refinement, and geometry-assisted path identification; (ii) a gradient-based alternating estimator fits the separable beam model b(φ)=g(φ)W^H a(φ) of Eq. (11) to the extracted responses, estimating the effective codebook W and the diagonal gain-pattern matrix Γ. The authors validate the approach by integrating measured 3D beam patterns from a 16×16 RIS prototype under 66 phase configurations into simulations, reporting an average beam response similarity (BRS) of 88.5% for the calibrated model versus 43.7% for the ideal model, and an increase in the probability that the absolute lower bound (ALB) is below 0.5 m from 0.52 to 0.74. The positioning-oriented formulation and the use of measured patterns are valuable, but the validation as written has unresolved circularity and in-sample-evaluation concerns that bear directly on the headline claims.

Significance. If the reported gains are genuine, the contribution is significant: a practical, on-site calibration procedure that works under the RIS's default codebook, explicitly accounts for multipath, and improves the positioning error floor is of clear interest to the RIS-aided localization community. The gradient derivations in Eqs. (36)–(40) are algebraically consistent, the Step 1 design of aggregating delay-domain power over codewords is sensible, and the measurement campaign under 66 phase states is a genuine empirical asset. However, the paper does not ship code, proofs of identifiability/convergence, or a clearly separated training/test evaluation. The central validation evidence is currently compromised by the ambiguity between the generative-model statement in §II.B and the measured-pattern statement in §V.B, and by the fact that the BRS evaluation grid essentially coincides with the calibration sampling grid. These issues must be resolved before the headline numbers can be accepted as evidence about real-hardware behavior.

major comments (4)
  1. [§II.B and §V.B (ground-truth inconsistency)] Immediately after Eq. (11), the paper states 'We use the model in (11) as the generative beam model in the numerical results.' Section V.B instead says that measured complex 3D beam patterns were incorporated into the simulations by replacing the ideal model. These two statements describe different ground truths. If (11) is literally the generative model, then the ground-truth beams used for the BRS in Fig. 6 and the ALB in Fig. 8 are synthesized from the same separable scalar-element-pattern model that Step 2 estimates; the 88.5% BRS and the 0.52→0.74 ALB improvement would then be in-sample fits of the model to its own generator, not evidence about real RIS hardware. If the simulations use measured patterns, that must be stated unambiguously and the train/test split must be specified. The manuscript cannot be interpreted as it stands, and the headline claims rest on this unresolved ambi
  2. [Eq. (45) and footnote 4 (in-sample BRS)] Equation (45) defines BRS on the S calibration samples, while footnote 4 says the reported BRS is evaluated on a dense 1-degree grid over [−50,50]° azimuth and [−50,20]° elevation. The calibration sampling step is 1 degree over [−50,50]°×[−50,10]°, so the evaluation grid essentially coincides with the fitting grid (with only the 10–20° elevation strip being novel). Thus the BRS largely measures in-sample fit, not generalization to unsampled directions. The visible low-power sidelobe mismatch in Fig. 6(d) is exactly where the scalar-element-pattern assumption of Eq. (11) would fail, and this is hidden by evaluating on the fitting grid. The authors should report BRS on directions not used for calibration, e.g., by holding out a subset of CA positions or using an evaluation grid finer than the calibration grid, and should separately report the match in low-power sidelobe regions.
  3. [§III.B, Eq. (33) (identifiability and convergence)] The optimization in Eq. (33) is a non-convex factorization problem. The counting condition in Eq. (14) is only necessary and does not account for the unit-norm constraints or the fact that W and Γ are not jointly identifiable without further conditions. No identifiability analysis or convergence guarantee is provided for the alternating gradient/least-squares scheme; the gradient update in Eq. (36) with an unspecified learning rate has no guarantee of reaching a global or even local optimum. Since the central claim is that the calibrated model is accurate enough to reduce the positioning error floor, the paper should either provide identifiability and convergence results for the alternating scheme, or at least include a sensitivity analysis over random initializations and learning rates to demonstrate that the reported BRS/ALB values are not initialization- or step-size-dependent.
  4. [Table II and §III (unspecified hyperparameters)] The algorithm depends on several hyperparameters that are not specified in Table II or the text: the learning rate l_r in Eq. (36), the regularization constant ϵ_0 in Eq. (40), the stopping thresholds I_max and ϵ_res in Eq. (24), the delay-grid size N_τ in Eq. (19), the path-identification tolerance ε in Eq. (27), and the number of epochs N_ep in Section III-C. Without these values, the reported BRS and ALB curves cannot be reproduced. At minimum, the authors should list the values used and report sensitivity to the most critical parameters (especially l_r, N_τ, and ε).
minor comments (5)
  1. [Eq. (45)] The notation [\bar B]_{g,:} is used for the ground-truth beam response, but the definition of \bar B is not made explicit in Section IV.A. Clarify that \bar B is the measured/interpolated ground-truth matrix (or the synthetic matrix if the generative model is intended).
  2. [Table I vs Table II] Table I lists the measured elevation range as [−50°, 0°], while Table II and Section V.B use an elevation coverage of [−50°, 10°]. Reconcile these ranges and state which one is used for calibration sampling and which for BRS evaluation.
  3. [Eq. (52)] The ALB in Eq. (52) is defined as the norm of the bias term only. Please state explicitly that this is a lower bound on RMSE due to model mismatch and not the full MSE or MCRB.
  4. [Eq. (15)] The Hadamard division in Eq. (15) assumes the pilot matrix X has no zero entries. State the pilot-sequence assumption or use a regularized division.
  5. [Notation] In the notation list, 'a◦b' is said to denote outer product, while Eq. (10) uses '⊙' for element-wise product. Please double-check the symbols so that the element-wise model in Eq. (10) is unambiguous.

Circularity Check

2 steps flagged · score 6.0 of 10

Validation partly circular: Sec. II.B makes Eq. (11) its own generator, conflicting with Sec. V.B; the 1° BRS headline is in-sample on the calibration grid.

  1. self definitional [Section II.B (Eq. 11) vs. Section V.B (measured-pattern integration)]
    "We use the model in (11) as the generative beam model in the numerical results, since RISs are typically deployed at large scales to ensure sufficient reflected signal strength [34]. ... The measured complex 3D beam patterns were incorporated into the numerical simulation framework to generate the received signals at the CA/UE side by replacing the ideal beam model with the measured beam responses."

    If the first sentence is operative, the ground-truth beams used for the BRS and ALB evaluations are synthesized from Eq. (11), the very model whose parameters Step 2 estimates. Fitting a model to data generated by that model demonstrates parameter identification and in-sample self-consistency, not the ability of the separable scalar-element-pattern ansatz to represent a measured RIS with per-element coupling/edge effects. Section V.B substitutes measured patterns but never resolves the conflict, so the provenance of the validation ground truth is load-bearing and unresolved.

  2. fitted input called prediction [Section IV.A, Eq. (45); Fig. 6; footnote 4]
    "However, in practice, the CA can only be moved to a finite number of locations. To improve measurement efficiency, we restrict the evaluation to the angular range of interest and use a total of S sampled directions for calibration. ... Here, [B]_{g,:} and [\bar{B}]_{g,:} denote the g-th rows of \hat{B} and \bar{B} according to (13), respectively, i.e., the beam responses of the g-th codeword over the S sampled directions."

    Eq. (45) defines BRS over the S sampled directions, and Step 2's loss (32)-(33) is minimized on exactly those directions. At 1° sampling, the CA grid (az [-50,50], el [-50,10]) coincides with most of the evaluation grid in Fig. 6, so the headline 88.5% vs 43.7% is a training-fit comparison. Footnote 4's 'dense angular grid with 1° resolution... rather than only on the calibration sample points' does not change the 1° case, where the two grids nearly coincide. The 2°/4° BRS and the ALB over a new UE area provide some out-of-sample evidence, but the flagship 1° number is in-sample.

full rationale

The central claim that the calibrated beam model achieves 88.5% BRS and raises the ALB<0.5 m probability from 0.52 to 0.74 rests on an unresolved ambiguity about the ground truth. Section II.B literally states that Eq. (11) is the generative beam model in the numerical results; since Eq. (11) is also the model being estimated, all simulated ground truth would be generated by the same parameterized family. Under that reading, the headline BRS/ALB numbers are self-consistency checks, not evidence about real RIS hardware. Section V.B instead says measured anechoic 3D patterns were interpolated into the simulations, which would provide independent support, but the paper never reconciles these two statements. Independently of that conflict, the BRS metric of Eq. (45) is defined on the S sampled directions used for calibration, and with the 1° sampling step this is essentially the same grid as the dense evaluation grid in Fig. 6. Thus the signature 88.5% figure is an in-sample fit metric; only the 2°/4° sampling results (87.3%, 85.2%) and the ALB evaluation at new UE locations are partly out-of-sample. The model form is inherited from the authors' prior work [7], but external references [33], [34] and the measured-pattern experiments could validate it, so self-citation alone is not the main issue. The combination of the unresolved generator statement and the in-sample headline metric makes the validation partially circular, though the out-of-sample elements prevent the whole paper from reducing to a tautology.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claim rests on the model form (11), far-field/frequency-flat assumptions, LOS availability for path identification, and the unproven identifiability of the non-convex factorization. All are stated or implicit in Sections II-III; none are independently validated beyond the reported simulation.

free parameters (5)
  • ε tolerance in geometry-assisted RIS path identification
    Hand-chosen threshold in Eq. (27); no value or sensitivity analysis given, despite controlling whether a multipath component is mistaken for the RIS path.
  • learning rate l_r
    Step-size in Eq. (36); not specified, and convergence of the non-convex alternating optimization depends on it.
  • regularization constant ϵ_0
    Stabilizer in Eq. (40); not specified.
  • I_max, ϵ_res, N_τ
    Stopping criteria and delay-grid size in Eqs. (19)-(24); not specified, affecting path-extraction fidelity.
  • RCS coefficient σ_RCS = 0.5 m^2
    Multipath strength in the validation scenario (Table II); chosen by hand, and all reported error/CDF results depend on it.
assumptions (6)
  • domain assumption Far-field plane-wave steering vector in Eq. (9) with known array geometry.
    The entire beam model assumes planar wavefronts at the RIS and known element positions; near-field effects are excluded in the conclusion.
  • domain assumption Scalar element-pattern approximation: b(φ)=g(φ)W^H a(φ), all elements share the same pattern and edge effects are neglected.
    Eq. (11) is the generative calibration model; if false, the calibrated model cannot represent the true beam.
  • domain assumption Beam response is frequency-flat over the 100 MHz band.
    Stated in Section II-A and Section V-A.
  • domain assumption LOS path is present and is the earliest detected path; BS/RIS/CA positions and common clock offset are known.
    Step 1 (Eq. 26) uses the earliest detected path as LOS reference and delay differences to identify the RIS path; obstruction breaks this.
  • domain assumption Multipath consists of single-bounce BS-SP-CA and BS-SP-RIS-CA/BS-RIS-SP-CA components with delays separable under the given bandwidth.
    Channel model Eqs. (4)-(6) and simulation setup; delay resolution limits separation (Fig. 5).
  • domain assumption The non-convex factorization problem (33) with unit-norm constraint has a unique solution recoverable by alternating gradient descent from the ideal-codebook initialization.
    Only a counting condition (14) is given; no identifiability or convergence analysis is provided; success depends on initialization near the true W.

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Cite this review

Pith. "Pith review of On-Site Beam Calibration for RIS-Aided Positioning Systems." pith.science (2026). https://pith.science/paper/RPB5N3VW

@misc{pith2026260724080,
  author       = {Pith},
  title        = {Pith review of: On-Site Beam Calibration for RIS-Aided Positioning Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RPB5N3VW}},
  note         = {Machine review of arXiv:2607.24080}
}
read the original abstract

High precision positioning is a key enabler for next-generation communication applications such as smart transportation and augmented reality. Reconfigurable intelligent surface (RIS) technology can enhance positioning by providing additional angular information and improving coverage under obstructed propagation conditions. However, true RIS beams can differ significantly from the simplified or ideal beam response models commonly used in RIS-aided positioning, leading to beam model mismatch and an elevated positioning error floor. This paper proposes an on-site RIS beam calibration framework that reduces this error floor by estimating a realistic 3D RIS beam response model from on-site measurements. The proposed calibration algorithm first extracts the RIS-reflected channel response from signals received by a calibration agent sampling the angular range of interest, using delay-domain sparse recovery, and then estimates the beam model parameters with a gradient-based estimator. To validate the proposed framework, 3D beam patterns under 66 phase modulations were measured and incorporated into simulations. With an angular sampling step of 1 deg, the calibrated model achieves an average beam response similarity of 88.5% with respect to the ground truth, compared with 43.7% for the ideal model. The probability that the absolute lower bound of the positioning error is below 0.5m increases from 0.52 without calibration to 0.74 after calibration, showing that on-site RIS beam calibration effectively reduces the positioning error floor caused by true beam model mismatch.

Figures

Figures reproduced from arXiv: 2607.24080 by the authors.

Figure 1
Figure 1. An illustration of an RIS-aided positioning system under calibration. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. 3D RIS beam pattern measurement setup, showing the setup in the [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Visualization of the 3D RIS beam patterns with the measured patterns obtained at 5 GHz. The white asterisks shown in the figures indicate the [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: CDF of the RIS channel estimation error for different multipath [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Visualization of RIS beam response power. Panels (a)–(c) show the reconstructed calibrated beams with angular sampling steps of [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]
Figure 7
Figure 7. Figure 7: Visualization of ALB when the UE moves within a [PITH_FULL_IMAGE:figures/full_fig_p011_7.png]
Figure 8
Figure 8. Figure 8: CDF of the ALB when the UE moves within a [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]

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Reference graph

Works this paper leans on

38 extracted references · 1 linked inside Pith

  1. [1]

    A DL-based high-precision positioning method in challenging urban scenarios for B5G CCUA Vs,

    K. Gaoet al., “A DL-based high-precision positioning method in challenging urban scenarios for B5G CCUA Vs,”IEEE Journal on Selected Areas in Communications, vol. 41, no. 6, pp. 1670–1687, 2023

    2023

  2. [2]

    Reconfigurable intelligent surfaces: Principles and opportunities,

    Y . Liuet al., “Reconfigurable intelligent surfaces: Principles and opportunities,”IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1546–1577, 2021

    2021

  3. [3]

    Multi-RIS-enabled 3D sidelink positioning,

    H. Chenet al., “Multi-RIS-enabled 3D sidelink positioning,”IEEE Transactions on Wireless Communications, vol. 23, no. 8, pp. 8700– 8716, 2024

    2024

  4. [4]

    RIS-enabled SISO localization under user mobility and spatial-wideband effects,

    K. Keykhosraviet al., “RIS-enabled SISO localization under user mobility and spatial-wideband effects,”IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 5, pp. 1125–1140, 2022

    2022

  5. [5]

    Effects of mutual coupling on degree of freedom and antenna efficiency in holographic MIMO communications,

    S. S. Yuanet al., “Effects of mutual coupling on degree of freedom and antenna efficiency in holographic MIMO communications,”IEEE Open Journal of Antennas and Propagation, vol. 4, pp. 237–244, 2023

    2023

  6. [6]

    A review of mutual coupling in MIMO systems,

    X. Chenet al., “A review of mutual coupling in MIMO systems,” IEEE Access, vol. 6, pp. 24 706–24 719, 2018

    2018

  7. [7]

    RIS beam calibration for ISAC systems: Modeling and performance analysis,

    M. Liet al., “RIS beam calibration for ISAC systems: Modeling and performance analysis,”arXiv preprint arXiv:2505.15403, 2025

    arXiv 2025

  8. [8]

    Over-the-air phase calibration of reconfigurable intelligent surfaces,

    W. Zhanget al., “Over-the-air phase calibration of reconfigurable intelligent surfaces,”IEEE Wireless Communications Letters, vol. 12, no. 4, pp. 664–668, 2023

    2023

Show all 38 references
  1. [9]

    Phases calibration of RIS using backpropagation algorithm,

    W. Zhanget al., “Phases calibration of RIS using backpropagation algorithm,” in2024 IEEE/CIC International Conference on Commu- nications in China (ICCC). IEEE, 2024, pp. 445–449

    2024

  2. [10]

    Efficient RIS calibration and beamforming with discrete phase states,

    N. Bordaet al., “Efficient RIS calibration and beamforming with discrete phase states,” inICC 2025-IEEE International Conference on Communications. IEEE, 2025, pp. 6826–6831

    2025

  3. [11]

    JrCUP: Joint RIS calibration and user positioning for 6G wireless systems,

    P. Zhenget al., “JrCUP: Joint RIS calibration and user positioning for 6G wireless systems,”IEEE Transactions on Wireless Communi- cations, vol. 23, no. 6, pp. 6683–6698, 2023

    2023

  4. [12]

    Large-scale planar array near-field calibration based on plane wave spectrum Maclaurin expansion,

    Y . Tanget al., “Large-scale planar array near-field calibration based on plane wave spectrum Maclaurin expansion,”IEEE Transactions on Antennas and Propagation, 2026

    2026

  5. [13]

    Multiprobe-enabled over-the-air calibration of millimeter-wave antenna arrays: concepts and experimental valida- tions,

    F. Zhanget al., “Multiprobe-enabled over-the-air calibration of millimeter-wave antenna arrays: concepts and experimental valida- tions,”IEEE Antennas and Propagation Magazine, vol. 66, no. 4, pp. 14–25, 2024

    2024

  6. [14]

    Large-scale phased array calibration based on amplitude-only measurement with the multiround grouped-REV method,

    W. Fanet al., “Large-scale phased array calibration based on amplitude-only measurement with the multiround grouped-REV method,”IEEE Transactions on Antennas and Propagation, vol. 72, no. 1, pp. 454–465, 2023

    2023

  7. [15]

    Phased array calibration based on measured complex signals in a compact multiprobe setup,

    Y . Zhanget al., “Phased array calibration based on measured complex signals in a compact multiprobe setup,”IEEE Antennas and Wireless Propagation Letters, vol. 21, no. 4, pp. 833–837, 2022

    2022

  8. [16]

    Phased array calibration based on rotating-element har- monic electric-field vector with time modulation,

    S. Liet al., “Phased array calibration based on rotating-element har- monic electric-field vector with time modulation,”IEEE Transactions on Antennas and Propagation, 2025

    2025

  9. [17]

    A novel amplitude-only measurement method to determine element fields in phased arrays,

    T. Takahashiet al., “A novel amplitude-only measurement method to determine element fields in phased arrays,”IEEE Transactions on Antennas and Propagation, vol. 60, no. 7, pp. 3222–3230, 2012

    2012

  10. [18]

    Fast amplitude-only measurement method for phased array calibration,

    R. Longet al., “Fast amplitude-only measurement method for phased array calibration,”IEEE Transactions on Antennas and Propagation, vol. 65, no. 4, pp. 1815–1822, 2016

    2016

  11. [19]

    Multi-element phased array calibration method by solving linear equations,

    R. Longet al., “Multi-element phased array calibration method by solving linear equations,”IEEE Transactions on Antennas and Propagation, vol. 65, no. 6, pp. 2931–2939, 2017

    2017

  12. [20]

    Improved over-the-air phased array calibration based on measured complex array signals,

    F. Zhanget al., “Improved over-the-air phased array calibration based on measured complex array signals,”IEEE Antennas and Wireless Propagation Letters, vol. 18, no. 6, pp. 1174–1178, 2019

    2019

  13. [21]

    A unified framework and sparse Bayesian per- spective for direction-of-arrival estimation in the presence of array imperfections,

    Z.-M. Liuet al., “A unified framework and sparse Bayesian per- spective for direction-of-arrival estimation in the presence of array imperfections,”IEEE Transactions on Signal Processing, vol. 61, no. 15, pp. 3786–3798, 2013

    2013

  14. [22]

    A simultaneous wideband calibration for digital beam- forming arrays at short distances [measurements corner],

    Y . Jiet al., “A simultaneous wideband calibration for digital beam- forming arrays at short distances [measurements corner],”IEEE Antennas and Propagation Magazine, vol. 63, no. 3, pp. 102–111, 2021

    2021

  15. [23]

    In-situ calibration of antenna arrays within wireless locating systems,

    E. Sippelet al., “In-situ calibration of antenna arrays within wireless locating systems,”IEEE Transactions on Antennas and Propagation, vol. 68, no. 4, pp. 2832–2841, 2019

    2019

  16. [24]

    A technique for in situ calibration of an antenna array using spherical mode expansions,

    J. D. Kuricet al., “A technique for in situ calibration of an antenna array using spherical mode expansions,”IEEE Transactions on An- tennas and Propagation, vol. 72, no. 5, pp. 4327–4338, 2024

    2024

  17. [25]

    In situ calibration of antenna arrays for positioning with 5G networks,

    M. Panet al., “In situ calibration of antenna arrays for positioning with 5G networks,”IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 10, pp. 4600–4613, 2023

    2023

  18. [26]

    Radio localization and mapping with recon- figurable intelligent surfaces: Challenges, opportunities, and research directions,

    H. Wymeerschet al., “Radio localization and mapping with recon- figurable intelligent surfaces: Challenges, opportunities, and research directions,”IEEE Vehicular Technology Magazine, vol. 15, no. 4, pp. 52–61, 2020

    2020

  19. [27]

    On the impact of hardware impairments on RIS- aided localization,

    C. ¨Ozt¨urket al., “On the impact of hardware impairments on RIS- aided localization,” inICC 2022-IEEE International Conference on Communications. IEEE, 2022, pp. 2846–2851

    2022

  20. [28]

    Path loss in reconfigurable intelligent surface- enabled channels,

    S. W. Ellingson, “Path loss in reconfigurable intelligent surface- enabled channels,” in2021 IEEE 32nd Annual International Sym- posium on Personal, Indoor and Mobile Radio Communications (PIMRC). IEEE, 2021, pp. 829–835

    2021

  21. [29]

    Over-the-air array calibration of mmWave phased array in beam-steering mode based on measured complex signals,

    Z. Wanget al., “Over-the-air array calibration of mmWave phased array in beam-steering mode based on measured complex signals,” IEEE Transactions on Antennas and Propagation, vol. 69, no. 11, pp. 7876–7888, 2021

    2021

  22. [30]

    How practical phase-shift errors affect beamforming of reconfigurable intelligent surface?

    J. Yanget al., “How practical phase-shift errors affect beamforming of reconfigurable intelligent surface?”IEEE Transactions on Commu- nications, vol. 71, no. 10, pp. 6130–6145, 2023

    2023

  23. [31]

    Employing high-dimensional RIS information for RIS- aided localization systems,

    T. Wuet al., “Employing high-dimensional RIS information for RIS- aided localization systems,”IEEE Communications Letters, vol. 28, no. 9, pp. 2046–2050, 2024

  24. [32]

    Approximate maximum-likelihood RIS-aided po- sitioning,

    W. Zhanget al., “Approximate maximum-likelihood RIS-aided po- sitioning,”IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 8859–8875, 2023

    2023

  25. [33]

    Mutual coupling in phased arrays: A review,

    H. Singhet al., “Mutual coupling in phased arrays: A review,” International Journal of Antennas and Propagation, vol. 2013, no. 1, p. 348123, 2013

    2013

  26. [34]

    Intelligent reflecting surface versus decode-and- forward: How large surfaces are needed to beat relaying?

    E. Bj ¨ornsonet al., “Intelligent reflecting surface versus decode-and- forward: How large surfaces are needed to beat relaying?”IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 244–248, 2019

    2019

  27. [35]

    Study on NR MIMO over-the-air (OTA) testing,

    3GPP, “Study on NR MIMO over-the-air (OTA) testing,” 3rd Gener- ation Partnership Project (3GPP), Technical Report TR 38.827, 2020, version 16.0.0

    2020

  28. [36]

    Modeling and analysis of OFDM-based 5G/6G localization under hardware impairments,

    H. Chenet al., “Modeling and analysis of OFDM-based 5G/6G localization under hardware impairments,”IEEE Transactions on Wireless Communications, vol. 23, no. 7, pp. 7319–7333, 2023

    2023

  29. [37]

    Statistical signal processing: estimation theory,

    S. M. Kay, “Statistical signal processing: estimation theory,”Prentice Hall, vol. 1, pp. Chapter–3, 1993

    1993

  30. [38]

    Design of low-cost single-layer 2-bit reflective pro- grammable metasurface based on folded ground,

    P. Liet al., “Design of low-cost single-layer 2-bit reflective pro- grammable metasurface based on folded ground,”IEEE Transactions on Microwave Theory and Techniques, vol. 71, no. 8, pp. 3455–3465, 2023

    2023

Pith tools

Reviewed July 31, 2026 · model on record in the stance chip above.