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Empirical Study on Near-Field and Spatial Non-Stationarity Modeling for THz XL-MIMO Channel in Indoor Scenario

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Indoor THz XL-MIMO channels are both near-field and spatially non-stationary, and a hybrid model combining specular reflection, point-source scattering, and statistically generated amplitude attenuation factors reproduces measured…

desk verdict A useful measurement-driven NF phase model with a real in-sample validation problem on the SnS side; worth reviewing seriously, but the headline agreement for the statistical SnS generator should be presented as a consistency check, not an independent test. read the letter →

arxiv 2505.09398 v1 pith:C6ST7BCM submitted 2025-05-14 eess.SP

Huixin Xu , Jianhua Zhang , Pan Tang , Hongbo Xing , Chong Han , Lei Tian , Qixing Wang , Guangyi Liu This is my paper · ORCID
classification eess.SP
keywords terahertzcommunicationsXL-MIMOnear-fieldpropagationspatialnon-stationaritychannelmeasurementmodelingamplitudeattenuationfactorindoor
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 argues that indoor THz extremely large-scale MIMO (XL-MIMO) channels cannot be captured by the usual far-field plane-wave assumption, because users sit in the near field and different antenna elements see different multipath powers. It proposes a hybrid channel model that treats reflections from large flat surfaces as images, small scatterers as point sources, and adds statistically generated amplitude attenuation factors to describe smooth power variation across the array. Using 100 GHz and 132 GHz measurements with 301- and 531-element arrays, the model matches measured entropy capacity, Demmel condition number, spatial correlation, channel gain, Rician K-factor, and RMS delay spread, whereas far-field and stationary baselines deviate clearly. The contribution is a measurement-based, low-cost statistical replacement for deterministic ray tracing in THz XL-MIMO evaluation.

What carries the argument

The load-bearing object is the amplitude attenuation factor (AAF), a continuous value in $[0,1]$ assigned to each path at each antenna element, normalized by the path's maximum amplitude across the array. It carries spatial non-stationarity: instead of binary cluster visibility, each path's power varies smoothly along the array, with a Beta-distributed marginal and an exponential spatial autocorrelation generated by Gaussian rank matching. The second mechanism is the hybrid near-field matrix $A(f)$, which computes element-wise amplitude, phase, delay, and angle for every NLoS path by choosing between the specular reflection model and the point-source model. Together they multiply the reference-element channel response element-wise to produce the final channel.

What would settle it

A direct test: using the published AAF constants ($p\sim\mathrm{Logn}(0.37, 0.58)$, $q = 0.48\ln(p) + 1.03$, $d_{\mathrm{corr}}\sim\mathrm{TruncExp}(40.61)$), generate channels for a different indoor room or a different frequency and compare the entropy-capacity and spatial-correlation CDFs with new measurements; if the distributional discrepancies grow well beyond the values reported here, or if the measured AAF marginals differ materially from the Beta and log-normal fits, the stationarity assumption fails. A narrower check is to measure inter-element phase differences for a large flat reflector at a larger Tx–Rx distance where spherical curvature is weaker: the specular reflection model predicts the mirror-image phase, and if the point-source model matches instead, the hybrid selection rule breaks.

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Extended reading notes

Core claim

At THz frequencies with arrays of hundreds of elements, multipath parameters such as phase, delay, angle, and power vary from element to element in ways the far-field model misses, and this variation has two distinct sources: spherical-wave near-field propagation and spatial non-stationarity from blockage or inconsistent reflection and scattering. The paper's central claim is that both effects can be modeled jointly by computing element-wise distances and angles for each path using either a specular reflection model (the mirror image of the receiver) or a scatterer-excited point-source model, then multiplying the reference-element channel response by an amplitude attenuation factor per path and element. The attenuation factors are modeled statistically: each path's normalized amplitudes follow a Beta distribution whose shape parameter $p$ is log-normal with mean 0.37 and variance 0.58, with $q = 0.48\ln(p) + 1.03$, and the spatial autocorrelation decays exponentially with a truncated-exponential decorrelation coefficient. A rank-matching, copula-based procedure generates spatially correlated attenuation factors that preserve the Beta marginal distribution. Validated against the measurements, the hybrid model produces small distributional discrepancies across all six metrics, while the far-field model and the binary visibility-region and stationary-spatial models produce substantially larger deviations.

Load-bearing premise

The model's load-bearing premise is that the power-variation statistics fitted in one indoor room—the Beta shape parameters and the exponential decorrelation coefficient—can be reused to generate new channels in the same kind of environment; the paper fits these statistics and validates against the same measurement campaign, so transfer to other rooms or frequencies is not yet established.

Editorial extensions

If this is right

  • Far-field plane-wave models underestimate entropy capacity and overestimate Demmel condition number in indoor THz XL-MIMO, so system evaluations using far-field assumptions will misjudge spatial multiplexing potential.
  • The choice between specular and point-source modeling matters most when the line-of-sight path is weak; in LoS-dominated links the NLoS modeling differences are masked, but after removing LoS, the point-source model overestimates entropy capacity and the specular model underestimates it.
  • Continuous amplitude attenuation factors reproduce measured spatial correlation and distributions of channel gain, Rician K-factor, and delay spread better than binary visibility-region models, which can produce artificial discontinuities.
  • Element-dependent angles combined with directional antenna patterns cause sizable power variation across the array, up to about 5 dB for the LoS path in the measured 531-element case; omitting either effect reduces the near-field model to far-field accuracy.
  • The statistical AAF generation procedure is a low-complexity substitute for deterministic ray-tracing generation of spatial non-stationarity, making the model easier to embed in system-level simulations.

Reading between the lines

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

  • Editorial inference: the portable result is the rank-matching generation method, not the six fitted constants; in a different indoor room or frequency band one would need to re-estimate $p$, $q$, and the decorrelation coefficient, but the same copula-based recipe should transfer.
  • Editorial inference: the observation that the specular component remains dominant across several surface roughness levels at 100–132 GHz suggests a testable threshold: when surface roughness height grows beyond a fraction of a wavelength, point-source or diffuse-scattering modeling should take over, and the paper's material set already hints at this ordering.
  • Editorial inference: the entropy-capacity and condition-number results imply that precoding and beamforming designs built on far-field plane-wave channel models will be suboptimal in THz XL-MIMO near-field conditions, and the generated channels provide a testbed for such designs.
  • Editorial inference: the same rank-matching construction could be reused for other element-dependent parameters such as differential delays or angles, provided their spatial autocorrelation is also well described by an exponential decay.
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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

3 major / 5 minor

Summary. The paper reports THz XL-MIMO channel measurements at 100 GHz (301-element ULA) and 132 GHz (531-element ULA) in indoor scenarios and uses them to motivate a channel model that jointly captures near-field spherical-wave propagation and spatial non-stationarity. The near-field component is a hybrid model combining a scatterer-excited point-source model (SPM) for small scatterers and a specular reflection model (SRM) for reflecting surfaces, with element-wise amplitude, phase, delay, and angle updates. The spatial non-stationarity component is a statistical model of amplitude attenuation factors (AAFs) based on a Beta marginal distribution, an exponential spatial autocorrelation, and a rank-matching generation procedure. The model is validated by comparing simulated and measured entropy capacity, Demmel condition number, spatial correlation, channel gain, Rician K-factor, and RMS delay spread, with Cramér–von Mises distances reported in Tables III and IV.

Significance. If the full model were shown to generalize, this would be a useful contribution: the 100/132 GHz dual-band XL-MIMO measurement campaign is substantial, the direct inter-element phase comparisons in Figs. 5 and 8 are physically convincing and provide strong support for the SRM/SPM dichotomy, and the rank-matching AAF generator is a low-complexity statistical alternative to deterministic ray tracing. The paper also makes a fair attempt to quantify model fidelity against several system- and channel-level metrics. However, the predictive claim is currently weakened by the fact that the SnS statistics are fitted and validated on the same measurement case, so the paper's central 'closely aligns with measurements' assertion is not yet established for unseen scenarios.

major comments (3)
  1. [Section III-C and Section IV-B] The AAF distribution parameters (p ~ Logn(0.37, 0.58), q = 0.48 ln(p) + 1.03, d_corr ~ TruncExp(40.61)) are fitted to the 248 SnS paths identified across the 12 Rx positions of Case 3, and the same Case 3 channels are then used in Section IV-B to validate the proposed SnS model against measurement. This is an in-sample evaluation, so the close CvM values in Tables III and IV and the agreement in Figs. 17 and 18 may reflect the fit rather than predictive accuracy. Please provide a held-out validation, for example by fitting the statistics on one subset of Case 3 positions and validating on the remaining positions, or by applying the fitted model to Case 2 or Case 4; alternatively, the claims should be explicitly restricted to characterization of the measured environment rather than predictive modeling.
  2. [Eq. (26) vs. Eqs. (13)-(14)] The average spatial correlation metric used to validate the SnS model, Eq. (26), is the same type of spatial autocorrelation quantity whose exponential decay coefficient d_corr was estimated from the measurements via Eqs. (13)-(14). The agreement in Fig. 17 is therefore partly a consistency check of the fitting procedure rather than an independent confirmation of the model's SnS behavior. Please either add validation metrics that are not directly derived from the fitted ACF, or state explicitly which parts of the reported agreement are self-consistency checks.
  3. [Footnote to Table II and abstract/conclusion] The footnote to Table II states that the distribution parameters for other deployment scenarios require additional measurements or ray tracing, which directly limits the generality of the proposed statistical SnS model. This is in tension with the abstract's and conclusion's claim that the model 'closely aligns with measurements' and provides an effective characterization of THz XL-MIMO channels as a low-cost statistical replacement. Please temper the conclusions to reflect that the fitted parameters are environment-specific, or provide evidence of transferability across at least one independent measurement scenario.
minor comments (5)
  1. [Section II-C, Power paragraph] The sentence 'This indicates that imply that noticeable amplitude variation...' contains a grammatical error and should read 'This indicates that noticeable amplitude variation...'.
  2. [Section III-C, Eq. (21)] The conversion in Eq. (21) is effectively vacuous under the stated convention that the reference element amplitude equals the maximum amplitude; this should be stated more clearly so that readers do not expect a nontrivial conversion step.
  3. [Section IV-B, entropy capacity equation] In Eq. (23), the SNR parameter Γ is given as 15 dB but is used directly in a linear-argument capacity formula; please clarify whether Γ is a linear SNR and, if not, convert it explicitly.
  4. [Tables III and IV] The tables do not state which measurement case is used for the NF validation metrics (capacity and condition number) versus the SnS validation metrics; please add explicit case identifiers to the tables and to the corresponding figure captions.
  5. [General presentation] There are several typographical issues, such as 'Cram ´er–von Mises' with an anomalous space, inconsistent use of 'Rx' and 'receiver', and missing commas in compound sentences; a careful proofread would improve readability.

Circularity Check

2 steps flagged · score 6.0 of 10

SnS validation is in-sample: AAF statistics and spatial-correlation decay are fitted to Case 3, then re-validated against the same Case 3 data; Eq. (26) largely restates the fitted ACF.

  1. fitted input called prediction [Section III-C, Eqs. (13)-(14); Section IV-B-2, Eq. (26) and Fig. 17]
    "d_corr represents the spatial autocorrelation decay coefficient, estimated by minimizing the mean square error between the measured ACF and the exponential model. ... The accuracy of the generated AAF is evaluated by analyzing the average spatial correlation of the SnS channel, which is defined as: ... As shown in Fig. 17, the simulated SnS channel exhibits spatial correlation trends that closely match the measured results."

    The decay coefficient d_corr used to generate AAFs is fitted from the measured ACF in Eq. (13) via the exponential model Eq. (14). The validation metric in Eq. (26) is a path-averaged version of the same spatial autocorrelation quantity, computed on the generated and measured H_SnS. Since the generator imposes the covariance exp(-d_corr|i-j|) in Eq. (16), the agreement in Fig. 17 is a consistency check with the fit rather than an independent confirmation of the SnS model.

  2. fitted input called prediction [Section III-C (AAF distribution fitting); Section IV-B-2 and Fig. 18 (validation); Table II footnote]
    "To address this, a statistical modeling approach is adopted. Accordingly, all identified SnS paths across the 12 Rx positions in Case 3 were analyzed statistically. A total of 248 SnS paths were selected for analysis. ... Fig. 18. CDFs of channel statistical parameters across all XL-MIMO elements in Case 3, comparing measurement results with model simulations: (a) Channel gain, (b) Rician K-factor, (c) RMS delay spread. ... Distribution parameters for other deployment scenarios can be obtained through additional channel measurements or ray-tracing simulations tailored to specific environments."

    The Beta parameters (mu=0.37, sigma=0.58, q=0.48ln(p)+1.03) and the correlation coefficient distribution (lambda=40.61) are estimated from all SnS paths in Case 3. The validation then compares simulations generated with those fitted parameters against measurements from the same Case 3 (Table IV, Fig. 18). The CvM agreement therefore shows the generated AAFs reproduce the statistics they were fitted to, not that the model predicts a new deployment. The footnote explicitly concedes that the distribution parameters are environment-specific and would need new measurements or ray tracing elsewhere.

full rationale

The near-field part of the paper is genuinely non-circular. The hybrid SPM/SRM phase model is derived from geometric propagation distances in Eqs. (3)-(10) and is checked directly against measured inter-element phase differences in Case 1 (Figs. 5 and 8); no parameter of that phase model is fitted to those phase curves. The SnS part, however, contains a circular validation loop. The AAF marginal parameters (mu_p=0.37, sigma_p=0.58, q=0.48 ln p + 1.03) and the spatial decay coefficient (lambda_corr=40.61) are estimated from all identified SnS paths in Case 3 (Section III-C). The validation section then compares generated channels against Case 3 measurements (Section IV-B, Tables III-IV, Fig. 18), so the reported CvM agreement for SnS metrics is an in-sample goodness-of-fit, not an out-of-sample prediction. This is made explicit by the Table II footnote, which concedes that distribution parameters for other environments require new measurement or ray-tracing fits. Moreover, the average-spatial-correlation validation metric in Eq. (26) is an aggregate of the same spatial ACF that was used to fit d_corr in Eqs. (13)-(14); reproducing it demonstrates consistency with the fit, not independent confirmation. The channel-gain/K-factor/delay-spread comparisons are also weakened by the fact that H_ref is extracted from measurement data (Implementation Step 1). The NF conclusions and the qualitative finding that VR and SS models fail to reproduce continuous SnS remain supported, but the paper's central claim that the proposed statistical SnS model 'closely aligns with measurements' is not tested on held-out data. Score 6: partial circularity confined to the SnS validation component.

Assumptions & free parameters 5 free parameters · 6 assumptions · 2 invented entities

The paper's physical near-field phase model adds at most standard geometric equations with no free parameters, and the SRM-vs-SPM distinction is validated directly against phase measurements. The statistical SnS component, however, carries five fitted parameters (mu_p, sigma_p, xi, gamma, lambda_corr) plus a classification threshold, all estimated from the same Case 3 data used for validation. These fitted constants do the work of making the simulated channel statistics match the measured ones; they are not derived from first principles and are not predicted. The AAF is a statistical representation rather than a new physical entity, but it functions as the model's free knob.

free parameters (5)
  • Beta shape parameter p of AAF distribution: log-normal mean mu_p = 0.37
    Fitted to the 248 SnS paths extracted from Case 3 measurements (Section III-C, Fig. 10a). Governs the marginal distribution of generated AAFs in Eq. (15).
  • Beta shape parameter p: log-normal std sigma_p = 0.58
    Fitted to the same SnS path statistics (Fig. 10a).
  • Relationship q = xi*ln(p) + gamma between Beta parameters = xi = 0.48, gamma = 1.03
    Obtained by linear regression of log(p) vs q across the identified SnS paths (Fig. 10b, Table II). This is an empirical constraint, not derived from physical principles.
  • Spatial autocorrelation decay coefficient d_corr distribution parameter lambda_corr = 40.61 (truncated exponential)
    Fitted to the measured ACF decay coefficients of the SnS paths (Fig. 11b, Eq. (16)). Controls the spatial correlation of generated AAFs.
  • SnS path classification threshold = 3 dB power variation across the array
    Chosen in Section III-C to distinguish SnS paths from non-SnS paths, with the rationale that it accounts for 'potential system jitter and NF-induced effects'. If this threshold were different, the set of 248 SnS paths and hence all fitted AAF statistics would change.
assumptions (6)
  • domain assumption The Rayleigh distance criterion (based on [8]) determines that users are in the near-field, so the spherical-wave model applies.
    Used in Section II to motivate the NF analysis. Standard in XL-MIMO literature, though Rayleigh distance is a far-field boundary heuristic, not an exact physics cutoff.
  • domain assumption The channel can be represented as a superposition of L discrete resolvable multipath components (path parameters extracted from CIRs).
    Foundation of the model in Eqs. (1)-(2) and the path tracking algorithm in Section III-C. This is standard for wideband channel modeling but ignores unresolvable diffuse components beyond the 40 dB dynamic range.
  • ad hoc to paper For each NLoS path, the wavefront can be modeled either as a point source (SPM, for small scatterers) or as specular reflection from the mirror-image of the Rx (SRM, for flat surfaces), and no intermediate or mixed mechanism is needed.
    The hybrid model of Section III-B classifies each path into one of two categories. The paper does not provide a quantitative criterion (e.g., surface roughness threshold or scatterer size) for choosing SRM vs SPM in the validation simulations; the classification appears to be made per-path based on the measurement geometry.
  • domain assumption The AAF statistics (Beta shapes, log-linear p-q relation, exponential spatial ACF) extracted from Case 3 are representative of SnS paths generally in this environment.
    Needed for the statistical generation in Section III-C and for the validation in Section IV. The paper's footnote in Table II states that parameters for other deployment scenarios must be obtained by additional measurements or ray tracing, which limits generalizability.
  • domain assumption A 40 dB power dynamic range is sufficient to capture the multipath structure relevant to the validation metrics.
    Applied in the PDP processing ('A 40 dB power dynamic range was applied, consistent with later modeling and validation settings', Section II-C). This choice determines which paths are detected, especially weak NLoS paths.
  • ad hoc to paper The maximum amplitude normalization of AAFs (Eq. (12)) and the conversion in Eq. (21) allow the reference element amplitude alpha_ref,l to be set equal to the maximum amplitude.
    The paper states this 'for analytical convenience' in Section III-C. In real measurements, the reference element may not be the maximum-amplitude element for every path, so the conversion in Eq. (21) introduces an approximation.
invented entities (2)
  • Amplitude attenuation factor (AAF) as a continuous [0,1] path-wise amplitude taper across array elements
    purpose: Models SnS power variation across the array in a statistical way, replacing binary VR visibility indicators.
    The AAF is a modeling construct introduced in this work and [18]. Its statistics are estimated from the same measurements used for validation, and it produces no externally falsifiable prediction (e.g., a measurable physical quantity outside the measured channel statistics). It is a compact representation of observed power variation.
  • Specular reflection model (SRM) with virtual mirror-image point source independent evidence
    purpose: Alternatively to SPM, describes the wavefront curvature of NLoS paths reflected from large flat surfaces.
    Although the specific modeling choice is new, it is grounded in standard geometrical optics and is directly checked against measured inter-element phase differences for multiple materials in Fig. 8b-f, giving it an independent falsifiable handle within this paper's measurements.

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Pith. "Pith review of Empirical Study on Near-Field and Spatial Non-Stationarity Modeling for THz XL-MIMO Channel in Indoor Scenario." pith.science (2026). https://pith.science/paper/C6ST7BCM

@misc{pith2026250509398,
  author       = {Pith},
  title        = {Pith review of: Empirical Study on Near-Field and Spatial Non-Stationarity Modeling for THz XL-MIMO Channel in Indoor Scenario},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/C6ST7BCM}},
  note         = {Machine review of arXiv:2505.09398}
}
read the original abstract

Terahertz (THz) extremely large-scale MIMO (XL-MIMO) is considered a key enabling technology for 6G and beyond due to its advantages such as wide bandwidth and high beam gain. As the frequency and array size increase, users are more likely to fall within the near-field (NF) region, where the far-field plane-wave assumption no longer holds. This also introduces spatial non-stationarity (SnS), as different antenna elements observe distinct multipath characteristics. Therefore, this paper proposes a THz XL-MIMO channel model that accounts for both NF propagation and SnS, validated using channel measurement data. In this work, we first conduct THz XL-MIMO channel measurements at 100 GHz and 132 GHz using 301- and 531-element ULAs in indoor environments, revealing pronounced NF effects characterized by nonlinear inter-element phase variations, as well as element-dependent delay and angle shifts. Moreover, the SnS phenomenon is observed, arising not only from blockage but also from inconsistent reflection or scattering. Based on these observations, a hybrid NF channel modeling approach combining the scatterer-excited point-source model and the specular reflection model is proposed to capture nonlinear phase variation. For SnS modeling, amplitude attenuation factors (AAFs) are introduced to characterize the continuous variation of path power across the array. By analyzing the statistical distribution and spatial autocorrelation properties of AAFs, a statistical rank-matching-based method is proposed for their generation. Finally, the model is validated using measured data. Evaluation across metrics such as entropy capacity, condition number, spatial correlation, channel gain, Rician K-factor, and RMS delay spread confirms that the proposed model closely aligns with measurements and effectively characterizes the essential features of THz XL-MIMO channels.

Figures

Figures reproduced from arXiv: 2505.09398 by the authors.

Figure 1
Figure 1. Comparison of XL-MIMO channel modeling approaches: our prior work [18] vs. this work. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The photograph of the measurement scenario. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Transmitters and receivers layout of the measurement scenario. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (14 more)
Figure 5
Figure 5. Figure 5: Variation of NF multipath parameters across antenna elements in Case 1 (concrete): (a) Relative power, (b) Inter-element phase difference, (c) Relative [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Measured and simulated SnS phenomenon. Measured SnS caused by [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: A schematic diagram of the THz XL-MIMO NF propagation and SnS channel model, including a hybrid NF model (SPM+SRM) and two SnS causes [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 9
Figure 9. Figure 9: Probability density function of AAFs and corresponding Beta [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 8
Figure 8. Figure 8: Inter-element phase differences for different materials in Case 1. (a) [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 10
Figure 10. Figure 10: (a) Probability density function of the Beta distribution parameter [PITH_FULL_IMAGE:figures/full_fig_p008_10.png]
Figure 12
Figure 12. Figure 12: Flowchart of the AAF generation procedure. [PITH_FULL_IMAGE:figures/full_fig_p009_12.png]
Figure 11
Figure 11. Figure 11: (a) Spatial autocorrelation of AAF and corresponding exponential model fitting. (b) Statistical distribution of correlation coefficients and their truncated exponential fitting. 2) Statistical Generation of AAF: For SS paths, the AAF is equal to 1 across all antenna e…
Figure 13
Figure 13. Figure 13: The implementation and validation framework of the proposed model. [PITH_FULL_IMAGE:figures/full_fig_p010_13.png]
Figure 14
Figure 14. Figure 14: b. After removing the LoS contributions, it can be observed that the traditional SPM model overestimates the entropy capacity due to exaggerated nonlinear phase variations in certain NLoS paths, while the SRM model underestimates it by underrepresenting such variation…
Figure 15
Figure 15. Figure 15: Demmel condition number comparison for N = 4, 8: measured, hybrid NF, SRM, SPM, and FF models in (a) LoS and (b) NLoS conditions. • Path gain The necessity of accounting for element radiation patterns and the spatial variation of multipath angles across the array in t…
Figure 16
Figure 16. Figure 16: Validate the proposed model from the NF propagation perspective by [PITH_FULL_IMAGE:figures/full_fig_p012_16.png]
Figure 17
Figure 17. Figure 17: Comparison of average spatial correlation between the simulated and [PITH_FULL_IMAGE:figures/full_fig_p012_17.png]
Figure 18
Figure 18. Figure 18: CDFs of channel statistical parameters across all XL-MIMO elements in Case 3, comparing measurement results with model simulations: (a) Channel [PITH_FULL_IMAGE:figures/full_fig_p013_18.png]

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

  1. Near-Field Propagation and Spatial Non-Stationarity Channel Model for 6-24 GHz (FR3) Extremely Large-Scale MIMO: Adopted by 3GPP for 6G

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.