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

T0 review · 3 major / 7 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read 3GPP adopts near-field, non-stationary channel model for 6G

desk verdict Useful 3GPP framework for FR3 XL-MIMO near-field/SNS, but the near-field NLOS phases are generated from two inconsistent source positions, which undermines the physical claim. read the letter →

arxiv 2506.17887 v1 pith:PGHV2UYY submitted 2025-06-22 eess.SP

Huixin Xu , Jianhua Zhang , Pan Tang , Hongbo Xing , Haiyang Miao , Nan Zhang , Jian Li , Jianming Wu
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Wenfei Yang Zhening Zhang Wei Jiang Zijian He Afshin Haghighat Qixing Wang Guangyi Liu
This is my paper · ORCID
classification eess.SP
keywords 3GPPextremelylarge-scaleMIMOFR3near-fieldpropagationspatialnon-stationarityvisibilityregionchannelmodeling6G
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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 argues that the standard 3GPP channel model used for 5G simulations can be extended, with modest additions, to capture two phenomena that appear when base stations carry hundreds or thousands of antennas at 6-24 GHz. The first is near-field propagation: at these apertures and frequencies a user can sit inside the array's near field, so wavefronts are spherical rather than planar and each antenna element sees a different phase and a different departure or arrival angle. The second is spatial non-stationarity (SNS): clusters can be visible to only part of the very large array, so path power varies element by element. The paper gives the concrete parameter extensions—element-wise distances to spherical-wave sources and element-wise power attenuation factors—that let the existing 3GPP structure reproduce these effects, and reports that 3GPP adopted the framework for the FR3 channel model. Simulated results show measurable near-field capacity gains (up to 11.6 bps/Hz indoors at 2 m radius) and stronger coupling-loss fading under SNS than under the stationary model.

What carries the argument

The machinery is a pair of element-wise parameter sets inserted into the standard coefficient-generation equations. Near-field replaces the per-cluster unit vectors and fixed phases with per-element geometry: distances $d_{1,n,m}$ from the BS to the spherical-wave source and $d_{2,n,m}$ from the UE to that source, computed from scaling factors $s_{\rm BS}$ and $s_{\rm UE}$, with $s_{\rm BS}=1$ for the strongest specular clusters and $s_{\rm BS}$ drawn from a Beta distribution otherwise. SNS multiplies each ray coefficient by $\sqrt{\alpha_{s,n,m}}$ at the BS and $\sqrt{\beta_u}$ at the UE, where the stochastic model sets $\alpha_{s,n}=1$ inside a visibility region and applies an exponential roll-off outside it.

What would settle it

In a UMi or InH double-directional measurement with a large virtual array, resolve the two strongest NLOS clusters at each user position and estimate each cluster's BS-to-spherical-source distance from the per-element phase slope; if a substantial share of those strongest clusters yield $s_{\rm BS}$ clearly below 1, the specular-cluster assumption and the resulting element-wise phases fail.

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Extended reading notes

Core claim

The central claim is that near-field propagation can be modeled inside the 3GPP TR 38.901 framework by replacing constant array-phase and angle parameters with element-wise quantities computed from the exact distance between each antenna element and an equivalent spherical-wave source per cluster. The two (or per-scenario $N_{\rm spec}$) strongest NLOS clusters are assumed to be specular reflections, so their source distance equals the total propagation path length; weaker clusters get a Beta-distributed fraction of that length. SNS is captured by element-wise power attenuation factors: a stochastic visibility-region model at the base station, a knife-edge diffraction blocker model for partial blockage, and fixed per-element attenuations at the user device. The paper reports that this framework has been adopted by 3GPP and that simulations show near-field capacity gains and SNS-induced fading.

Load-bearing premise

The model assumes that the $N_{\rm spec}$ strongest NLOS clusters are purely specular reflections, so their spherical-wave source sits at the full path length, and that every other path has exactly one non-specular interaction, so $s_{\rm UE}=1-s_{\rm BS}$; if real strong clusters involve diffraction or multiple bounces, the element-wise phases and angles will be miscomputed.

Editorial extensions

If this is right

  • System-level 6G simulators built on the 3GPP procedure can reproduce near-field capacity gains, such as 11.6 bps/Hz in an indoor hotspot at 2 m radius, without leaving the standard model's structure.
  • SNS-aware simulations will show stronger coupling-loss fading than spatial-stationary models (0.91 dB in UMi, 0.67 dB in InH), directly affecting link-budget and coverage predictions.
  • Element-wise angle and phase parameters enable realistic evaluation of XL-MIMO beamforming and precoding, including spherical-wavefront focusing and per-element codebook design.
  • The scenario tables (Beta-shape parameters, SNS probability, visibility-probability fits) provide immediate defaults for UMa, UMi, InH, InF, RMa, and SMa.
  • The extensions slot into the existing twelve-step generation flow, so deployed 3GPP-compliant codebases need only added steps, not a new architecture.

Reading between the lines

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

  • The same element-wise distance machinery could be carried into sub-THz bands and reconfigurable-intelligence-surface channels, where spherical-wave and partial-visibility effects are stronger.
  • If the specular-cluster assumption fails in dense indoor or factory settings, the Beta-fit parameters and the $s_{\rm UE}=1-s_{\rm BS}$ rule would need re-estimation from double-directional measurements.
  • A direct test: use the model's per-element phases to predict array beam patterns and compare with measured near-field patterns; this would isolate the phase model from the SNS attenuation model.
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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 / 7 minor

Summary. The paper proposes an XL-MIMO channel modeling framework for the 6–24 GHz FR3 band that extends the 3GPP TR 38.901 structure with two features: near-field propagation and spatial non-stationarity (SNS). Near-field propagation is modeled by introducing per-cluster distances d1,n,m and d2,n,m from the BS and UE to spherical-wave sources, which are then used to compute element-wise phases and angles. SNS is modeled at the BS side either stochastically through visibility regions and power attenuation factors or through a physical blocker-based knife-edge model, and at the UE side through fixed per-element attenuation values. The framework is presented as the model adopted by 3GPP, and its performance is illustrated by simulations of channel capacity and coupling loss in UMi and InH scenarios.

Significance. If the proposed framework is physically sound, it is a valuable and timely standardization contribution: it provides a concrete, parameterized way for system-level simulators to reproduce near-field spherical-wave effects and element-wise power variations within the established TR 38.901 coefficient generation procedure. The paper's strengths are its close integration with the 3GPP structure, the detailed parameter tables (Tables III and IV) that make the model directly implementable, and the clear separation of near-field distance generation from SNS attenuation. The main significance is therefore contingent on fixing a load-bearing physical consistency issue in the near-field NLOS generation, because element-wise phases that do not correspond to any physical path would undermine the claimed near-field capability and the interpretation of the capacity gains in Fig. 14.

major comments (3)
  1. [Section III-B, Eq. (6)] The near-field NLOS generation does not enforce that the BS-side and UE-side spherical-wave source positions coincide for non-specular clusters. In Section III-B, d1,n,m and d2,n,m are generated as sBS,n·c·tau and sUE,n·c·tau with sUE,n = 1 - sBS,n, while the directions r-hat_tx,n,m and r-hat_rx,n,m are taken from the existing far-field angular distributions. The implicit source positions are therefore p_BS = d1·r-hat_tx and p_UE = r_UE + d2·r-hat_rx, and nothing in Eqs. (8)-(11) or in the direction-vector construction at the end of Section III-B constrains p_BS = p_UE. For a single-scatterer NLOS path these two positions will generally differ by many wavelengths at 6-24 GHz, so the element-wise phases and angles computed in Eq. (6) are not those of any physical path. This affects every non-specular cluster, not only misclassified ones, and it means that the capacity comparison in Fig. 14 does not establish that the model reproduces physical near-field phase behavior. The model should either generate a single physical source point and derive d1, d2, and the element-wise angular parameters from it, or explicitly re-frame d1 and d2 as independent hypothetical source distances and justify the resulting unphysical phase distribution.
  2. [Section III-A, Eq. (9)] The assumption that the Nspec strongest clusters are exactly the specular reflection clusters is load-bearing for the near-field model but is not validated within the paper. Section III-A states that paths with sBS = 1 tend to have higher average power and therefore the strongest clusters are treated as specular, with sBS = 1 and d1 = d2 = c·tau. If a strong NLOS cluster is actually produced by diffraction or diffuse scattering, its spherical-wave source is not at the mirror-image point, and setting both distances to the full path length will misplace the source and corrupt the element-wise phases. The paper cites a 3GPP contribution [29] for the power ordering but provides no direct validation in the UMi/InH scenarios used for evaluation. At minimum, the authors should report the sensitivity of the near-field capacity results to the choice of Nspec and to the specular/non-specular classification criterion.
  3. [Section V-B] The performance evaluation is simulation-only and is largely self-referential: the Beta distribution parameters for sBS, the PrSNS normal distribution, the VP-power model, and the VR construction are all fitted from ray-tracing data in the same scenarios used for the capacity and coupling-loss simulations. The results in Figs. 14 and 15 therefore demonstrate internal consistency of the framework rather than independent predictive accuracy. There is no comparison with the measured phase and power variations shown in Figs. 2-4, and no confidence intervals are reported for the capacity gains. The claim that the results 'demonstrate the effectiveness' of the framework should be softened or supported by a direct measurement-based check of at least one near-field and one SNS statistic.
minor comments (7)
  1. [Eq. (6)] The rendering of Eq. (6) is ambiguous: the phase terms appear to be missing norm bars around the distance differences, e.g., d1,n,m - |d1,n,m·r-hat_tx,n,m - d-bar_tx,s|. The authors should write the equation with explicit absolute values.
  2. [Eq. (7)] The indexing in Eq. (7) is confusing for Nspec = 2: the sum over specular reflection clusters runs from n = 3 to Nspec, which is empty in UMa/UMi, while the first two strongest clusters are already handled separately. This should be clarified or re-indexed.
  3. [Eq. (16)] The simplified knife-edge expression '-20log10(0.5 - Fh1|h2|w1|w2)' is unclear, especially the meaning of the subscript 'h1|h2|w1|w2'. Please define the notation and state which edge factors are set to 0.5.
  4. [Section IV-A-1-b] The VR generation procedure should state whether the randomly chosen reference corner and the dimensions a,b allow the VR to extend beyond the array boundary, and if so, how the area consistency with VP is maintained.
  5. [Section II-B, Fig. 3] In Fig. 3(a), please specify whether the inter-element phase differences are unwrapped and what element spacing is used, since the reported 0.83 rad fluctuation is otherwise hard to interpret.
  6. [Section II-C] The statement that near-field amplitude variations are generally negligible is asserted without a supporting reference or quantitative bound; a brief justification or citation would help.
  7. [Abstract and Introduction] The phrase 'adopted by 3GPP' should specify the stage of adoption, e.g., agreed in RAN1 and being incorporated into TR 38.901, so that readers do not assume a final published specification.

Circularity Check

0 steps flagged · score 0.0 of 10

No material circularity: capacity/coupling-loss results are simulation outputs of the fitted model rather than fitted targets, and the 3GPP-adoption claim rests on external standardization proceedings.

full rationale

The paper's derivation chain is self-contained for circularity analysis. Near-field parameters (d1, d2) are generated from Beta-distributed scaling factors fitted to ray-tracing distributions of sBS and ssum (Eqs. (8)-(11), Table III), and SNS attenuation factors are generated from visibility-probability fits (Eqs. (13)-(15), Table IV). The performance metrics (capacity, coupling loss) are simulation outputs of the resulting channel coefficients (Figs. 14-15); no capacity or coupling-loss value was used as a fitting target, so these demonstrations are not fitted inputs renamed as predictions. The 'adopted by 3GPP' status is an external standardization fact supported by RAN1 study-item documents (e.g., [20]) rather than a mathematical conclusion derived from the model. Self-citations to the authors' 3GPP contributions exist, but none carries a load-bearing uniqueness or derivation step; the cited measurements and fits are independently parameterized or external. The physical-consistency concern about independently drawn BS/UE source positions for non-specular clusters is a model-realism issue, not a circularity between inputs and claims. Therefore no circular step is identified.

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

The central modeling framework rests on fitted parameters from ray-tracing simulations and 3GPP contributions, not on published measurement datasets. The near-field phase model assumes amplitude variation is negligible based on prior literature. The SNS model depends on the visibility-probability and power relation fitted by a 3GPP contribution and on fixed UE attenuation values from another 3GPP contribution.

free parameters (6)
  • Nspec (number of specular clusters) = UMa/UMi: 2, InH/InF: 4
    Set from ray-tracing fraction of paths with sBS = 1; used to decide which clusters get specular source distances (Section III-A, Table III).
  • Beta distribution shape parameters for sBS = UMi alpha=1.53 beta=1.42; InH alpha=1.25 beta=1.27; etc.
    Fitted to histograms of the ray-tracing scaling factor for non-specular clusters; used to draw d1 for weak clusters (Eq. (9), Table III).
  • PrSNS normal distribution parameters = mu/sigma per scenario, e.g., UMa 0.56/0.20
    Fitted normal distribution over UEs from ray tracing; used to classify clusters as SS or SNS (Section IV-A1a, Table IV).
  • VP model parameters A, B, R, sigma-squared = e.g., UMa A=0.15, B=0.45, R=33, sigma2=0.0015
    Exponential fit of cluster visibility probability versus power from ray tracing; used to assign visibility region sizes (Eq. (14), Table IV).
  • Roll-off factor C = 13
    Hand-set to control attenuation sharpness outside the visibility region; used in Eq. (15) and defined for all scenarios (Section IV-A1c).
  • UE-side per-element attenuation values = Depends on usage scenario and frequency band; see 3GPP R1-2504688
    Derived from measurements and simulations of hand and head blockage; assigned as fixed attenuation per element (Section IV-B).
assumptions (5)
  • domain assumption The Nspec strongest clusters are specular reflection clusters, so sBS = 1 for them.
    Section III-A: 'a practical modeling approach is to designate the Nspec strongest-power clusters as specular reflection clusters.' If this ranking is wrong, d1 for strong clusters is miscomputed.
  • domain assumption Every non-specular path has exactly one non-specular interaction, so sBS + sUE = 1.
    Section III-B, Eq. (11). Rare cases with ssum < 1 are ignored as low power. Multiple-bounce paths are excluded by construction.
  • domain assumption Amplitude, XPR, delay, and Doppler variations across elements are negligible in the near field.
    Section II-C1, citing [26]-[28]. Only phase and angle are updated element-wise; if amplitude variations matter at short range, the model omits them.
  • domain assumption Ray-tracing simulations under UMa, UMi, InH, InF, RMa, and SMa are representative of real FR3 propagation.
    All fitted distributions in Tables III and IV come from ray-tracing contributions; no public measurement-based validation is shown for these parameter values.
  • domain assumption Knife-edge diffraction (Blockage Model B) accurately models partial blockage for the four new blocker types.
    Section IV-A2, Eq. (16). The model assumes single or simplified edge diffraction for building edges.
invented entities (2)
  • Spherical-wave source per cluster (hypothetical source in Fig. 6)
    purpose: Virtual point from which element-wise phase and angle for NLOS paths are computed.
    It is a modeling abstraction, not a measured physical scatterer. Its distance is set by sBS or total path length, and no independent observable is given other than the phase and angle behavior it is designed to reproduce.
  • Visibility region (VR) per SNS cluster
    purpose: Rectangular area on the array inside which a cluster is unattenuated and outside which power rolls off.
    The VR is a construct to implement the visibility probability; its size and location are random and calibrated to ray tracing, with no direct measurement.

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Cite this review

Pith. "Pith review of Near-Field Propagation and Spatial Non-Stationarity Channel Model for 6-24 GHz (FR3) Extremely Large-Scale MIMO: Adopted by 3GPP for 6G." pith.science (2026). https://pith.science/paper/PGHV2UYY

@misc{pith2026250617887,
  author       = {Pith},
  title        = {Pith review of: Near-Field Propagation and Spatial Non-Stationarity Channel Model for 6-24 GHz (FR3) Extremely Large-Scale MIMO: Adopted by 3GPP for 6G},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PGHV2UYY}},
  note         = {Machine review of arXiv:2506.17887}
}
read the original abstract

Next generation cellular deployments are expected to exploit the 6-24 GHz frequency range 3 (FR3) and extremely large-scale multiple-input multiple-output (XL-MIMO) to enable ultra-high data rates and reliability. However, the significantly enlarged antenna apertures and higher carrier frequencies render the far-field and spatial stationarity assumptions in the existing 3rd generation partnership project (3GPP) channel models invalid, giving rise to new features such as near-field propagation and spatial non-stationarity (SNS). Despite extensive prior research, incorporating these new features within the standardized channel modeling framework remains an open issue. To address this, this paper presents a channel modeling framework for XL-MIMO systems that incorporates both near-field and SNS features, adopted by 3GPP. For the near-field propagation feature, the framework models the distances from the base station (BS) and user equipment to the spherical-wave sources associated with clusters. These distances are used to characterize element-wise variations of path parameters, such as nonlinear changes in phase and angle. To capture the effect of SNS at the BS side, a stochastic-based approach is proposed to model SNS caused by incomplete scattering, by establishing power attenuation factors from visibility probability and visibility region to characterize antenna element-wise path power variation. In addition, a physical blocker-based approach is introduced to model SNS effects caused by partial blockage. Finally, a simulation framework for near-field and SNS is developed within the structure of the existing 3GPP channel model. Performance evaluations demonstrate that the near-field model captures higher channel capacity potential compared to the far-field model. Coupling loss results indicate that SNS leads to more pronounced propagation fading relative to the spatial stationary model.

Figures

Figures reproduced from arXiv: 2506.17887 by the authors.

Figure 2
Figure 2. PDPs across TX antenna elements in (a) UMa and (b) indoor scenarios. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Variations of near-field parameters across TX antenna elements: (a) [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Power variation of SNS paths across TX antenna elements in (a) UMa [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (10 more)
Figure 5
Figure 5. Figure 5: Illustration of the XL-MIMO channel modeling framework incorporating the near-field propagation and SNS features. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Near-field propagation feature: (a) Specular reflection and (b) Non-specular reflection. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: CDFs of the scaling factors at the BS side in (a) UMi and (b) InH [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 9
Figure 9. Figure 9: CDFs of the sum of scaling factors at the BS and UE sides in (a) [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: (a) Cluster VP; (b) SNS probability of UEs. [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]
Figure 11
Figure 11. Figure 11: Relationship between cluster VP and power in UMa scenario. [PITH_FULL_IMAGE:figures/full_fig_p009_11.png]
Figure 12
Figure 12. Figure 12: Illustration of the power attenuation factor across antenna elements. [PITH_FULL_IMAGE:figures/full_fig_p010_12.png]
Figure 13
Figure 13. Figure 13: Channel coefficient generation procedure: evolution from MIMO to XL-MIMO. [PITH_FULL_IMAGE:figures/full_fig_p011_13.png]
Figure 14
Figure 14. Figure 14: Channel capacity results for (a) UMi and (b) InH scenarios. [PITH_FULL_IMAGE:figures/full_fig_p012_14.png]
Figure 15
Figure 15. Figure 15: Coupling loss results for UMi and InH scenarios. [PITH_FULL_IMAGE:figures/full_fig_p013_15.png]

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Pith tools

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