Pith. sign in

REVIEW 5 major objections 5 minor 42 references

Distributed U6G ELAA Communication Systems: Channel Measurement and Small-Scale Fading Characterization

T0 review · 5 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Measured U6G extra-large-array channels can be split into subarrays and treated as far-field channels, supporting distributed 6G processing.

desk verdict Useful U6G ELAA measurement paper with credible far-field subarray claim, but the -20 dB NMSE evidence comes from a flexible NOMP fit rather than the DFT processing it motivates, and the Gini formula has a fixable typo. read the letter →

arxiv 2504.20514 v1 pith:APSRJRJE submitted 2025-04-29 eess.SP

Jiachen Tian , Zhengtao Jin , Xiayang Chen , Yu Han , Shi Jin , Wenjin Wang , Chao-Kai Wen This is my paper · ORCID
classification eess.SP
keywords channelmeasurementU6Gextra-largescaleantennaarrayELAAdistributedprocessingsmall-scalefadingspatialnon-stationaritysubarray-wisefar-fieldapproximation
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 reports channel measurements at 7.8 GHz with 100 MHz bandwidth in indoor, outdoor, and outdoor-to-indoor scenarios, and asks whether an extremely large antenna array of 64 virtual elements can be handled by distributed subarray processing. It finds that although the full array sees a non-stationary, near-field channel, each subarray of 4 or 8 elements is well approximated as a far-field channel, with reconstruction NMSE of about -20 dB (measured -18.82 dB for 16 subarrays and -18.09 dB for 8). It also finds consistent angular and delay structure across subarrays and across sub-bands. A sympathetic reader would care because these are measurement-based justifications for using low-complexity, distributed DFT-based processing for U6G ELAA systems rather than full-array near-field processing.

What carries the argument

The argument is carried by three linked instruments. First, a virtual-array channel sounder synthesizes a 64-element ELAA by horizontally moving an 8-element physical array while all objects are kept stationary, giving measured snapshots of the full ELAA. Second, the Newtonized orthogonal matching pursuit (NOMP) estimator decomposes the received OFDM signal into 100 multipath components with delay and azimuth parameters and reconstructs the channel matrix $\mathbf{H}$. Third, a two-dimensional DFT transform $\mathbf{H}_{\mathrm{D-S}} = \mathbf{U}_F \mathbf{H} \mathbf{U}_A^H$, together with a normalized mean-square error (NMSE) comparing the virtual full-array channel with a far-field reconstruction built subarray-by-subarray, provides the concrete evidence: the full array spreads over several spatial DFT directions while each subarray concentrates on nearly one direction.

What would settle it

Measure the same geometry with a physical 64-element ELAA, or repeat the virtual-array scan in random order while monitoring phase drift, and check whether the subarray-wise far-field NMSE stays near -18 to -20 dB and whether the LoS azimuth still follows the spherical-wave model; if the error rises substantially or the pattern is not reproducible, the virtual-array static-environment assumption is what produced the result.

Watch

Extended reading notes

Core claim

The central discovery is that the near-field non-stationarity of an ELAA at upper 6 GHz does not force full-array near-field processing: dividing the 64-element array into subarrays makes each subarray's channel nearly a single far-field direction in the DFT domain, and the subarray-wise far-field reconstruction achieves an NMSE around -20 dB, improving as the number of subarrays grows. The measurement evidence is that LoS azimuth angles across eight subarrays track the spherical-wave model, while RMS angular spread and Rician K factor vary across subarrays, showing non-stationarity, yet dominant angular directions, delay spread, and LoS arrival time remain consistent, showing common structure. In the frequency domain, four sub-bands yield nearly identical average power angular spectra and RMS angular spreads from 18.13 to 19.25 degrees, supporting sub-band distributed processing. The paper states its own conclusion as: the near-field non-stationary ELAA channel can be effectively approximated as far-field subarray-wise under the U6G frequency band.

Load-bearing premise

The claim rests on treating a virtual 64-element array as a real one: the 8-element physical array is moved through eight positions under the assumption that the channel is perfectly static, so any phase drift, clock error, or environmental change between snapshots would be misread as spatial variation and could bias every subarray comparison.

Editorial extensions

If this is right

  • A U6G ELAA base station can process the channel subarray by subarray under a far-field assumption, so per-subarray algorithms from conventional massive MIMO carry over.
  • Increasing the number of subarrays from 8 to 16 improves the subarray-wise far-field reconstruction NMSE from -18.09 dB to -18.82 dB, giving a tunable complexity-accuracy tradeoff.
  • Because subarrays share dominant angular directions, delay spread, and LoS arrival time, CSI learned on one subarray can be reused for others, lowering channel-estimation overhead.
  • Because spatial characteristics are similar across sub-bands, with RMS angular spread between 18.13 and 19.25 degrees, sub-band or out-of-band CSI can support distributed estimation without full-band sounding.
  • Under line-of-sight, the U6G channel is sparse, with Gini index between 0.91 and 0.96, so compressive estimation and low-rank processing remain effective for the full array.

Reading between the lines

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

  • Editorial inference: Because each subarray behaves as a far-field channel, the same DFT-based hybrid beamforming and compressed channel-estimation routines built for conventional massive MIMO can likely be reused per subarray, making the hardware and baseband savings concrete; the paper argues this direction but does not implement a full system.
  • Editorial inference: The measured consistency of RMS angular spread across sub-bands suggests a testable frequency-extrapolation scheme in which spatial covariance estimated on one sub-band initializes beamforming on another; the paper reports the supporting statistics but does not run that algorithm.
  • Editorial inference: A true 64-element array measurement, rather than a virtual array assembled from eight static snapshots, would be the natural stress test of the -18 to -20 dB approximation claim, since any temporal drift in the virtual scan could masquerade as spatial non-stationarity.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper reports a channel measurement campaign at a center frequency of 7.8 GHz using a 100 MHz OFDM sounder and a virtual 64-element array built from an 8-element physical array. The authors characterize small-scale fading statistics (APAS, RMS AS, PDP, RMS DS, Rician K, sparsity) in indoor, outdoor, and outdoor-to-indoor scenarios, then analyze ELAA channel properties from a distributed-processing perspective: subarray-wise non-stationarities and consistencies, sub-band characteristics, and the accuracy of a subarray-wise far-field approximation quantified by an NMSE of about -18 dB. The paper concludes that these measurements validate low-complexity DFT-based distributed processing for U6G ELAA systems.

Significance. The measurement dataset and scenario comparisons are potentially useful for channel modeling in the upper mid-band, and the subarray-wise far-field approximation is directly relevant to reducing ELAA processing complexity. The paper's strengths include the construction of a custom sounder, the use of NOMP for MPC extraction, and the breadth of analyzed metrics. However, several technical issues—an incorrect Gini index formula, a mismatch between the claimed U6G band and the actual 7.8 GHz measurement frequency, and a validation gap between the NOMP-based NMSE and the DFT-based processing recommendation—currently undermine the support for the central claims.

major comments (5)
  1. [I and II.A] The Introduction (Section I) defines U6G as the 6245–7125 MHz band, but the system described in Section II.A up-converts the signal to a 7.8 GHz center frequency. All subsequent claims about 'U6G channel characteristics' therefore concern a frequency band outside the defined U6G range. Please either reclassify the reported band as FR3/upper mid-band and revise the title, abstract, and contributions accordingly, or provide a clear justification for treating 7.8 GHz as part of the U6G band.
  2. [III.F, Eq. (12)] The Gini index formula in Eq. (12) does not satisfy the stated property that G=0 for equal powers. For L MPCs with equal power, the expression gives G = 1 - 1/L (approximately 0.99 for L=100), not 0. This contradicts the text that states G=0 represents equal power. Please correct the formula or the normalization, and recompute the reported Gini-index values and CDFs in Fig. 9.
  3. [IV.C and IV.E.5] The NMSE of the subarray-wise far-field approximation is computed with H_recon obtained from the NOMP estimator using L=100 far-field MPCs per subarray (Section II.C), not from the low-complexity DFT beamspace representation recommended in Section IV.F.1. The reported -18.82 dB (B=16) and -18.09 dB (B=8) therefore quantify the fit of a highly flexible, oracle-like far-field model, not the error of the practical DFT-based subarray processing the paper promotes. Please provide an NMSE for DFT-based reconstructions with a limited number of beams per subarray, or clearly separate the goodness-of-fit claim from the DFT-processing claim.
  4. [II.B and IV] The virtual 64-element array is synthesized from eight snapshot positions of an 8-element physical array. The paper states that the environment was static, but it does not provide any phase-coherence validation between the snapshots, such as repeated reference measurements, drift statistics, or calibration residuals. Phase drift, clock instability, or any environmental change during the mechanical movement would directly contaminate the spatial channel and all subsequent subarray comparisons, including the NMSE values. Please provide evidence of temporal stability or describe a phase-compensation procedure.
  5. [II.C, Eq. (2)] The delay term p_nf(τℓ) = exp(−j2πη_f Δf τℓ / λ) in Eq. (2) is dimensionally inconsistent: the exponent should be a phase (e.g., −j2πη_f Δf τℓ after carrier removal), and the division by λ makes the argument of the exponential carry units of 1/m. This appears to be a typo, but it should be corrected and the reconstruction formula in Eq. (3) checked for consistency with the corrected phase model.
minor comments (5)
  1. [III.F] After correcting Eq. (12), the Gini-index values and the associated sparsity discussion in Section III.F and Fig. 9 should be revisited, as the reported range of 0.91 to 0.96 will shift under a correct formula.
  2. [III.B, III.D, III.E] The Gaussian fits for RMS AS, RMS DS, and Rician K are presented without goodness-of-fit statistics (e.g., RMSE or Kolmogorov–Smirnov tests); adding such statistics would strengthen the modeling claims.
  3. [IV.D] The claim that a wider band yields a larger RMS AS is based on four very similar values (18.85°, 18.13°, 19.25°, 18.73°); a statistical comparison or error bars are needed to support this trend.
  4. [IV.E.5] The statement 'The NMSE of this approximation is about -20 dB' is inconsistent with the reported values -18.82 dB and -18.09 dB; please use the exact figures and, if possible, report the number of observations Q and per-observation variability.
  5. [IV.C, Fig. 13] The DFT energy-concentration analysis is qualitative; consider reporting the fraction of channel energy captured by the first one or two DFT beams per subarray to quantify the support for DFT-based processing.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the subarray-wise far-field NMSE is an in-sample model-fit residual, and the DFT-processing overreach is an evidence gap, not a circular derivation.

full rationale

The paper's main claims are empirical characterization results, not first-principles predictions. APAS, RMS AS, PDP, RMS DS, Rician K, and Gini indices are computed directly from measured CIRs via NOMP and are fitted to standard distributions; these are descriptive model fits, not derivations from the fitted constants. The subarray-wise azimuth comparison in Sec. IV.A is validated against an independent spherical-wave geometric model, so that validation is non-circular. The one suspicious step is Sec. IV.C: the -18.82/-18.09 dB NMSE of Eq. (14) is computed between the measured virtual-array channel H_virt and H_recon, where H_recon,b is produced by the NOMP estimator run under the far-field assumption. This is an in-sample fit residual, not a prediction from a separate low-complexity DFT scheme, so it is weaker evidence for the DFT-based processing recommended in Sec. IV.F.1; with L=100 MPCs per subarray the model is flexible and the residual is partly self-consistency. However, this is a statistical/evidence-level limitation, not circularity: the NMSE could in principle have been large and refuted the approximation, and no equation in the paper reduces to its own input by construction. Self-citations [26], [27], [38], and [41] are standard technique references and do not carry a load-bearing uniqueness or ansatz claim. The virtual-array static-channel assumption is a measurement validity concern, not circularity. Overall, the derivation chain is self-contained against the measurements, with only a minor self-referential flavor in the far-field NMSE validation; hence score 2.

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

The central claims rest mainly on the measurement pipeline, NOMP on an 8-element ULA, virtual array synthesis under a static environment, and fitted distribution models for the reported statistics. No new physical entities, mediators, forces, or conserved quantities are introduced. The largest free-parameter load is the set of Gaussian, Laplace, and S-V model parameters fitted to the measured data; these are presented as models rather than predictions. The virtual-array stationarity assumption is the most fragile axiom because every ELAA result depends on it.

free parameters (6)
  • PAS Gaussian and Laplace fit parameters per scenario = Table I, e.g., indoor Tx1 Gaussian a=0.59, b=89.00, c=0.39
    Fitted to the measured APAS for each Tx position; used to claim Gaussian and Laplace models describe U6G angular power spectra.
  • RMS AS Gaussian mean and variance per scenario and subarray = Indoor means 18.31, 32.04, 41.49, 28.70 degrees; outdoor 15.80, 18.77, 29.67 degrees; O2I 17.08 degrees; Table III…
    Fitted to CDFs of NOMP-derived angular spread; the paper claims Gaussian validity based on visual fit only.
  • RMS DS Gaussian mean and variance per scenario = Indoor mean 14.36 ns, outdoor 37.64 ns, O2I 42.52 ns, with variances 4.96^2, 2.80^2, 3.12^2
    Fitted to measured delay spread CDFs; central to the claim that Gaussian models work for U6G RMS delay spread.
  • Rician K factor Gaussian mean and variance per scenario and subarray = Indoor mean 2.30, outdoor 4.14, O2I 1.34, with standard deviations 0.28, 0.52, 0.19; Table III subarray means 1.88 to…
    Fitted to CDFs of the LoS-to-NLoS power ratio; the K factor is a fitted model, not a prediction.
  • S-V cluster and ray decay factors per scenario = Cluster decay 18.04 to 37.05 ns; ray decay 4.15 to 69.96 ns, Table II
    Fitted from the measured PDP clusters to support the claim that U6G channels match the Saleh-Valenzuela model.
  • NOMP MPC count and angular resolution = L=100, 1-degree angular grid
    Chosen a priori in Section II.C; the number of extracted MPCs and the angular resolution affect all derived statistics and fitted parameters.
assumptions (6)
  • domain assumption The received signal is a superposition of a finite number of ray-like multipath components, Eq. (1).
    Standard ray-based channel model used for NOMP extraction; accepted in the cited literature.
  • domain assumption NOMP on an 8-element ULA with far-field steering vectors gives accurate MPC parameters in delay and angle.
    Invoked in Section II.C; the estimator assumes the small-scale array sees plane waves and that L=100 MPCs capture the channel.
  • domain assumption The propagation environment is static during the virtual array synthesis.
    Section II.B states all objects are stationary and Section IV uses this to merge eight physical array positions into a 64-element ELAA; if false, all ELAA results are biased.
  • standard math DFT matrices form valid bases for far-field delay and angular channel representations in Eq. (13).
    Standard property of uniformly spaced arrays and OFDM delay taps, cited to [37], [38].
  • standard math Spherical wave geometry correctly models the LoS azimuth angle variation across subarrays in Fig. 10.
    Uses the geometric spherical-wave model from [36] to compare measured LoS angles with theory.
  • domain assumption Gaussian distributions are appropriate models for RMS AS, RMS DS, and Rician K, and the Gini index measures channel sparsity.
    Sections III.B through III.F fit these models and visually validate them; the Gini formula in Eq. (12) is internally inconsistent as printed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Distributed U6G ELAA Communication Systems: Channel Measurement and Small-Scale Fading Characterization." pith.science (2026). https://pith.science/paper/APSRJRJE

@misc{pith2026250420514,
  author       = {Pith},
  title        = {Pith review of: Distributed U6G ELAA Communication Systems: Channel Measurement and Small-Scale Fading Characterization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/APSRJRJE}},
  note         = {Machine review of arXiv:2504.20514}
}
read the original abstract

The distributed upper 6 GHz (U6G) extra-large scale antenna array (ELAA) is a key enabler for future wireless communication systems, offering higher throughput and wider coverage, similar to existing ELAA systems, while effectively mitigating unaffordable complexity and hardware overhead. Uncertain channel characteristics, however, present significant bottleneck problems that hinder the hardware structure and algorithm design of the distributed U6G ELAA system. In response, we construct a U6G channel sounder and carry out extensive measurement campaigns across various typical scenarios. Initially, U6G channel characteristics, particularly small-scale fading characteristics, are unveiled and compared across different scenarios. Subsequently, the U6G ELAA channel characteristics are analyzed using a virtual array comprising 64 elements. Furthermore, inspired by the potential for distributed processing, we investigate U6G ELAA channel characteristics from the perspectives of subarrays and sub-bands, including subarray-wise nonstationarities, consistencies, far-field approximations, and sub-band characteristics. Through a combination of analysis and measurement validation, several insights and benefits, particularly suitable for distributed processing in U6G ELAA systems, are revealed, which provides practical validation for the deployment of U6G ELAA systems.

Figures

Figures reproduced from arXiv: 2504.20514 by the authors.

Figure 1
Figure 1. System setup of the U6G ELAA channel sounder. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Images of typical channel measurement campaigns: (a) Layout of indoor scenarios. (b) Schematic diagram of indoor scenarios. (c) Layout of outdoor [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. APAS: (a) Indoor. (b) Outdoor. (c) Comparison of indoor (Tx1), outdoor (Tx1) and O2I. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: CDF of RMS-AS in indoor scenarios. 10 15 20 25 30 35 40 45 50 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 CDF Outdoor, Tx1, measurement Outdoor, Tx1, fitted Outdoor, Tx2, measurement Outdoor, Tx2, fitted Outdoor, Tx3, measurement Outdoor, Tx3, fitted O2I, measurement O2I, …
Figure 5
Figure 5. Figure 5: CDF of RMS-AS in outdoor and O2I scenarios. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: PDP in Various Scenarios: (a) Indoor at Tx1, (b) Indoor at Tx2, (c) Outdoor at Tx1, (d) Outdoor at Tx2, (e) Outdoor at Tx3, (f) O2I. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: CDF of RMS DS in indoor, outdoor, and O2I scenarios. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: CDF of the Rician K factors in indoor, outdoor, and O2I scenarios. [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 10
Figure 10. Figure 10: Azimuth angle variations versus subarrays. [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]
Figure 12
Figure 12. Figure 12: Angular characteristics of different subarrays. (a) APAS of different [PITH_FULL_IMAGE:figures/full_fig_p010_12.png]
Figure 13
Figure 13. Figure 13: Comparison of channel characteristics in delay and spatial domains after DFT: (a) The whole ELAA. (b) Subarray 1 and Subarray 6. [PITH_FULL_IMAGE:figures/full_fig_p011_13.png]
Figure 14
Figure 14. Figure 14: APAS of different sub-bands. under different sub-bands remain nearly identical, while other dominant directions exhibit slight deviations. Next, we analyze the RMS AS derived from these sub￾bands. The RMS AS values for Band1, Band2, Band3, and Band4 are 18.85◦ , 18.13…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

42 extracted references · 33 canonical work pages

  1. [1]

    The road to 6G: Ten physical layer challenges for communications engineers,

    M. Matthaiou, O. Yurduseven, H. Q. Ngo, D. Morales-Jimenez, S. L. Cotton, and V . F. Fusco, “The road to 6G: Ten physical layer challenges for communications engineers,” IEEE Commun. Mag., vol. 59, no. 1, pp. 64-69, Jan. 2021. I

    work page 2021

  2. [2]

    World Radiocommunication Conference 2023 (WRC-23),

    ITU, “World Radiocommunication Conference 2023 (WRC-23),” Avail- able: https://www.itu.int/dms pub/itu-r/opb/act/R-ACT-WRC.15-2023- PDF-E.pdf, Dec. 2023 I

    work page 2023

  3. [3]

    7-24 GHz frequency range,

    3GPP, “7-24 GHz frequency range,” Tech. Rep. (TR) 38.820, 2021, V16.1.0. [Online]. Available: https://portal.3gpp.org/desktopmodules/ Specifications/SpecificationDetails.aspx?specificationId=3599. I

    work page 2021

  4. [4]

    6G wireless communications in 7–24 GHz band: Opportunities, techniques, and challenges,

    Z. Cui, P. Zhang, and S. Pollin, “6G wireless communications in 7–24 GHz band: Opportunities, techniques, and challenges,” arXiv: 2310.06425, 2023. I, I-A

    arXiv 2023

  5. [5]

    A tutorial on extremely large-scale MIMO for 6G: Fundamentals, signal processing, and applications,

    Z. Wang et al ., “A tutorial on extremely large-scale MIMO for 6G: Fundamentals, signal processing, and applications,” IEEE Commun. Surv. & Tut., early access, doi: 10.1109/COMST.2023.3349276. I

    arXiv 2023

  6. [6]

    Near-Field Channel Characterization for Mid-band ELAA Systems: Sounding, Parameter Estimation, and Modeling

    W. Fan, Z. Yuan, Y . Lyu, J. Zhang, G. Pedersen, J. Borrill, and F. Zhang, “Near-field channel characterization for mid-band ELAA systems: Sounding, parameter Estimation, and modeling,”arXiv:2405.06159, 2024. I

    work page Pith review arXiv 2024

  7. [7]

    A Tutorial On Near-Field XL-MIMO Communica- tions Towards 6G,

    H. Lu et al ., “A Tutorial On Near-Field XL-MIMO Communica- tions Towards 6G,” IEEE Commun. Surv. & Tut. , early access, doi: 10.1109/COMST.2024.3387749. I

    arXiv 2024

  8. [8]

    Sub-6 GHz to mmWave for 5G-advanced and beyond: Channel measurements, characteristics and impact on system perfor- mance,

    H. Miao et al ., “Sub-6 GHz to mmWave for 5G-advanced and beyond: Channel measurements, characteristics and impact on system perfor- mance,” IEEE J. Sel. Areas Commun. , vol. 41, no. 6, pp. 1945-1960, Jun. 2023. I-A 13

    work page 1945

Show all 42 references
  1. [9]

    Propagation measurements and channel models in indoor environment at 6.75 GHz FR1(C) and 16.95 GHz FR3 upper-mid band spectrum for 5G and 6G

    D. Shakya et al ., “Propagation measurements and channel models in indoor environment at 6.75 GHz FR1(C) and 16.95 GHz FR3 upper-mid band spectrum for 5G and 6G”, arXiv:2405.01358, 2024. I-A

    2024 arXiv

  2. [10]

    On modeling of dense multipath component for indoor massive MIMO channels,

    J. Li, B. Ai, R. He, M. Yang, and Z. Zhong, “On modeling of dense multipath component for indoor massive MIMO channels,” IEEE Ant. Wireless Propag. Lett., vol. 18, no. 3, pp. 526-530, Mar. 2019. I-A

    2019

  3. [11]

    Cellular wireless networks in the upper mid-band,

    S. Kang, et al, “Cellular wireless networks in the upper mid-band,” arXiv:2309.03038, 2023. I-A

    2023 arXiv

  4. [12]

    Sharing spectrum and services in the 7-24 GHz upper midband,

    P. Testolina, M. Polese, and T. Melodia, “Sharing spectrum and services in the 7-24 GHz upper midband,” arXiv:2402.08649, 2024. I-A

    2024 arXiv

  5. [13]

    Non-stationarities in extra-large-scale massive MIMO,

    E. D. Carvalho, A. Ali, A. Amiri, M. Angjelichinoski, and R. W. Heath, “Non-stationarities in extra-large-scale massive MIMO,” IEEE Wireless Commun., vol. 27, no. 4, pp. 74-80, Aug. 2020. I-A

    2020

  6. [14]

    Toward extra large-scale MIMO: New channel properties and low-cost designs,

    Y . Han, S. Jin, M. Matthaiou, T. Q. S. Quek, and C. -K. Wen, “Toward extra large-scale MIMO: New channel properties and low-cost designs,” IEEE Internet Things J. , vol. 10, no. 16, pp. 14569-14594, Aug. 2023. I-A

    2023

  7. [15]

    Low-overhead localization and VR identification for subarray-based ELAA systems,

    J. Tian, Y . Han, S. Jin, and M. Matthaiou, “Low-overhead localization and VR identification for subarray-based ELAA systems,” IEEE Wireless Commun. Lett., vol. 12, no. 5, pp. 784-788, May 2023. I-A

    2023

  8. [16]

    Channel measurement, modeling, and simulation for 6G: A survey and tutorial,

    J. Zhang et al ., “Channel measurement, modeling, and simulation for 6G: A survey and tutorial,” arXiv:2305.16616, 2023. I-A

    2023 arXiv

  9. [17]

    Multi-frequency mmWave massive MIMO channel measurements and characterization for 5G wireless communication systems,

    J. Huang, C. -X. Wang, R. Feng, J. Sun, W. Zhang, and Y . Yang, “Multi-frequency mmWave massive MIMO channel measurements and characterization for 5G wireless communication systems,” IEEE J. Sel. Areas Commun., vol. 35, no. 7, pp. 1591-1605, Jul. 2017. I-A, II-C, IV-A

    2017

  10. [18]

    Empirical studies of propagation characteristics and modeling based on XL-MIMO channel measurement: From far-field to near-field,

    H. Miao et al ., “Empirical studies of propagation characteristics and modeling based on XL-MIMO channel measurement: From far-field to near-field,” arXiv: 2404.17270v1, Apr. 2024. I-A

    2024 arXiv

  11. [19]

    Phase shifters versus switches: An energy efficiency perspective on hybrid beamforming,

    S. Payami, N. M. Balasubramanya, C. Masouros, and M. Sellathurai, “Phase shifters versus switches: An energy efficiency perspective on hybrid beamforming,” IEEE Wireless Commun. Lett. , vol. 8, no. 1, pp. 13-16, Feb. 2019. I-A

    2019

  12. [20]

    On the uplink transmission of extra-large scale massive MIMO systems,

    X. Yang, F. Cao, M. Matthaiou, and S. Jin, “On the uplink transmission of extra-large scale massive MIMO systems,” IEEE Trans. Veh. Technol., vol. 69, no. 12, pp. 15229-15243, Dec. 2020. I-A

    2020

  13. [21]

    Near-field wideband beamforming for extremely large antenna arrays,

    M. Cui and L. Dai, “Near-field wideband beamforming for extremely large antenna arrays,” IEEE Trans. Wireless Commun., early access, doi: 10.1109/TWC.2024.3398770. I-A

    2024

  14. [22]

    Hybrid spherical- and planar-wave mod- eling and DCNN-powered estimation of terahertz ultra-massive MIMO channels,

    Y . Chen, L. Yan, and C. Han, “Hybrid spherical- and planar-wave mod- eling and DCNN-powered estimation of terahertz ultra-massive MIMO channels,” IEEE Trans. Commun. , vol. 69, no. 10, pp. 7063-7076, Oct

  15. [23]

    Newtonized orthog- onal matching pursuit: Frequency estimation over the continuum,

    B. Mamandipoor, D. Ramasamy, and U. Madhow, “Newtonized orthog- onal matching pursuit: Frequency estimation over the continuum,” IEEE Trans. Signal Process. , vol. 64, no. 19, pp. 5066-5081, Oct. 2016. I-B, II-C

    2016

  16. [24]

    Millimeter wave channel modeling and cellular capacity evaluation,

    M. R. Akdeniz et al., “Millimeter wave channel modeling and cellular capacity evaluation,” IEEE J. Sel. Areas Commun. , vol. 32, no. 6, pp. 1164-1179, Jun. 2014. II-C

    2014

  17. [25]

    On indoor millimeter wave massive MIMO channels: Measurement and simulation,

    B. Ai et al ., “On indoor millimeter wave massive MIMO channels: Measurement and simulation,” IEEE J. Sel. Areas Commun. , vol. 35, no. 7, pp. 1678-1690, Jul. 2017. II-C, III-D

    2017

  18. [26]

    Efficient downlink channel reconstruction for FDD multi-antenna Systems,

    Y . Han, T. -H. Hsu, C. -K. Wen, K. -K. Wong, and S. Jin, “Efficient downlink channel reconstruction for FDD multi-antenna Systems,” IEEE Trans. Wireless Commun., vol. 18, no. 6, pp. 3161-3176, Jun. 2019. II-C, IV-D

    2019

  19. [27]

    Near- field channel reconstruction in sensing RIS-assisted wireless commu- nication systems,

    J. Tian, Y . Han, S. Jin, X. Li, J. Zhang, and M. Matthaiou, “Near- field channel reconstruction in sensing RIS-assisted wireless commu- nication systems,” IEEE Trans. Wireless Commun. , early access, doi: 10.1109/TWC.2024.3389026. II-C

    2024

  20. [28]

    Spatial channel characteristics in outdoor environments and their impact on BS antenna system performance,

    K. I. Pedersen, P. E. Mogensen, and B. H. Fleury, “Spatial channel characteristics in outdoor environments and their impact on BS antenna system performance,” in Proc. IEEE VTC, pp. 719-723, May 1998. III-A, III-A

    1998

  21. [29]

    A stochastic model of the temporal and azimuthal dispersion seen at the base station in outdoor propagation environments,

    K. I. Pedersen, P. E. Mogensen, and B. H. Fleury, “A stochastic model of the temporal and azimuthal dispersion seen at the base station in outdoor propagation environments,” IEEE Trans. Veh. Technol., vol. 49, no. 2, pp. 437-447, Mar. 2000. III-A

    2000

  22. [30]

    A statistical model for indoor multipath propagation,

    A. Saleh and R. Valenzuela, “A statistical model for indoor multipath propagation,” IEEE J. Sel. Areas Commun. , vol. 5, no. 2, pp. 128-137, Feb. 1987. III-C

    1987

  23. [31]

    Measurement-based massive MIMO channel modeling for outdoor LoS and NLoS environments,

    J. Chen, X. Yin, X. Cai, and S. Wang, “Measurement-based massive MIMO channel modeling for outdoor LoS and NLoS environments,” IEEE Access, vol. 5, pp. 2126-2140, 2017. III-E

    2017

  24. [32]

    Measuring sparsity of wireless channels,

    H. Zhang, R. He, B. Ai, S. Cui, and H. Zhang, “Measuring sparsity of wireless channels,” IEEE Trans. Cogn. Commun. Netw., vol. 7, no. 1, pp. 133-144, Mar. 2021. III-F

    2021

  25. [33]

    Channel sparsity variation and model-based analysis on 6, 26, and 105 GHz measurements,

    X. Liu et al., “Channel sparsity variation and model-based analysis on 6, 26, and 105 GHz measurements,” IEEE Trans. Veh. Technol. , early access, doi: 10.1109/TVT.2024.3371029. III-F

    2024

  26. [34]

    Non-stationarities in extra-large-scale massive MIMO,

    E. D. Carvalho, A. Ali, A. Amiri, M. Angjelichinoski, and R. W. Heath, “Non-stationarities in extra-large-scale massive MIMO,” IEEE Wireless Commun., vol. 27, no. 4, pp. 74-80, Aug. 2020. IV-A

    2020

  27. [35]

    Effi- cient ray-tracing simulation for near-field spatial non-stationary mmWave massive MIMO channel and its experimental validation,

    Z. Yuan, J. Zhang, V . Degli-Esposti, Y . Zhang, and W. Fan, “Effi- cient ray-tracing simulation for near-field spatial non-stationary mmWave massive MIMO channel and its experimental validation,” IEEE Trans. Wireless Commun., early access, doi: 10.1109/TWC.2024.335707. IV-A

  28. [36]

    Spherical-wave model for short-range MIMO,

    J. Jiang and M. A. Ingram, “Spherical-wave model for short-range MIMO,” IEEE Trans. Commun. , vol. 53, no. 9, pp. 1534-1541, Sept

  29. [37]

    Channel estimation via orthogonal matching pursuit for hybrid MIMO systems in millimeter wave commu- nications,

    J. Lee, G. -T. Gil and, Y . H. Lee, “Channel estimation via orthogonal matching pursuit for hybrid MIMO systems in millimeter wave commu- nications,” IEEE Trans. Commun. , vol. 64, no. 6, pp. 2370-2386, Jun

  30. [38]

    Channel estimation for TDD/FDD massive MIMO systems with channel covariance computing,

    H. Xie, F. Gao, S. Jin, J. Fang, and Y . -C. Liang, “Channel estimation for TDD/FDD massive MIMO systems with channel covariance computing,” IEEE Trans. Wireless Commun., vol. 17, no. 6, pp. 4206-4218, Jun. 2018. IV-C

    2018

  31. [39]

    Efficient multiband channel reconstruction and tracking for hybrid mmWave MIMO systems,

    W. Chen, Y . Han, S. Jin, and H. Sun, “Efficient multiband channel reconstruction and tracking for hybrid mmWave MIMO systems,” IEEE Trans. Commun., vol. 69, no. 12, pp. 8501-8517, Dec. 2021. IV-D

    2021

  32. [40]

    Spectral efficiency of DFT- based processing hybrid architectures in massive MIMO,

    W. Tan, M. Matthaiou, S. Jin, and X. Li, “Spectral efficiency of DFT- based processing hybrid architectures in massive MIMO,” IEEE Wireless Commun. Lett., vol. 6, no. 5, pp. 586-589, Oct. 2017. IV-F1

    2017

  33. [41]

    DFT-based hybrid beamforming multiuser systems: Rate analysis and beam selection,

    Y . Han, S. Jin, J. Zhang, J. Zhang, and K. -K. Wong, “DFT-based hybrid beamforming multiuser systems: Rate analysis and beam selection,” IEEE J. Sel. Top. Signal Proces., vol. 12, no. 3, pp. 514-528, Jun. 2018. IV-F1

    2018

  34. [42]

    Beam squint and channel estimation for wideband mmWave massive MIMO-OFDM systems,

    B. Wang, M. Jian, F. Gao, G. Y . Li, and H. Lin, “Beam squint and channel estimation for wideband mmWave massive MIMO-OFDM systems,” IEEE Trans. Signal Proces. , vol. 67, no. 23, pp. 5893-5908, Dec. 2019. IV-F2

    2019

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.