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Modular XL-Array-Enabled 3-D Localization based on Hybrid Spherical-Planar Wave Model in Terahertz Systems

T0 review · 2 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read A modular extra-large Terahertz array with one RF chain per sub-array can localize users in three dimensions by combining spherical-wave geometry between sub-arrays with planar-wave compressed-sensing angle estimation inside them.

desk verdict A coherent three-stage modular-XL-MIMO localization algorithm with a real dictionary-pruning complexity win, but the SNS-handling step rests on an untested power-threshold assumption and the simulations need tightening before the results become a design recipe. read the letter →

arxiv 2504.13455 v1 pith:HLRWYXO7 submitted 2025-04-18 eess.SP

Yang Zhang , Ruidong Li , Cunhua Pan , Hong Ren , Tuo Wu , Changhong Wang This is my paper · ORCID
classification eess.SP
keywords 3-DlocalizationTerahertzmodularXL-MIMOhybridspherical-planarwavemodelspatialnon-stationaritycompressedsensingSOMPweightedleastsquares
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 is trying to establish that a modular extra-large Terahertz antenna array, in which each of many small sub-arrays has a single radio-frequency chain, can deliver 3-D user localization without the prohibitive complexity of full near-field processing. Its proposed three-stage method selects the sub-arrays that actually see a user, estimates their line-of-sight angles by compressed sensing, triangulates those angles with weighted least squares, and then refines the angles of the remaining visible sub-arrays on a small dictionary built around the coarse position. The central claim, backed by simulations in the paper, is that the fine estimate lands close to the full-dictionary accuracy while cutting complexity, and that it beats a collocated array design and a near-field joint-estimation benchmark at low signal-to-noise ratios. A sympathetic reader would see this as evidence that spatial non-stationarity, usually a nuisance in extra-large arrays, can be turned into an advantage by using only the informative sub-arrays as localization anchors.

What carries the argument

The central object is the hybrid spherical-planar wave model (HSPWM), which assigns each sub-array its own pair of azimuth and elevation angles of arrival (spherical-wave relation to the user) while letting all antennas inside a sub-array share one steering vector (planar-wave approximation). It does the load-bearing work: the spherical side makes every visible sub-array a geometrically distinct anchor for WLS triangulation, and the planar side keeps the per-sub-array angle estimation cheap enough for compressed sensing. The three algorithmic mechanisms that run on top of it are the normalized-power visibility test, the SOMP block-sparse recovery of the LoS angle, and the reduced dictionary that shrinks each non-typical sub-array's search space from $I_k J_k$ to $(2\bar{i}+1)(2\bar{j}+1)$ codewords around the coarse position.

What would settle it

Set up the same simulation with an added strong single-reflection NLoS path whose received power at some sub-arrays is comparable to the LoS power, then compare the sub-arrays selected by the threshold in (17) with the true line-of-sight visibility set. If misclassified sub-arrays displace the WLS estimate or the RMSE rises sharply relative to the LoS-only baseline, the power-separation premise fails; the paper does not sweep the threshold $\psi$ or the LoS and NLoS power ratio.

Watch

Extended reading notes

Core claim

Using the hybrid spherical-planar wave model (HSPWM), the paper treats the channel to each sub-array as a planar-wave steering vector whose antennas share one azimuth and one elevation angle of arrival, while the sub-array locations themselves are tied to the user position through spherical-wave geometry. On this model the localization problem becomes: find which sub-arrays have line-of-sight paths, estimate their angles, triangulate, and refine. The paper's contribution is a complete pipeline that does this: a normalized received-power criterion selects visible sub-arrays; simultaneous orthogonal matching pursuit over subcarriers, formulated as block-sparse recovery, estimates the LoS angles of the strongest visible sub-arrays; pseudo-linear equations feed those angles into an iterative weighted least squares coarse position; and a reduced dictionary centered on that coarse position estimates the angles of the remaining visible sub-arrays before a final WLS refinement. Simulation results show the fine RMSE close to the full-dictionary upper bound and better low-SINR performance relative to the benchmarks, with complexity dominated by $O(K_{\mathrm{Ref}} I_k J_k M_S N I)$ rather than by processing every sub-array with a full dictionary.

Load-bearing premise

The method hinges on the assumption that a sub-array with a line-of-sight path receives much more power than one without it, so a fixed normalized-power threshold cleanly separates visible from invisible sub-arrays; if a reflected path or shadowing blurs that separation, the wrong anchors are selected and the triangulation is biased.

Editorial extensions

If this is right

  • Using only a small number of strongest visible sub-arrays for the first estimate, then a reduced dictionary for the rest, the fine position lands close to the full-dictionary result while cutting per-stage complexity by a quadratic factor.
  • Because sub-arrays with no line-of-sight path are discarded, the method tolerates spatial non-stationarity: with only nine of twenty-five sub-arrays visible, positioning remains acceptable and approaches the no-blockage case at high SINR.
  • The sub-array interval has an optimal value: larger spacing widens the angular spread the WLS triangulation sees, but beyond a point the far sub-arrays fade enough that their angle estimates hurt rather than help.
  • For a fixed total antenna count, there is a best split between the number of sub-arrays and antennas per sub-array, since sub-arrays supply anchors and antennas supply per-anchor angular resolution.
  • Increasing training blocks improves RMSE but with diminishing returns, suggesting transmit power is a more effective way to buy accuracy than longer pilots.

Reading between the lines

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

  • The paper estimates time of arrival with a MUSIC step but never feeds those ranges into the WLS estimator; combining ToA ranges with the AoA-based pseudo-linear equations is a natural next step that should help when few sub-arrays are visible.
  • The visible and non-visible split depends on a fixed normalized-power threshold that is never swept; a calibration curve of RMSE versus the threshold under varying LoS and NLoS power ratios would test how much margin the selection step actually needs.
  • Because the reduced dictionary is centered on the coarse position, a coarse estimate far from the true position could bias the fine stage; an adaptive multi-resolution dictionary that re-expands when the coarse residual is large would guard against that failure mode.
  • The optimal SA interval and AE allocation results suggest the array layout can be treated as a design variable: one could pose a joint layout-and-estimation optimization that maximizes positioning accuracy under a hardware budget, an optimization the paper stops short of solving.
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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

2 major / 6 minor

Summary. This paper considers 3-D localization of multiple single-antenna UEs in a THz uplink system equipped with a modular XL-MIMO array with sub-connected hybrid beamforming. The authors model the channel with a hybrid spherical-planar wave model, where each sub-array sees a distinct incident angle while a planar wavefront is assumed within each SA, and incorporate spatial non-stationarity via visibility indicators. They propose a three-stage algorithm: (i) identify visible SAs based on normalized received power and estimate AoAs of a few typical visible SAs via simultaneous orthogonal matching pursuit (SOMP) over a frequency-domain block-sparse dictionary; (ii) obtain a coarse UE position via iterative weighted least squares (WLS) from the AoA pseudo-linear equations; (iii) estimate AoAs of the remaining visible SAs with a geometrically reduced dictionary and refine the position with WLS. Simulations compare RMSE versus SINR against a near-field joint channel estimation and localization benchmark and a collocated DFT-MUSIC design, and examine the effects of SA interval, antenna allocation, training blocks, and visibility region layout. The central claim is that the proposed framework achieves accuracy close to the full-dictionary upper bound at substantially reduced complexity, and outperforms the benchmarks especially at low SINR.

Significance. If the claims are upheld, the paper contributes a practical and computationally efficient pipeline for SNS-aware 3-D localization in modular XL-MIMO THz systems. The use of geometric priors to shrink the CS dictionary is a sensible complexity-reduction idea, and the modular-array versus collocated comparison is a useful design insight. The WLS pseudo-linear derivation is correct to first order, and the three-stage coarse-to-fine structure is well motivated. However, the significance is tempered by the fact that the visible-SA selection and LoS-identification steps rest on an untested power-separation assumption, and the z-axis phase convention in Eq. (8) appears to be the conjugate of the standard UPA response; both need to be resolved before the simulation results can support the paper's conclusions.

major comments (2)
  1. [Section III-B, Eq. (17) and Fig. 10] The visible-SA selection criterion in Eq. (17) assumes that LoS received power at visible SAs 'highly exceeds' that at invisible ones and that a fixed threshold ψ separates them. The paper never states the value of ψ used, never sweeps ψ, and never varies the reflection coefficient Γ in Eq. (5), which controls the NLoS power level. The VR experiments in Section VI-F impose visibility patterns a priori rather than testing whether Eq. (17) discovers them from received powers. Since a strong single-reflection NLoS path or a low-power visible SA could place an invisible SA above the threshold or a visible one below it, the selected anchors in Stage 1 can be wrong, biasing the WLS coarse estimate and the Stage 3 reduced-dictionary center. This directly undermines the claimed low-SINR advantage in Fig. 6. The authors should report the default ψ, provide a sensitivity study over ψ and Γ, and ideally test the selection when the LoS/NLoS power gap is reduced.
  2. [Section II-B, Eqs. (8) and (10)] The virtual elevation AoA is defined as φ = -sinϕ in Eq. (8), so the z-axis steering vector in Eq. (10) has phase e^{-j2π d/λ sinϕ}, the conjugate of the standard UPA response for a wave arriving from elevation ϕ. With the array deployed along the positive z-axis and the geometry in Eq. (3b) defining ϕ as the elevation above the x-y plane, a physical plane wave from that direction should exhibit a phase advance e^{+j2π d/λ sinϕ}. If the channel model in Eq. (6) and the dictionary both use the flipped sign, the system is internally consistent but unphysical; if any step uses the standard convention, the estimated ϕ is negated and the WLS z-coordinate estimate is biased. The authors must verify the sign convention against the physical array response, correct Eqs. (8) and (10) if needed, and re-run the simulations with the corrected model.
minor comments (6)
  1. [Section III-D] The ToA estimator derived via MUSIC in Eqs. (28)-(32) is not used in either Stage 2 or Stage 3; the positioning pipeline relies solely on AoA. Either integrate the ToA as an additional measurement or remove this section to avoid a dangling contribution.
  2. [Section IV-B, Eq. (45)] The weight matrix W depends on the covariance Rz of the AoA estimation errors, but the paper does not specify how Rz is computed or estimated. Algorithm 2 updates W using this covariance, but no model or empirical procedure is given. Please state the error covariance used in the simulations or provide an approximation.
  3. [Section VI-C] The text says 'D = 0.2 m in default simulation setup' but Table I lists the default SA interval as D = 1 m; clarify which value is used for the low-interval case.
  4. [Section VI-E] The reference to 'Fig. 8' in the discussion of training blocks should be 'Fig. 9'.
  5. [Table II and Fig. 6] The schemes SOMP-LS and OMP-LS are plotted but never defined in the text; add a brief description of these benchmarks.
  6. [Abstract and throughout] There are several typographical errors, including 'structual' in the abstract and 'indicting' in Section VI-C; the paper would benefit from careful proofreading.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the AoA-WLS position chain and reduced-dictionary refinement are self-contained; power-based SA selection is an untested assumption, not a fitted prediction.

full rationale

The proposed 3-D localization is derived from forward geometric and sparse-recovery equations, not from the output positions. Stage 1 estimates AoAs with SOMP on the dictionary (19)-(23); Stage 2 solves the closed-form WLS pseudo-linear system (38)-(47), whose inputs are the AoA estimates and known SA geometry; Stage 3 constructs the reduced dictionary (48) around the coarse estimate, which is a standard coarse-to-fine refinement and does not presuppose the fine AoAs or the final position. The visible-SA selection in (17) relies on the assumption that LoS power dominates NLoS power and on an unreported threshold ψ; however, this is a robustness and validation gap rather than a circular step, since the paper makes no fitting claim that would turn the selection into a renamed prediction. Self-citations, including [8]-[11], [27], [35], and [42], appear only as background or benchmark context and do not supply a load-bearing premise for the derivation. The evaluation is anchored to external comparisons (the DFT-MUSIC collocated design and the [41]-based benchmark), so the central accuracy and complexity claims are not self-referential. Accordingly, no circular steps are present.

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

The central claim rests on a set of standard signal-processing tools plus domain assumptions about the THz channel and the modular array. No new physical entities are introduced. The load-bearing free parameters are the SA-selection threshold ψ, the number of typical SAs K_Ref, the reduced-dictionary radii, and the dictionary grid spacing; the paper tunes or fixes these by hand without a principled selection rule. The HSPWM and LoS-dominance assumptions are the most fragile domain assumptions because the whole algorithm depends on them and they are not stress-tested.

free parameters (5)
  • ψ (visible SA selection threshold) = not reported
    Used in Eq. (17) to classify SAs as visible vs invisible; no value or sensitivity analysis is given.
  • K_Ref (number of typical visible SAs) = 2 or 3 in simulations
    Controls the complexity-accuracy trade-off; Fig. 5 shows performance varies with K_Ref, so reported gains depend on this hand choice.
  • \bar{i}, \bar{j} (dictionary reduction radii) = 8 in Table I
    Pre-defined window size for the reduced dictionary in (48); affects both complexity and AoA accuracy, and no sensitivity analysis is provided.
  • Dictionary grid spacings Δω, Δφ = 0.01 rad
    Set in Table I; determines the fundamental AoA resolution and the dictionary size, hence complexity and ceiling accuracy.
  • SA interval D and AE allocation (Kx, Mx) = D=1m, K=25, M_SA=25
    The paper itself shows RMSE is strongly non-monotonic in D and AE allocation (Figs. 7-8), so the default operating point is a favorable choice.
assumptions (6)
  • domain assumption Within each SA, all antennas share the same azimuth/elevation AoAs (planar wave), while between SAs the spherical wave model applies; the CS dictionary and pseudo-linear equations rely on this.
    Section II-B states this as the channel model. If the SA aperture is large relative to distance, the common-AoA assumption breaks and the CS dictionary is mismatched.
  • domain assumption Received power at visible SAs highly exceeds that at invisible SAs, so normalized-power thresholding (17) and largest-correlation SOMP (Algorithm 1) can isolate LoS.
    Section III-B states 'the loss of NLoS paths is much higher than that of the LoS path'; this is required for reliable SA selection and LoS AoA identification.
  • standard math AoA estimation errors satisfy sinΔθ≈Δθ, cosΔθ≈1 (and similarly for Δϕ), used to derive the pseudo-linear residual model in (35).
    Section IV-A; standard first-order Taylor expansion, reasonable for small errors, but not verified for low-SINR outliers.
  • domain assumption The entries of the sensing matrix (F_RF)^H A are i.i.d. zero-mean complex Gaussian, making the CS sensing matrix suitable for sparse recovery.
    Section III-C; an approximation based on large MS and random phases; the deterministic DFT-like analog combiner may not fully satisfy it.
  • domain assumption The channel between each UE and SA contains one LoS path and at most L (known, L=2) single-reflection NLoS paths, and the LoS path has the largest gain.
    Eq. (12) and Section VI-A; the algorithm selects the single largest-correlation block, so paths with comparable gain would break it.
  • domain assumption Only LoS and single-reflection NLoS are considered; multiple reflections and diffuse scattering are neglected.
    Section II-B: 'signals suffering multiple reflections and scatterings are severely attenuated owing to the path fading in the THz band'.

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Cite this review

Pith. "Pith review of Modular XL-Array-Enabled 3-D Localization based on Hybrid Spherical-Planar Wave Model in Terahertz Systems." pith.science (2026). https://pith.science/paper/HLRWYXO7

@misc{pith2026250413455,
  author       = {Pith},
  title        = {Pith review of: Modular XL-Array-Enabled 3-D Localization based on Hybrid Spherical-Planar Wave Model in Terahertz Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HLRWYXO7}},
  note         = {Machine review of arXiv:2504.13455}
}
read the original abstract

This work considers the three-dimensional (3-D) positioning problem in a Terahertz (THz) system enabled by a modular extra-large (XL) array with sub-connected architecture. Our purpose is to estimate the Cartesian Coordinates of multiple user equipments (UEs) with the received signal of the RF chains while considering the spatial non-stationarity (SNS). We apply the hybrid spherical-planar wave model (HSPWM) as the channel model owing to the structual feature of the modular array, and propose a 3-D localization algorithm with relatively high accuracy and low complexity. Specifically, we first distinguish the visible sub-arrays (SAs) located in the VR and estimate the angles-of-arrival (AoAs) from each UE to typical visible SAs with the largest receive power via compressed sensing (CS) method. In addition, we apply the weighted least square (WLS) method to obtain a coarse 3-D position estimation of each UE according to the AoA estimations. Then, we estimate the AoAs of the other SAs with a reduced dictionary (RD)-CS-based method for lower computational complexity, and utilize all the efficient AoA estimations to derive a fine position estimation. Simulation results indicate that the proposed positioning framework based on modular XL-array can achieve satisfactory accuracy with evident reduction in complexity. Furthermore, the deployment of SAs and the allocation of antenna elements need to be specially designed for better positioning performance.

Figures

Figures reproduced from arXiv: 2504.13455 by the authors.

Figure 2
Figure 2. Hybrid beamforming structure of the modular [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 1
Figure 1. System Model. A. Modular Architecture and Transmission Model For simplicity in geometric relation and further manipu￾lations, we consider that the modular XL-MIMO array is deployed parallel to the xOz plane. In addition, the M antennas are uniformly divided into K SAs, each SA is set as a UPA with Mx × Mz = MS antennas, and the K SAs are deployed in a Kx × Kz uniform planar form with horizontal and elevation interva… view at source ↗
Figure 4
Figure 4. Training analog combiner and pilot design. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figures from the paper (6 more)
Figure 3
Figure 3. Figure 3: Flowchart of the localization framework. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png]
Figure 5
Figure 5. Figure 5: and 6. 1) Benchmark 1: In this scenario, the key idea of the joint channel estimation and positioning algorithm proposed in [41] is utilized. Some adjustments are applied to adapt to the differences between the system models: All UEs are considered as active UEs, and w…
Figure 6
Figure 6. Figure 6: RMSE comparison with different algorithms. [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: RMSE with different SA interval. each AoA estimation. Such phenomenon reveals that there is a trade-off between the utilized spatial DoFs within the whole propagation region and the accuracy w.r.t. each AoA estimations. In addition, it is essential to properly design A…
Figure 8
Figure 8. Figure 8: illustrates the impact of different allocation schemes with the same total number of the AEs. In this simulation setup, the total amount of the AEs is set as M = 24 × 24 = 576 with different allocation schemes, while we still set Mx = Mz and Kx = Kz for fairness in acc…
Figure 10
Figure 10. Figure 10: illustrates robustness of the proposed framework to the existence of VR. It is worth noting that “VR = Diagonals” refers to the scheme where the 9 SAs on the diagonals of the whole 5 × 5 modular array are assumed to locate in the VR, and “VR = 3 × 3” refers to the sch…

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Reference graph

Works this paper leans on

45 extracted references · 40 canonical work pages · cited by 1 Pith paper

  1. [1]

    Service-aware 6G: An intelligent and open network based on the convergence of communication, computing and caching,

    Y . Zhou, L. Liu, L. Wang, N. Hui, X. Cui, J. Wu, Y . Peng, Y . Qi, and C. Xing, “Service-aware 6G: An intelligent and open network based on the convergence of communication, computing and caching,” Digit. Commun. Netw., vol. 6, no. 3, pp. 253–260, Aug. 2020. MANUSCRIPT SUBMITTED TO IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 13

    work page 2020

  2. [2]

    6G wireless networks: Vision, requirements, architecture, and key technologies,

    Z. Zhang, Y . Xiao, Z. Ma, M. Xiao, Z. Ding, X. Lei, G. K. Karagiannidis, and P. Fan, “6G wireless networks: Vision, requirements, architecture, and key technologies,” IEEE V eh. Technol. Mag. , vol. 14, no. 3, pp. 28–41, Sep. 2019

    work page 2019

  3. [3]

    Cellular- connected wireless virtual reality: Requirements, challenges, and solu- tions,

    F. Hu, Y . Deng, W. Saad, M. Bennis, and A. H. Aghvami, “Cellular- connected wireless virtual reality: Requirements, challenges, and solu- tions,” IEEE Commun. Mag. , vol. 58, no. 5, pp. 105–111, May 2020

    work page 2020

  4. [4]

    Survey of wireless indoor positioning techniques and systems,

    H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Trans. Syst., Man, and Cybern. C, Appl. Rev. , vol. 37, no. 6, pp. 1067–1080, Nov. 2007

    work page 2007

  5. [5]

    Indoor tracking: Theory, methods, and technologies,

    D. Dardari, P. Closas, and P. M. Djuri ´c, “Indoor tracking: Theory, methods, and technologies,” IEEE Trans. V eh. Technol., vol. 64, no. 4, pp. 1263–1278, Apr. 2015

    work page 2015

  6. [6]

    3-D indoor positioning for millimeter-wave massive MIMO systems,

    Z. Lin, T. Lv, and P. T. Mathiopoulos, “3-D indoor positioning for millimeter-wave massive MIMO systems,” IEEE Trans. Commun. , vol. 66, no. 6, pp. 2472–2486, Jun. 2018

    work page 2018

  7. [7]

    Millimeter- wave MIMO-NOMA-based positioning system for internet-of-things applications,

    L. Han, R. Liu, Z. Wang, X. Yue, and J. S. Thompson, “Millimeter- wave MIMO-NOMA-based positioning system for internet-of-things applications,” IEEE Internet Things J. , vol. 7, no. 11, pp. 11 068–11 077, Nov. 2020

    work page 2020

  8. [8]

    RIS-Aided Localization Algorithm and Analysis: Tackling Non-Gaussian Angle Estimation Errors

    T. Wu, C. Pan, Y . Pan, S. Hong, H. Ren, and M. Elkashlan, “RIS- aided localization algorithm and analysis: Tackling non-gaussian angle estimation errors,” 2022, arXiv: 2208.07606

    work page Pith review arXiv 2022

Show all 45 references
  1. [9]

    Fingerprint-based mmwave positioning system aided by reconfigurable intelligent surface,

    T. Wu, C. Pan, Y . Pan, H. Ren, M. Elkashlan, and C.-X. Wang, “Fingerprint-based mmwave positioning system aided by reconfigurable intelligent surface,” IEEE Wireless Commun. Lett. , vol. 12, no. 8, pp. 1379–1383, Aug 2023

    2023

  2. [10]

    Joint angle estimation error analysis and 3-D positioning algorithm design for mmwave positioning system,

    T. Wu, C. Pan, Y . Pan, S. Hong, H. Ren, M. Elkashlan, F. Shu, and J. Wang, “Joint angle estimation error analysis and 3-D positioning algorithm design for mmwave positioning system,” IEEE Internet Things J., vol. 11, no. 2, pp. 2181–2197, Jan. 2024

    2024

  3. [11]

    Exploit high-dimensional RIS information to local- ization: What is the impact of faulty element?

    T. Wu, C. Pan, K. Zhi, H. Ren, M. Elkashlan, C.-X. Wang, R. Schober, and X.-H. You, “Exploit high-dimensional RIS information to local- ization: What is the impact of faulty element?” IEEE J. Sel. Areas Commun., vol. 42, no. 10, pp. 2803–2819, Oct. 2024

    2024

  4. [12]

    Be- yond 5G: Thz-based medium access protocol for mobile heterogeneous networks,

    A. S. Cacciapuoti, K. Sankhe, M. Caleffi, and K. R. Chowdhury, “Be- yond 5G: Thz-based medium access protocol for mobile heterogeneous networks,” IEEE Commun. Mag., vol. 56, no. 6, pp. 110–115, Jun. 2018

    2018

  5. [13]

    Wireless communications and applications above 100 GHz: Opportunities and challenges for 6G and beyond,

    T. S. Rappaport, Y . Xing, O. Kanhere, S. Ju, A. Madanayake, S. Mandal, A. Alkhateeb, and G. C. Trichopoulos, “Wireless communications and applications above 100 GHz: Opportunities and challenges for 6G and beyond,” IEEE Access , vol. 7, pp. 78 729–78 757, Jun. 2019

    2019

  6. [14]

    A survey on terahertz communications,

    Z. Chen, X. Ma, B. Zhang, Y . Zhang, Z. Niu, N. Kuang, W. Chen, L. Li, and S. Li, “A survey on terahertz communications,” China Commun. , vol. 16, no. 2, pp. 1–35, Feb. 2019

    2019

  7. [15]

    Millimeter wave and terahertz urban microcell propagation measurements and models,

    Y . Xing and T. S. Rappaport, “Millimeter wave and terahertz urban microcell propagation measurements and models,” IEEE Commun. Lett. , vol. 25, no. 12, pp. 3755–3759, Dec. 2021

    2021

  8. [16]

    Position location for futuristic cellular communications: 5G and beyond,

    O. Kanhere and T. S. Rappaport, “Position location for futuristic cellular communications: 5G and beyond,” IEEE Commun. Mag. , vol. 59, no. 1, pp. 70–75, Jan. 2021

    2021

  9. [17]

    Fraunhofer and fresnel distances: Unified derivation for aperture antennas,

    K. T. Selvan and R. Janaswamy, “Fraunhofer and fresnel distances: Unified derivation for aperture antennas,” IEEE Ant. Propag. Mag. , vol. 59, no. 4, pp. 12–15, Aug. 2017

    2017

  10. [18]

    Channel estimation for extremely large-scale MIMO: Far-field or near-field?

    M. Cui and L. Dai, “Channel estimation for extremely large-scale MIMO: Far-field or near-field?” IEEE Trans. Commun. , vol. 70, no. 4, pp. 2663–2677, Apr. 2022

    2022

  11. [19]

    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. 14 569–14 594, Aug. 2023

    2023

  12. [20]

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

    Z. Wang, J. Zhang, H. Du, D. Niyato, S. Cui, B. Ai, M. Debbah, K. B. Letaief, and H. V . Poor, “A tutorial on extremely large-scale MIMO for 6G: Fundamentals, signal processing, and applications,” IEEE Commun. Surveys Tuts., vol. 26, no. 3, pp. 1560–1605, Jan. 2024

    2024

  13. [21]

    Near-field localization with a reconfigurable intelligent surface acting as lens,

    Z. Abu-Shaban, K. Keykhosravi, M. F. Keskin, G. C. Alexandropoulos, G. Seco-Granados, and H. Wymeersch, “Near-field localization with a reconfigurable intelligent surface acting as lens,” inProc. IEEE Int. Conf. Commun., Jun. 2021, pp. 1–6

    2021

  14. [22]

    Reconfigurable in- telligent surfaces for localization: Position and orientation error bounds,

    A. Elzanaty, A. Guerra, F. Guidi, and M.-S. Alouini, “Reconfigurable in- telligent surfaces for localization: Position and orientation error bounds,” IEEE Transactions on Signal Processing , vol. 69, pp. 5386–5402, Aug. 2021

    2021

  15. [23]

    RIS-enabled localization continuity under near-field conditions,

    M. Rahal, B. Denis, K. Keykhosravi, B. Uguen, and H. Wymeersch, “RIS-enabled localization continuity under near-field conditions,” in Proc. IEEE Int. Workshop Signal Process. Adv. Wireless Commun. , Sep. 2021, pp. 436–440

    2021

  16. [24]

    Compressive near-field localization for multipath RIS-aided environments,

    O. Rinchi, A. Elzanaty, and M.-S. Alouini, “Compressive near-field localization for multipath RIS-aided environments,” IEEE Communica- tions Letters , vol. 26, no. 6, pp. 1268–1272, Jun. 2022

    2022

  17. [25]

    Mixed near-field and far-field source localization based on exact spatial propagation geometry,

    J. He, L. Li, T. Shu, and T.-K. Truong, “Mixed near-field and far-field source localization based on exact spatial propagation geometry,” IEEE Trans. V eh. Technol., vol. 70, no. 4, pp. 3540–3551, Apr. 2021

    2021

  18. [26]

    Mixed near-field and far-field localization and array calibration with partly calibrated arrays,

    J. He, T. Shu, L. Li, and T.-K. Truong, “Mixed near-field and far-field localization and array calibration with partly calibrated arrays,” IEEE Trans. Signal Process. , vol. 70, pp. 2105–2118, Apr. 2022

    2022

  19. [27]

    RIS-aided near-field localization and channel estimation for the terahertz system,

    Y . Pan, C. Pan, S. Jin, and J. Wang, “RIS-aided near-field localization and channel estimation for the terahertz system,” IEEE J. Sel. Topics Signal Process., vol. 17, no. 4, pp. 878–892, Jul. 2023

    2023

  20. [28]

    Low- complexity channel estimation for extremely large-scale MIMO in near field,

    C. Huang, J. Xu, W. Xu, X. You, C. Yuen, and Y . Chen, “Low- complexity channel estimation for extremely large-scale MIMO in near field,” IEEE Wireless Commun. Lett. , vol. 13, no. 3, pp. 671–675, Mar. 2024

    2024

  21. [29]

    Near-field localization and channel reconstruction for ELAA systems,

    Z. Lu, Y . Han, S. Jin, and M. Matthaiou, “Near-field localization and channel reconstruction for ELAA systems,” IEEE Trans. Wireless Commun., vol. 23, no. 7, pp. 6938–6953, Jul. 2024

    2024

  22. [30]

    Spatial non- stationary near-field channel modeling and validation for massive MIMO systems,

    Z. Yuan, J. Zhang, Y . Ji, G. F. Pedersen, and W. Fan, “Spatial non- stationary near-field channel modeling and validation for massive MIMO systems,” IEEE Trans. Antennas and Propag. , vol. 71, no. 1, pp. 921– 933, Jan. 2023

    2023

  23. [31]

    Multi-user modular XL-MIMO communications: Near-field beam focusing pattern and user grouping,

    X. Li, Z. Dong, Y . Zeng, S. Jin, and R. Zhang, “Multi-user modular XL-MIMO communications: Near-field beam focusing pattern and user grouping,” IEEE Trans. Wireless Commun. , vol. 23, no. 10, pp. 13 766– 13 781, Oct. 2024

    2024

  24. [32]

    Near-field modeling and performance analysis of modular extremely large-scale array commu- nications,

    X. Li, H. Lu, Y . Zeng, S. Jin, and R. Zhang, “Near-field modeling and performance analysis of modular extremely large-scale array commu- nications,” IEEE Commun. Lett. , vol. 26, no. 7, pp. 1529–1533, Jul. 2022

    2022

  25. [33]

    Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems,

    X. Yu, J.-C. Shen, J. Zhang, and K. B. Letaief, “Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems,” IEEE J. Sel. Topics Signal Process. , vol. 10, no. 3, pp. 485–500, Apr. 2016

    2016

  26. [34]

    Spatially sparse precoding in millimeter wave MIMO systems,

    O. E. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, “Spatially sparse precoding in millimeter wave MIMO systems,” IEEE Trans. Wireless Commun. , vol. 13, no. 3, pp. 1499–1513, Mar. 2014

    2014

  27. [35]

    Hybrid precoding design for near-field wideband THz systems with spatial non-stationarity,

    M. Liu, C. Pan, K. Zhi, H. Ren, and J. Wang, “Hybrid precoding design for near-field wideband THz systems with spatial non-stationarity,”IEEE Commun. Lett. , vol. 28, no. 10, pp. 2372–2376, Oct. 2024

    2024

  28. [36]

    High-resolution AoA estima- tion for hybrid antenna arrays,

    S.-F. Chuang, W.-R. Wu, and Y .-T. Liu, “High-resolution AoA estima- tion for hybrid antenna arrays,” IEEE Trans. Antennas Propag. , vol. 63, no. 7, pp. 2955–2968, Jul. 2015

    2015

  29. [37]

    Low- complexity and high-resolution DOA estimation for hybrid analog and digital massive MIMO receive array,

    F. Shu, Y . Qin, T. Liu, L. Gui, Y . Zhang, J. Li, and Z. Han, “Low- complexity and high-resolution DOA estimation for hybrid analog and digital massive MIMO receive array,” IEEE Trans. Commun. , vol. 66, no. 6, pp. 2487–2501, Jun. 2018

    2018

  30. [38]

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

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

    2022

  31. [39]

    Cross-field channel esti- mation for ultra massive-MIMO THz systems,

    S. Tarboush, A. Ali, and T. Y . Al-Naffouri, “Cross-field channel esti- mation for ultra massive-MIMO THz systems,” IEEE Trans. Wireless Commun., vol. 23, no. 8, pp. 8619–8635, Aug. 2024

    2024

  32. [40]

    Can far-field beam training be deployed for cross-field beam alignment in terahertz UM-MIMO communications?

    Y . Chen, C. Han, and E. Bj ¨ornson, “Can far-field beam training be deployed for cross-field beam alignment in terahertz UM-MIMO communications?” IEEE Trans. Wireless Commun. , vol. 23, no. 10, pp. 14 972–14 987, Oct. 2024

    2024

  33. [41]

    Sensing user’s activity, channel, and location with near-field extra-large-scale MIMO,

    L. Qiao, A. Liao, Z. Li, H. Wang, Z. Gao, X. Gao, Y . Su, P. Xiao, L. You, and D. W. K. Ng, “Sensing user’s activity, channel, and location with near-field extra-large-scale MIMO,” IEEE Trans. Commun., vol. 72, no. 2, pp. 890–906, Feb. 2024

    2024

  34. [42]

    Machine learning-based near-field emitter localization via grouped hybrid analog and digital massive MIMO receive array,

    Y . Li, F. Shu, J. Bai, C. Pan, Y . Wu, Y . Song, and J. Wang, “Machine learning-based near-field emitter localization via grouped hybrid analog and digital massive MIMO receive array,” 2024, arXiv: 2406.09695

    2024 arXiv

  35. [43]

    Multi-ray channel modeling and wideband characterization for wireless communications in the terahertz band,

    C. Han, A. O. Bicen, and I. F. Akyildiz, “Multi-ray channel modeling and wideband characterization for wireless communications in the terahertz band,” IEEE Trans. Wireless Commun. , vol. 14, no. 5, pp. 2402–2412, May 2015

    2015

  36. [44]

    Sum-rate maximization for intelligent reflecting surface assisted terahertz communications,

    Y . Pan, K. Wang, C. Pan, H. Zhu, and J. Wang, “Sum-rate maximization for intelligent reflecting surface assisted terahertz communications,” IEEE Trans. V eh. Technol., vol. 71, no. 3, pp. 3320–3325, Mar. 2022

    2022

  37. [45]

    A distance and bandwidth dependent adaptive modulation scheme for THz commu- nications,

    A.-A. A. Boulogeorgos, E. N. Papasotiriou, and A. Alexiou, “A distance and bandwidth dependent adaptive modulation scheme for THz commu- nications,” in Proc. IEEE Int. Workshop Signal Process. Adv. Wireless Commun. (SPA WC), Jun. 2018, pp. 1–5

    2018

Pith tools

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