Pith. sign in

REVIEW 10 minor 180 references

Cross Far- and Near-Field Beam Management Technologies in Millimeter-Wave and Terahertz MIMO Systems

T0 review · 0 major / 10 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 Near-field and cross-field channels give beam management a distance axis that far-field beams lack, and this survey shows how scanning, CSI estimation, beamforming, and tracking must be redesigned to exploit it.

desk verdict A useful, honest survey of field-aware beam management; no new results, but the taxonomy holds and the central distance-resolution claim is sound. read the letter →

arxiv 2504.18855 v1 pith:ODKDK3D4 submitted 2025-04-26 cs.IT eess.SPmath.IT

Yuhang Chen , Heyin Shen , Chong Han This is my paper · ORCID
classification cs.ITeess.SPmath.IT
keywords near-fieldcommunicationscross-fieldbeammanagementterahertzMIMOmillimeter-wavehybridsphericalandplanarwavemodelscanningtracking
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 survey argues that the decisive difference between far-field (FF) and near-field/cross-field (NF/CF) beam management is distance-domain resolution. In the FF regime, a beam is indexed by angle alone; in NF and CF regimes, the beam pattern also focuses in range, so scanning codebooks, channel estimation, beamforming, and tracking must treat angle and distance jointly. The paper develops this through channel-model analysis, introducing the hybrid spherical- and planar-wave model (HSPM) as a compact cross-field representation, and reviews beam management techniques across all four tasks. If the central claim is right, 6G millimeter-wave and terahertz systems cannot simply reuse far-field beam training procedures.

What carries the argument

The central object is the distance-dependent phase of the array response, captured in the cross-field setting by the hybrid spherical- and planar-wave model (HSPM). HSPM models spherical-wave propagation between subarrays and planar-wave propagation within each subarray, giving a parameter count of $N_p(1+5K_rK_t)$ instead of the quadratic count of the full spherical-wave model. The paper uses this model to show that the FF approximation error grows with $\pi d^2 L_t L_r/(\lambda D_{11})$, and that HSPM stays accurate when the communication distance exceeds the Rayleigh distance of each subarray. The HSPM-based beam pattern then depends on both angle and distance, which is what makes near-field and cross-field beam management different from far-field beam management.

What would settle it

A channel measurement campaign at 0.3 THz with an ultra-large array (for example, 1024 elements) spanning distances from 5 to 40 meters could falsify the central claim: if the measured HSPM approximation error relative to the full spherical-wave channel does not stay small when each subarray lies within its own Rayleigh distance, or if subarray path gains and reflector sets are not shared, then the distance-domain beam management rationale loses its foundation.

Watch

Extended reading notes

Core claim

The central claim is that NF and CF channels provide additional resolution in the distance domain, and that this distance resolution is the key factor driving beam management design differences across propagation regimes. Whereas FF beams align along an angular direction, NF and CF beams focus at a specific position, meaning angle and distance are jointly resolved. This distinction propagates through every stage of beam management: codebooks must become polar or distance-aware, channel estimation must exploit spherical-domain sparsity, beamforming can separate users that share an angle but sit at different distances, and tracking must represent user position and velocity rather than just direction.

Load-bearing premise

The argument leans on the assumption that, in the hybrid spherical- and planar-wave model, different subarrays share the same reflectors and path-gain amplitudes and differ only in phase and the angles of departure and arrival; if real terahertz cross-field channels do not have this sharing structure, the cross-field channel representation and the beam-management conclusions built on it weaken.

Editorial extensions

If this is right

  • Beam scanning codebooks for NF and CF links must cover both angle and distance, using polar-domain or hierarchical designs rather than angular-only far-field codebooks.
  • Channel estimation in NF and CF must exploit spherical-domain or polar-domain sparsity; far-field angular-sparsity assumptions degrade as the Rayleigh distance grows.
  • Beamforming with NF focusing beams can serve users at the same angle but different distances, which far-field steering beams cannot do without sacrificing signal power to one user.
  • Beam tracking in NF and CF should represent the state with user position and velocity (curvature of arrival) rather than only angles and path gain.
  • Extending far-field techniques to near-field and cross-field scenarios is possible but increases complexity, motivating unified, field-agnostic, low-overhead beam management strategies.

Reading between the lines

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

  • Editorial: if the HSPM is validated by measurement, it could become a standard low-complexity cross-field channel model for terahertz system design, a step the paper points toward but does not itself propose.
  • Editorial: the distance-resolution view suggests a testable benchmark: compare exhaustive, hierarchical, and ML-based scanning over a continuous range of distances crossing the Rayleigh boundary, rather than evaluating isolated FF and NF cases.
  • Editorial: co-directional users separated in range could be served simultaneously with focusing beams, turning distance resolution into a multiplexing resource in dense indoor terahertz hotspots.
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

0 major / 10 minor

Summary. This manuscript is a survey of beam management (BM) for millimeter-wave and terahertz massive MIMO, organized around propagation-field regimes. It introduces the spherical-wave (SWM), planar-wave (PWM), and hybrid spherical/planar-wave (HSPM) channel models, discusses their parameter counts and approximation errors, derives the corresponding beam patterns, and argues that near-field (NF) and cross-field (CF) beams provide resolution in both angle and distance, while far-field (FF) beams resolve angle only. The remainder of the paper reviews beam scanning, CSI estimation, beamforming, and beam tracking, classifying each by methodology and comparing how the techniques operate in FF, NF, and CF settings. The concluding claim is that the additional distance-domain resolution of NF/CF channels is a key factor that differentiates BM design across regimes.

Significance. The survey's central claim is well grounded. The exact SWM model in Section II-A1 and the beam-pattern expressions (18) and (24) show explicitly that NF/CF steering depends on distance as well as angle, and this is consistent with the cited near-field beam-focusing literature. The HSPM parameter-sharing assumption in Section II-A3 is acknowledged by the authors as awaiting measurement characterization in [25], and the main conclusion does not rest on that assumption. The taxonomy is useful, the comparison tables (Tables II–V) are a valuable reference, and the paper is unusually thorough in covering side-information-assisted and ML-based methods across all four BM functions. I found no internally inconsistent derivation that would undermine the paper's central qualitative message.

minor comments (10)
  1. [Section II-B, Fig. 5] The numerical values reported for the transition quantities (e.g., πd²LtsLrs/(λD11) ≈ 6.1×10⁻³ for 16 subarrays and πd²LtLr/(λD11) = 0.707 for Nt=Nr=1024 at 40 m) cannot be reproduced from the stated setting d=32λ under the definitions in Eq. (12); please reconcile the antenna-spacing parameter with the quoted numbers and report the full array and subarray geometry used in the simulations.
  2. [Section II-C, Eqs. (15)–(17)] The phase expression ∆Φ_i in Eq. (16) appears to have a unit or notation inconsistency with the extra factor d in e^{−j2πd/λ ∆Φ_i}; please clarify whether Γ and D are normalized by the antenna spacing or whether the factor d should be omitted.
  3. [Section II-A3] The claim that SWM and PWM are special cases of the HSPM at Kt=Nt, Kr=Nr and Kt=Kr=1 is correct in channel-matrix form, but the HSPM parameter count Np(1+5KrKt) does not reduce to the SWM parameter count 2NpNtNr in the former case; please clarify that the parameter-count comparison is intended for intermediate subarray partitions.
  4. [Section II-B] The error analysis is performed only for the line-of-sight path; the statement that the extension to NLoS paths follows a similar procedure should be accompanied by a brief explanation of how reflector-dependent angles enter the error, or should be explicitly marked as an open validation issue.
  5. [Abstract and Section I-C] The phrase 'building the basic through analyzing' should be rewritten as 'building the basics by analyzing'.
  6. [Fig. 2] The label 'Beam sacnning' should read 'Beam scanning'.
  7. [Section III, first paragraph] The phrase 'production of the codeword and the array response vector' should be 'product of the codeword and the array response vector'.
  8. [Section V-A] The phrase 'while strike achieving high spectral efficiency' should read 'while still achieving high spectral efficiency'.
  9. [Section V-C] The word 'catheterized' should be 'categorized'.
  10. [References] References [75] and [77] are the same Dovelos et al. paper, and references [161] and [162] appear to be the same conference paper; please merge the duplicates and renumber the citations accordingly.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey's central distance-resolution claim is derived from first-principles spherical-wave equations and the broader cited literature, not from fitted parameters or self-referential definitions.

full rationale

This paper is a survey rather than a derivation of a new quantitative result. Its central claim—that NF and CF channels offer additional resolution in the distance domain—is demonstrated directly from the channel models and beam-pattern equations in Sec. II: the PWM phase in Eq. (5) depends only on angles under the planar-wave approximation, while the SWM phase in Eqs. (1), (15)–(16) and the HSPM beam pattern in Eq. (24) contain explicit distance-dependent terms. The NF/CF beam-focusing illustrations in Fig. 6 are matched-filter constructions, so the location of maximum gain follows from the steering design, but this is standard array theory and is not presented as an empirical prediction. The HSPM in Sec. II-A3 is taken from the authors' prior work [22], yet the paper explicitly states that 'Detailed parameters in the HSPM need to be further characterized by channel measurement and analysis as studied in [25],' and Fig. 5 evaluates its accuracy against the ground-truth SWM. Thus the HSPM is used as a transparent modeling framework with stated assumptions, not as an unverified self-citation that forces the survey's conclusion. The remaining self-citations ([68], [86], [134], etc.) appear as examples in a literature review, which is normal and non-load-bearing. No fitted parameter is renamed as a prediction, no uniqueness theorem is imported from the authors' prior work, and no ansatz is smuggled in via citation. The paper's own limitation note about HSPM parameter characterization is a genuine caveat about future measurement work, not evidence of circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

This survey does not fit parameters to data and introduces no new physical entities. Its central conclusions rest on standard propagation-model assumptions and on the HSPM parameter-sharing hypothesis, which the paper acknowledges still requires measurement characterization.

assumptions (4)
  • domain assumption The planar-wave model is accurate when the normalized aperture-distance product pi d^2 Lt Lr / (lambda D) is much smaller than 1 and inaccurate when it approaches unity.
    Invoked in Section II-B to define far-field versus near-field applicability and to motivate why large arrays at terahertz frequencies require near-field and cross-field models. This is a standard engineering approximation cited to [13], [22], not proven in the paper.
  • domain assumption HSPM subarrays share common reflectors and equal path-gain amplitudes, with only direction-of-departure, direction-of-arrival, and phase differing across subarray pairs.
    State in Section II-A3. This parameter-sharing assumption gives the HSPM its low parameter count and underpins the cross-field channel representation. The paper states that measurement-based characterization is deferred to [25].
  • domain assumption High-frequency millimeter-wave and terahertz channels are sparse with at most Np = 10 significant paths.
    Used in Section II-A2 to justify why far-field and near-field sparse recovery methods are effective. This is standard in the cited literature, but the survey does not independently validate the sparsity bound.
  • domain assumption The Rayleigh distance 2 S^2 / lambda separates near-field and far-field propagation regions.
    Used throughout Section II to classify communication regimes and is attributed to [13]. The survey does not derive or re-examine this boundary.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Cross Far- and Near-Field Beam Management Technologies in Millimeter-Wave and Terahertz MIMO Systems." pith.science (2026). https://pith.science/paper/ODKDK3D4

@misc{pith2026250418855,
  author       = {Pith},
  title        = {Pith review of: Cross Far- and Near-Field Beam Management Technologies in Millimeter-Wave and Terahertz MIMO Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ODKDK3D4}},
  note         = {Machine review of arXiv:2504.18855}
}
read the original abstract

The evolution of wireless communication toward next-generation networks introduces unprecedented demands on data rates, latency, and connectivity. To meet these requirements, two key trends have emerged: the use of higher communication frequencies to provide broader bandwidth, and the deployment of massive multiple-input multiple-output systems with large antenna arrays to compensate for propagation losses and enhance spatial multiplexing. These advancements significantly extend the Rayleigh distance, enabling near-field (NF) propagation alongside the traditional far-field (FF) regime. As user communication distances dynamically span both FF and NF regions, cross-field (CF) communication has also emerged as a practical consideration. Beam management (BM)-including beam scanning, channel state information estimation, beamforming, and beam tracking-plays a central role in maintaining reliable directional communications. While most existing BM techniques are developed for FF channels, recent works begin to address the unique characteristics of NF and CF regimes. This survey presents a comprehensive review of BM techniques from the perspective of propagation fields. We begin by building the basic through analyzing the modeling of FF, NF, and CF channels, along with the associated beam patterns for alignment. Then, we categorize BM techniques by methodologies, and discuss their operational differences across propagation regimes, highlighting how field-dependent channel characteristics influence design tradeoffs and implementation complexity. In addition, for each BM method, we identify open challenges and future research directions, including extending FF methods to NF or CF scenarios, developing unified BM strategies for field-agnostic deployment, and designing low-overhead BM solutions for dynamic environments.

Figures

Figures reproduced from arXiv: 2504.18855 by the authors.

Figure 1
Figure 1. Illustration of users in different propagation regions. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Illustration of two representative beam management procedures: (a) beam estimation followed by beam alignment, and [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Organization of this paper machine learning (ML)-based, and side information-assisted methods. Potential research directions for beam scanning are also discussed. Sec. IV focuses on CSI estimation, dividing the discussion into beam estimation and channel estimation approaches. Their respective advantages and research oppor￾tunities in different field regimes are also summarized. Sec. V discusses beamforming design f… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Channel model in 2D planar array systems. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Errors of PWM and HSPM versus communication [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Beamforming gain of the PWM, SWM, and HSPM-based beams. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: FC hybrid beamforming architecture. Analog precoder RF chain DAC RF chain DAC Digital baseband [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: AoSA hybrid beamforming architecture. due to the larger number of phase shifters and interconnec￾tions. In contrast, the AoSA structure is more power-efficient because it requires fewer hardware components, such as phase shifters and combiners. However, this reduction …
Figure 9
Figure 9. Figure 9: DAoSA hybrid beamforming architecture. Analog precoder RF chain DAC RF chain DAC RF chain DAC RF chain DAC Digital baseband Widely-spaced subarrays [PITH_FULL_IMAGE:figures/full_fig_p018_9.png]
Figure 10
Figure 10. Figure 10: WSMS hybrid beamforming architecture. Second, the FC, AoSA, and DAoSA structures have limited spatial multiplexing capabilities due to the sparse nature of high-frequency channels, especially in the THz band [30], [104]. These architectures assume an antenna spacing o…
Figure 11
Figure 11. Figure 11: Normalized power with PWM, SWM, and HSPM-based beamforming for users at the same direction. [PITH_FULL_IMAGE:figures/full_fig_p020_11.png]
Figure 12
Figure 12. Figure 12: Spectral efficiency with beam focusing and beam [PITH_FULL_IMAGE:figures/full_fig_p021_12.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

180 extracted references · 74 canonical work pages

  1. [25]

    Cross Far- and Near- Field Channel Measurement and Modeling in Extremely Large-scale Movable Antenna Array Systems,

    Y .-Q. Wang, C. Han, S. Sun, and J. Zhang, “Cross Far- and Near- Field Channel Measurement and Modeling in Extremely Large-scale Movable Antenna Array Systems,” IEEE Transactions on Antennas and Propagation, to appear 2025

    2025

  2. [68]

    Can Far-Field Beam Training Be Deployed for Cross-Field Beam Alignment in Terahertz UM-MIMO Communications?

    Y . Chen, C. Han, and E. Björnson, “Can Far-Field Beam Training Be Deployed for Cross-Field Beam Alignment in Terahertz UM-MIMO Communications?” IEEE Transactions on Wireless Communications , vol. 23, no. 10, pp. 14 972–14 987, 2024

    2024

  3. [121]

    Beam Focusing for Near-Field Multiuser MIMO Communica- tions,

    H. Zhang, N. Shlezinger, F. Guidi, D. Dardari, M. F. Imani, and Y . C. Eldar, “Beam Focusing for Near-Field Multiuser MIMO Communica- tions,” IEEE Transactions on Wireless Communications , vol. 21, no. 9, pp. 7476–7490, 2022

    work page 2022

  4. [1]

    6G Wireless Communication Systems: Applications, Requirements, Tech- nologies, Challenges, and Research Directions,

    M. Z. Chowdhury, M. Shahjalal, S. Ahmed, and Y . M. Jang, “6G Wireless Communication Systems: Applications, Requirements, Tech- nologies, Challenges, and Research Directions,” IEEE Open Journal of the Communications Society , vol. 1, pp. 957–975, 2020

    2020

  5. [2]

    6G Wireless Communications: Vision and Potential Techniques,

    P. Yang, Y . Xiao, M. Xiao, and S. Li, “6G Wireless Communications: Vision and Potential Techniques,” IEEE Network , vol. 33, no. 4, pp. 70–75, 2019

    2019

  6. [3]

    Towards 6G: Evolution of Key Performance Indicators and Technology Trends,

    A. Mourad, R. Yang, P. H. Lehne, and A. de la Oliva, “Towards 6G: Evolution of Key Performance Indicators and Technology Trends,” in Proc. of 6G Wireless Summit , 2020, pp. 1–5. 27

    2020

  7. [4]

    New Mid-Band for 6G: Several Considerations from the Channel Propagation Characteristics Perspective,

    J. Zhang, H. Miao, P. Tang, L. Tian, and G. Liu, “New Mid-Band for 6G: Several Considerations from the Channel Propagation Characteristics Perspective,” IEEE Communications Magazine, vol. 63, no. 1, pp. 175– 180, 2025

    2025

  8. [5]

    Millimeter- wave and terahertz spectrum for 6G wireless,

    S. Tripathi, N. V . Sabu, A. K. Gupta, and H. S. Dhillon, “Millimeter- wave and terahertz spectrum for 6G wireless,” in 6G Mobile Wireless Networks. Springer, 2021, pp. 83–121

    2021

Show all 180 references
  1. [6]

    Terahertz communications for 6g and beyond wireless networks: Chal- lenges, key advancements, and opportunities,

    A. Shafie, N. Yang, C. Han, J. M. Jornet, M. Juntti, and T. Kürner, “Terahertz communications for 6g and beyond wireless networks: Chal- lenges, key advancements, and opportunities,” IEEE Network , vol. 37, no. 3, pp. 162–169, 2023

    2023

  2. [7]

    Terahertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade,

    I. F. Akyildiz, C. Han, Z. Hu, S. Nie, and J. M. Jornet, “Terahertz Band Communication: An Old Problem Revisited and Research Directions for the Next Decade,” IEEE Trans. Commun., vol. 70, no. 6, pp. 4250–4285, May 2022

    2022

  3. [8]

    The Role of Millimeter- Wave Technologies in 5G/6G Wireless Communications,

    W. Hong, Z. H. Jiang, C. Yu, D. Hou, H. Wang, C. Guo, Y . Hu, L. Kuai, Y . Yu, Z. Jiang, Z. Chen, J. Chen, Z. Yu, J. Zhai, N. Zhang, L. Tian, F. Wu, G. Yang, Z.-C. Hao, and J. Y . Zhou, “The Role of Millimeter- Wave Technologies in 5G/6G Wireless Communications,”IEEE Journal o...

    2021

  4. [9]

    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 Communications Surveys & Tutorials , vol. 26, no. 3, pp. 1560– 1605, 2024

    2024

  5. [10]

    Towards 6G MIMO: Massive Spatial Multiplexing, Dense Arrays, and Interplay Between Electromagnetics and Processing,

    E. Björnson, C.-B. Chae, R. W. Heath Jr, T. L. Marzetta, A. Mezghani, L. Sanguinetti, F. Rusek, M. R. Castellanos, D. Jun, and Ö. T. Demir, “Towards 6G MIMO: Massive Spatial Multiplexing, Dense Arrays, and Interplay Between Electromagnetics and Processing,” arXiv preprint arXi...

    2024 arXiv

  6. [11]

    Toward 6G Networks: Use Cases and Technologies,

    M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, “Toward 6G Networks: Use Cases and Technologies,” IEEE Commu- nications Magazine, vol. 58, no. 3, pp. 55–61, 2020

    2020

  7. [12]

    Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research,

    C. D. Alwis, A. Kalla, Q.-V . Pham, P. Kumar, K. Dev, W.-J. Hwang, and M. Liyanage, “Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research,” IEEE Open Journal of the Communications Society , vol. 2, pp. 836–886, 2021

    2021

  8. [13]

    How to Differentiate Between Near Field and Far Field: Revisiting the Rayleigh Distance,

    S. Sun, R. Li, C. Han, X. Liu, L. Xue, and M. Tao, “How to Differentiate Between Near Field and Far Field: Revisiting the Rayleigh Distance,” IEEE Communications Magazine , vol. 63, no. 1, pp. 22–28, 2025

    2025

  9. [14]

    Near-Field MIMO Communications for 6G: Fundamentals, Challenges, Potentials, and Future Directions,

    M. Cui, Z. Wu, Y . Lu, X. Wei, and L. Dai, “Near-Field MIMO Communications for 6G: Fundamentals, Challenges, Potentials, and Future Directions,” IEEE Communications Magazine , vol. 61, no. 1, pp. 40–46, 2023

    2023

  10. [15]

    A Tutorial on Near- Field XL-MIMO Communications Toward 6G,

    H. Lu, Y . Zeng, C. You, Y . Han, J. Zhang, Z. Wang, Z. Dong, S. Jin, C.-X. Wang, T. Jiang, X. You, and R. Zhang, “A Tutorial on Near- Field XL-MIMO Communications Toward 6G,” IEEE Communications Surveys & Tutorials, vol. 26, no. 4, pp. 2213–2257, 2024

    2024

  11. [16]

    Near- Field Communications: A Tutorial Review,

    Y . Liu, Z. Wang, J. Xu, C. Ouyang, X. Mu, and R. Schober, “Near- Field Communications: A Tutorial Review,” IEEE Open Journal of the Communications Society, vol. 4, pp. 1999–2049, 2023

    1999

  12. [17]

    Cross Far- and Near- Field Wireless Communications in Terahertz Ultra-Large Antenna Array Systems,

    C. Han, Y . Chen, L. Yan, Z. Chen, and L. Dai, “Cross Far- and Near- Field Wireless Communications in Terahertz Ultra-Large Antenna Array Systems,” IEEE Wireless Communications, vol. 31, no. 3, pp. 148–154, 2024

    2024

  13. [18]

    Hybrid Near-Field and Far-Field XL-MIMO: How Many Users Can Be Supported?

    Y . Zhang, Z. Yang, S. Yue, L. Liu, and B. Di, “Hybrid Near-Field and Far-Field XL-MIMO: How Many Users Can Be Supported?” IEEE Communications Letters, vol. 28, no. 10, pp. 2402–2406, 2024

    2024

  14. [19]

    A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies,

    M. Giordani, M. Polese, A. Roy, D. Castor, and M. Zorzi, “A Tutorial on Beam Management for 3GPP NR at mmWave Frequencies,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 173–196, 2019

    2019

  15. [20]

    On Spherical vs. Plane Wave Modeling of Line-of-sight MIMO Channels,

    F. Bohagen, P. Orten, and G. E. Oien, “On Spherical vs. Plane Wave Modeling of Line-of-sight MIMO Channels,” IEEE Trans. Commun. , vol. 57, no. 3, pp. 841–849, 2009

    2009

  16. [21]

    Recent Research on Massive MIMO Propagation Channels: A Survey,

    P. Zhang, J. Chen, X. Yang, N. Ma, and Z. Zhang, “Recent Research on Massive MIMO Propagation Channels: A Survey,” IEEE Commun. Mag., vol. 56, no. 12, pp. 22–29, 2018

    2018

  17. [22]

    Hybrid Spherical- and Planar-Wave Modeling and DCNN-Powered Estimation of Terahertz Ultra-Massive MIMO Channels,

    Y . Chen, L. Yan, and C. Han, “Hybrid Spherical- and Planar-Wave Modeling and DCNN-Powered Estimation of Terahertz Ultra-Massive MIMO Channels,” IEEE Transactions on Communications , vol. 69, no. 10, pp. 7063–7076, 2021

    2021

  18. [23]

    Two-Level Spatial Multiplexing Using Hybrid Beamforming for Millimeter-Wave Backhaul,

    X. Song, W. Rave, N. Babu, S. Majhi, and G. Fettweis, “Two-Level Spatial Multiplexing Using Hybrid Beamforming for Millimeter-Wave Backhaul,” IEEE Trans. Wireless Commun. , vol. 17, no. 7, pp. 4830– 4844, 2018

    2018

  19. [24]

    A Dynamic Array-of-Subarrays Ar- chitecture and Hybrid Precoding Algorithms for Terahertz Wireless Communications,

    L. Yan, C. Han, and J. Yuan, “A Dynamic Array-of-Subarrays Ar- chitecture and Hybrid Precoding Algorithms for Terahertz Wireless Communications,” IEEE J. Sel. Areas Commun. , vol. 38, no. 9, pp. 2041–2056, 2020

    2020

  20. [26]

    Wavefront Engineering: Realizing Efficient Terahertz Band Communications in 6G and Beyond,

    A. Singh, V . Petrov, H. Guerboukha, I. V . Reddy, E. W. Knightly, D. M. Mittleman, and J. M. Jornet, “Wavefront Engineering: Realizing Efficient Terahertz Band Communications in 6G and Beyond,” IEEE Wireless Communications, vol. 31, no. 3, pp. 133–139, 2024

    2024

  21. [27]

    Subarray-based Coordinated Beam- forming Training for mmWave and sub-THz Communications,

    C. Lin, G. Y . Li, and L. Wang, “Subarray-based Coordinated Beam- forming Training for mmWave and sub-THz Communications,” IEEE J. Sel. Areas Commun. , vol. 35, no. 9, pp. 2115–2126, 2017

    2017

  22. [28]

    Three-Dimensional End-to-End Modeling and Analysis for Graphene-Enabled Terahertz Band Communications,

    C. Han and I. F. Akyildiz, “Three-Dimensional End-to-End Modeling and Analysis for Graphene-Enabled Terahertz Band Communications,” IEEE Trans. Veh. Technol., vol. 66, no. 7, pp. 5626–5634, 2017

    2017

  23. [29]

    On Millimeter Wave and THz Mobile Radio Channel for Smart Rail Mobility,

    K. Guan, G. Li, T. Kürner, A. F. Molisch, B. Peng, R. He, B. Hui, J. Kim, and Z. Zhong, “On Millimeter Wave and THz Mobile Radio Channel for Smart Rail Mobility,” IEEE Trans. Veh. Technol., vol. 66, no. 7, pp. 5658–5674, 2017

    2017

  24. [30]

    Joint Inter-path and Intra- path Multiplexing for Terahertz Widely-spaced Multi-subarray Hybrid Beamforming Systems,

    L. Yan, Y . Chen, C. Han, and J. Yuan, “Joint Inter-path and Intra- path Multiplexing for Terahertz Widely-spaced Multi-subarray Hybrid Beamforming Systems,” IEEE Trans. Commun., vol. 70, no. 2, pp. 1407– 1422, 2022

    2022

  25. [31]

    Channel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?

    X. Wei and L. Dai, “Channel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?” IEEE Com- munications Letters, vol. 26, no. 1, pp. 177–181, 2022

    2022

  26. [32]

    Hybrid-Field Channel Estimation for Extremely Large-Scale Massive MIMO System,

    Z. Hu, C. Chen, Y . Jin, L. Zhou, and Q. Wei, “Hybrid-Field Channel Estimation for Extremely Large-Scale Massive MIMO System,” IEEE Communications Letters, vol. 27, no. 1, pp. 303–307, 2023

    2023

  27. [33]

    1– 200, Oct

    “IEEE Standard for Information Technology–Local and Metropoli- tan Area Networks–Specific Requirements–Part 15.3: Amendment 2: Millimeter-wave-based Alternative Physical Layer Extension,” pp. 1– 200, Oct. 2009, iEEE Std. 802.15.3c-2009

    2009

  28. [34]

    A Unified 3D Beam Training and Tracking Procedure for Terahertz Communication,

    B. Ning, Z. Chen, Z. Tian, C. Han, and S. Li, “A Unified 3D Beam Training and Tracking Procedure for Terahertz Communication,” IEEE Trans. Wireless Commun., vol. 21, no. 4, pp. 2445–2461, April 2022

    2022

  29. [35]

    6G Wireless Communications: From Far-Field Beam Steering to Near-Field Beam Focusing,

    H. Zhang, N. Shlezinger, F. Guidi, D. Dardari, and Y . C. Eldar, “6G Wireless Communications: From Far-Field Beam Steering to Near-Field Beam Focusing,” IEEE Communications Magazine , vol. 61, no. 4, pp. 72–77, 2023

    2023

  30. [36]

    Codebook design and beam training for extremely large-scale RIS: Far-field or near-field?

    X. Wei, L. Dai, Y . Zhao, G. Yu, and X. Duan, “Codebook design and beam training for extremely large-scale RIS: Far-field or near-field?” China Commun., vol. 19, no. 6, pp. 193–204, Jun. 2022

    2022

  31. [37]

    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 Transactions on Communica- tions, vol. 70, no. 4, pp. 2663–2677, 2022

    2022

  32. [38]

    Near-Field Beam Training in THz Communications: The Merits of Uniform Circular Array,

    Y . Xie, B. Ning, L. Li, and Z. Chen, “Near-Field Beam Training in THz Communications: The Merits of Uniform Circular Array,” IEEE Wireless Commun. Lett., vol. 12, no. 4, pp. 575–579, April 2023

    2023

  33. [39]

    Hybrid Beamforming for Terahertz Wire- less Communications: Challenges, Architectures, and Open Problems,

    C. Han, L. Yan, and J. Yuan, “Hybrid Beamforming for Terahertz Wire- less Communications: Challenges, Architectures, and Open Problems,” IEEE Wireless Commun., vol. 28, no. 4, pp. 198–204, Aug. 2021

    2021

  34. [40]

    Codebook design for beam alignment in millimeter wave communication systems,

    J. Zhang, Y . Huang, Q. Shi, J. Wang, and L. Yang, “Codebook design for beam alignment in millimeter wave communication systems,” IEEE Transactions on Communications, vol. 65, no. 11, pp. 4980–4995, 2017

    2017

  35. [41]

    Common Codebook Millimeter Wave Beam Design: Designing Beams for Both Sounding and Communication With Uniform Planar Arrays,

    J. Song, J. Choi, and D. J. Love, “Common Codebook Millimeter Wave Beam Design: Designing Beams for Both Sounding and Communication With Uniform Planar Arrays,” IEEE Transactions on Communications , vol. 65, no. 4, pp. 1859–1872, 2017

    2017

  36. [42]

    Hierarchical codebook design for beamforming training in millimeter-wave communication,

    Z. Xiao, T. He, P. Xia, and X.-G. Xia, “Hierarchical codebook design for beamforming training in millimeter-wave communication,” IEEE Transactions on Wireless Communications , vol. 15, no. 5, pp. 3380– 3392, 2016

    2016

  37. [43]

    Hierarchical Beam Training for Extremely Large-Scale MIMO: From Far-Field to Near-Field,

    Y . Lu, Z. Zhang, and L. Dai, “Hierarchical Beam Training for Extremely Large-Scale MIMO: From Far-Field to Near-Field,” IEEE Transactions on Communications, vol. 72, no. 4, pp. 2247–2259, 2024

    2024

  38. [44]

    Codebook Design for Extremely Large-Scale MIMO Systems: Near-Field and Far- 28 Field,

    X. Zhang, H. Zhang, J. Zhang, C. Li, Y . Huang, and L. Yang, “Codebook Design for Extremely Large-Scale MIMO Systems: Near-Field and Far- 28 Field,” IEEE Transactions on Communications, vol. 72, no. 2, pp. 1191– 1206, 2024

    2024

  39. [45]

    Two-stage hierarchical beam training for near-field communications,

    C. Wu, C. You, Y . Liu, L. Chen, and S. Shi, “Two-stage hierarchical beam training for near-field communications,” IEEE Transactions on Vehicular Technology, vol. 73, no. 2, pp. 2032–2044, 2024

    2024

  40. [46]

    Fast Near-Field Beam Training for Extremely Large-Scale Array,

    Y . Zhang, X. Wu, and C. You, “Fast Near-Field Beam Training for Extremely Large-Scale Array,” IEEE Wireless Commun. Lett. , vol. 11, no. 12, pp. 2625–2629, Dec. 2022

    2022

  41. [47]

    Near-field Beam Training with Sparse DFT Codebook,

    C. Zhou, C. Wu, C. You, J. Zhou, and S. Shi, “Near-field Beam Training with Sparse DFT Codebook,” IEEE Transactions on Communications , pp. 1–1, to appear 2024

    2024

  42. [48]

    Near-Field Beam Training With Hierarchical Codebook: Two-Stage Learning-Based Approach,

    C. Weng, X. Guo, and Y . Wang, “Near-Field Beam Training With Hierarchical Codebook: Two-Stage Learning-Based Approach,” IEEE Transactions on Vehicular Technology, vol. 73, no. 9, pp. 14 003–14 008, 2024

    2024

  43. [49]

    Spatial-chirp codebook-based hierarchical beam training for extremely large-scale massive mimo,

    X. Shi, J. Wang, Z. Sun, and J. Song, “Spatial-chirp codebook-based hierarchical beam training for extremely large-scale massive mimo,” IEEE Transactions on Wireless Communications , vol. 23, no. 4, pp. 2824–2838, 2024

    2024

  44. [50]

    Efficient Hybrid Near- and Far-Field Beam Training for XL-MIMO Communications,

    J. Luo, J. Fan, K. Xie, and X. Shi, “Efficient Hybrid Near- and Far-Field Beam Training for XL-MIMO Communications,” IEEE Transactions on Vehicular Technology, vol. 73, no. 12, pp. 19 785–19 790, 2024

    2024

  45. [51]

    Triple-Refined Hybrid-Field Beam Training for mmWave Extremely Large-Scale MIMO,

    K. Chen, C. Qi, O. A. Dobre, and G. Ye Li, “Triple-Refined Hybrid-Field Beam Training for mmWave Extremely Large-Scale MIMO,” IEEE Transactions on Wireless Communications , vol. 23, no. 8, pp. 8556– 8570, 2024

    2024

  46. [52]

    Wideband beam tracking in thz massive mimo systems,

    J. Tan and L. Dai, “Wideband beam tracking in thz massive mimo systems,” IEEE J. Sel. Areas Commun. , vol. 39, no. 6, pp. 1693–1710, 2021

    2021

  47. [53]

    Near-Field Rainbow: Wideband Beam Training for XL-MIMO,

    M. Cui, L. Dai, Z. Wang, S. Zhou, and N. Ge, “Near-Field Rainbow: Wideband Beam Training for XL-MIMO,” IEEE Transactions on Wire- less Communications, vol. 22, no. 6, pp. 3899–3912, 2023

    2023

  48. [54]

    A Codebook Based Simul- taneous Beam Training for mmWave Multi-User MIMO Systems with Split Structures,

    R. Zhang, H. Zhang, W. Xu, and C. Zhao, “A Codebook Based Simul- taneous Beam Training for mmWave Multi-User MIMO Systems with Split Structures,” in Proc. of IEEE Global Communications Conference (GLOBECOM), 2018, pp. 1–6

    2018

  49. [55]

    Hierarchical Codebook- Based Multiuser Beam Training for Millimeter Wave Massive MIMO,

    C. Qi, K. Chen, O. A. Dobre, and G. Y . Li, “Hierarchical Codebook- Based Multiuser Beam Training for Millimeter Wave Massive MIMO,” IEEE Transactions on Wireless Communications , vol. 19, no. 12, pp. 8142–8152, 2020

    2020

  50. [56]

    Fast Hybrid Far/Near-Field Beam Training For Extremely Large-Scale Millimeter Wave/Terahertz Systems,

    H. Wang, J. Fang, H. Duan, and H. Li, “Fast Hybrid Far/Near-Field Beam Training For Extremely Large-Scale Millimeter Wave/Terahertz Systems,” IEEE Transactions on Communications , to appear 2024

    2024

  51. [57]

    Coded Beam Training,

    T. Zheng, J. Zhu, Q. Yu, Y . Yan, and L. Dai, “Coded Beam Training,” IEEE Journal on Selected Areas in Communications , vol. 43, no. 3, pp. 928–943, 2025

    2025

  52. [58]

    Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems,

    Y . Zhang, M. Alrabeiah, and A. Alkhateeb, “Reinforcement Learning of Beam Codebooks in Millimeter Wave and Terahertz MIMO Systems,” IEEE Trans. Commun. , vol. 70, no. 2, pp. 904–919, Feb. 2022

    2022

  53. [59]

    Neural Codebook Design for MIMO Network Beam Management,

    R. M. Dreifuerst and R. W. Heath, “Neural Codebook Design for MIMO Network Beam Management,” IEEE Transactions on Wireless Communications, pp. 1–1, to appear 2025

    2025

  54. [60]

    Efficient Initial Access Based on DRL-Empowered Beam Sweeping,

    J. Che, Z. Zhang, Y . Yang, and Z. Yang, “Efficient Initial Access Based on DRL-Empowered Beam Sweeping,” IEEE Transactions on Wireless Communications, to appear 2025

    2025

  55. [61]

    Scenario-adaptive meta-learning for mmwave beam alignment,

    Z. Xu, S. Wang, and Y .-J. A. Zhang, “Scenario-adaptive meta-learning for mmwave beam alignment,” IEEE Transactions on Wireless Commu- nications, to appear 2025

    2025

  56. [62]

    Near-Field Beam Training Based on Deep Learning for Extremely Large-Scale MIMO,

    G. Jiang and C. Qi, “Near-Field Beam Training Based on Deep Learning for Extremely Large-Scale MIMO,” IEEE Communications Letters , vol. 27, no. 8, pp. 2063–2067, 2023

    2023

  57. [63]

    Side Angle Information Assisted Near-Field Beam Training for XL-Array Communications,

    L. Liu, C. You, Y . Zhang, and T. Liu, “Side Angle Information Assisted Near-Field Beam Training for XL-Array Communications,” IEEE Communications Letters , vol. 28, no. 9, pp. 2201–2205, 2024

    2024

  58. [64]

    Toward intelligent millimeter and terahertz communication for 6g: Computer vision-aided beamforming,

    Y . Ahn, J. Kim, S. Kim, K. Shim, J. Kim, S. Kim, and B. Shim, “Toward intelligent millimeter and terahertz communication for 6g: Computer vision-aided beamforming,” IEEE Wireless Commun. , vol. 30, no. 5, pp. 179–186, Oct. 2023

    2023

  59. [65]

    Fast Beam Training and Alignment for IRS-Assisted Millimeter Wave/Terahertz Systems,

    P. Wang, J. Fang, W. Zhang, and H. Li, “Fast Beam Training and Alignment for IRS-Assisted Millimeter Wave/Terahertz Systems,” IEEE Transactions on Wireless Communications , vol. 21, no. 4, pp. 2710– 2724, 2022

    2022

  60. [66]

    Low-Complexity Deep-Learning- Based DOA Estimation for Hybrid Massive MIMO Systems With Uni- form Circular Arrays,

    D. Hu, Y . Zhang, L. He, and J. Wu, “Low-Complexity Deep-Learning- Based DOA Estimation for Hybrid Massive MIMO Systems With Uni- form Circular Arrays,” IEEE Wireless Communications Letters , vol. 9, no. 1, pp. 83–86, 2020

    2020

  61. [67]

    Millidegree-Level Direction- of-Arrival Estimation and Tracking for Terahertz Ultra-Massive MIMO Systems,

    Y . Chen, L. Yan, C. Han, and M. Tao, “Millidegree-Level Direction- of-Arrival Estimation and Tracking for Terahertz Ultra-Massive MIMO Systems,” IEEE Transactions on Wireless Communications , vol. 21, no. 2, pp. 869–883, 2022

    2022

  62. [69]

    Deep Learning Based Beam Training for Extremely Large-Scale Massive MIMO in Near-Field Domain,

    W. Liu, H. Ren, C. Pan, and J. Wang, “Deep Learning Based Beam Training for Extremely Large-Scale Massive MIMO in Near-Field Domain,” IEEE Communications Letters , vol. 27, no. 1, pp. 170–174, 2023

    2023

  63. [70]

    Near-Field Mul- tiuser Beam-Training for Extremely Large-Scale MIMO Systems,

    W. Liu, C. Pan, H. Ren, J. Wang, and R. Schober, “Near-Field Mul- tiuser Beam-Training for Extremely Large-Scale MIMO Systems,”IEEE Transactions on Communications , pp. 1–1, 2024

    2024

  64. [71]

    Near-Field Beam Training: Joint Angle and Range Estimation With DFT Codebook,

    X. Wu, C. You, J. Li, and Y . Zhang, “Near-Field Beam Training: Joint Angle and Range Estimation With DFT Codebook,” IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 11 890–11 903, 2024

    2024

  65. [72]

    Low-Overhead Beam Training Scheme for Extremely Large-Scale RIS in Near Field,

    W. Liu, C. Pan, H. Ren, F. Shu, S. Jin, and J. Wang, “Low-Overhead Beam Training Scheme for Extremely Large-Scale RIS in Near Field,” IEEE Transactions on Communications , vol. 71, no. 8, pp. 4924–4940, 2023

    2023

  66. [73]

    Channel Esti- mation and Hybrid Precoding for Millimeter Wave Cellular Systems,

    A. Alkhateeb, O. El Ayach, G. Leus, and R. W. Heath, “Channel Esti- mation and Hybrid Precoding for Millimeter Wave Cellular Systems,” IEEE Journal of Selected Topics in Signal Processing , vol. 8, no. 5, pp. 831–846, 2014

    2014

  67. [74]

    Hybrid MIMO Architectures for Millimeter Wave Communica- tions: Phase Shifters or Switches?

    R. Méndez-Rial, C. Rusu, N. González-Prelcic, A. Alkhateeb, and R. W. Heath, “Hybrid MIMO Architectures for Millimeter Wave Communica- tions: Phase Shifters or Switches?” IEEE Access, vol. 4, pp. 247–267, 2016

    2016

  68. [76]

    Information- Theoretically Optimal Compressed Sensing via Spatial Coupling and Approximate Message Passing,

    D. L. Donoho, A. Javanmard, and A. Montanari, “Information- Theoretically Optimal Compressed Sensing via Spatial Coupling and Approximate Message Passing,” IEEE Transactions on Information Theory, vol. 59, no. 11, pp. 7434–7464, Nov. 2013

    2013

  69. [77]

    Channel Estimation and Hybrid Combining for Wideband Terahertz Massive MIMO Systems,

    K. Dovelos, M. Matthaiou, H. Q. Ngo, and B. Bellalta, “Channel Estimation and Hybrid Combining for Wideband Terahertz Massive MIMO Systems,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 6, pp. 1604–1620, June 2021

    2021

  70. [78]

    Bsa-omp: Beam-split-aware orthogonal matching pursuit for thz channel estimation,

    A. M. Elbir and S. Chatzinotas, “Bsa-omp: Beam-split-aware orthogonal matching pursuit for thz channel estimation,” IEEE Wireless Communi- cations Letter, vol. 12, no. 4, pp. 738–742, April 2023

    2023

  71. [79]

    Terahertz Massive MIMO With Holographic Reconfigurable Intelligent Surfaces,

    Z. Wan, Z. Gao, F. Gao, M. D. Renzo, and M.-S. Alouini, “Terahertz Massive MIMO With Holographic Reconfigurable Intelligent Surfaces,” IEEE Transations on Communications , vol. 69, no. 7, pp. 4732–4750, Jul. 2021

    2021

  72. [80]

    TRICE: A Channel Estimation Framework for RIS-Aided Millimeter- Wave MIMO Systems,

    K. Ardah, S. Gherekhloo, A. L. F. de Almeida, and M. Haardt, “TRICE: A Channel Estimation Framework for RIS-Aided Millimeter- Wave MIMO Systems,” IEEE Signal Processing Letter , vol. 28, pp. 513–517, Feb. 2021

    2021

  73. [81]

    Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems,

    P. Wang, J. Fang, H. Duan, and H. Li, “Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems,” IEEE Signal Processing Letter , vol. 27, pp. 905–909, May 2020

    2020

  74. [82]

    Joint Channel Estimation and Data Rate Maximization for Intelligent Reflect- ing Surface Assisted Terahertz MIMO Communication Systems,

    X. Ma, Z. Chen, W. Chen, Z. Li, Y . Chi, C. Han, and S. Li, “Joint Channel Estimation and Data Rate Maximization for Intelligent Reflect- ing Surface Assisted Terahertz MIMO Communication Systems,” IEEE Access, vol. 8, pp. 99 565–99 581, 2020

    2020

  75. [83]

    Channel Estimation for IRS-Assisted Millimeter-Wave MIMO Systems: Sparsity-Inspired Ap- proaches,

    T. Lin, X. Yu, Y . Zhu, and R. Schober, “Channel Estimation for IRS-Assisted Millimeter-Wave MIMO Systems: Sparsity-Inspired Ap- proaches,” IEEE Transactions on Communications , vol. 70, no. 6, pp. 4078–4092, April 2022

    2022

  76. [84]

    Triple-Structured Compressive Sensing- Based Channel Estimation for RIS-Aided MU-MIMO Systems,

    X. Shi, J. Wang, and J. Song, “Triple-Structured Compressive Sensing- Based Channel Estimation for RIS-Aided MU-MIMO Systems,” IEEE Transactions on Wireless Communications, vol. 21, no. 12, pp. 11 095– 11 109, Dec. 2022. 29

    2022

  77. [85]

    Near- Field Channel Estimation for Extremely Large-Scale Reconfigurable In- telligent Surface (XL-RIS)-Aided Wideband mmWave Systems,

    S. Yang, C. Xie, W. Lyu, B. Ning, Z. Zhang, and C. Yuen, “Near- Field Channel Estimation for Extremely Large-Scale Reconfigurable In- telligent Surface (XL-RIS)-Aided Wideband mmWave Systems,” IEEE Journal on Selected Areas in Communications, vol. 42, no. 6, pp. 1567– 1582, 2024

    2024

  78. [86]

    Hybrid Spherical- and Planar-Wave Channel Modeling and Estimation for Terahertz Integrated UM-MIMO and IRS Systems,

    Y . Chen, R. Li, C. Han, S. Sun, and M. Tao, “Hybrid Spherical- and Planar-Wave Channel Modeling and Estimation for Terahertz Integrated UM-MIMO and IRS Systems,” IEEE Transactions on Wireless Commu- nications, vol. 22, no. 12, pp. 9746–9761, 2023

    2023

  79. [87]

    Hybrid Near-Far Field Channel Estimation for Holographic MIMO Communications,

    S. Yue, S. Zeng, L. Liu, Y . C. Eldar, and B. Di, “Hybrid Near-Far Field Channel Estimation for Holographic MIMO Communications,” IEEE Transactions on Wireless Communications, vol. 23, no. 11, pp. 15 798– 15 813, 2024

    2024

  80. [88]

    Cross-Field Channel Estimation for Ultra Massive-MIMO THz Systems,

    S. Tarboush, A. Ali, and T. Y . Al-Naffouri, “Cross-Field Channel Estimation for Ultra Massive-MIMO THz Systems,” IEEE Transactions on Wireless Communications, vol. 23, no. 8, pp. 8619–8635, 2024

    2024

  81. [89]

    Super-Resolution Channel Estimation for MmWave Massive MIMO With Hybrid Precoding,

    C. Hu, L. Dai, T. Mir, Z. Gao, and J. Fang, “Super-Resolution Channel Estimation for MmWave Massive MIMO With Hybrid Precoding,”IEEE Transactions on Vehicular Technology , vol. 67, no. 9, pp. 8954–8958, Sept. 2018

    2018

  82. [90]

    Massive MIMO Channel Estimation Over the mmWave Systems Through Parameters Learning,

    W. Shao, S. Zhang, X. Zhang, J. Ma, N. Zhao, and V . C. M. Le- ung, “Massive MIMO Channel Estimation Over the mmWave Systems Through Parameters Learning,” IEEE Communications Letters , vol. 23, no. 4, pp. 672–675, Apr. 2019

    2019

  83. [91]

    A Semi-Blind Channel Estimation Algorithm for Massive MIMO Systems,

    K. Mawatwal, D. Sen, and R. Roy, “A Semi-Blind Channel Estimation Algorithm for Massive MIMO Systems,” IEEE Wireless Communica- tions Letters, vol. 6, no. 1, pp. 70–73, Feb. 2017

    2017

  84. [92]

    Channel Estimation for RIS- Aided mmWave MIMO Systems via Atomic Norm Minimization,

    J. He, H. Wymeersch, and M. Juntti, “Channel Estimation for RIS- Aided mmWave MIMO Systems via Atomic Norm Minimization,”IEEE Transactions on Wireless Communications , vol. 20, no. 9, pp. 5786– 5797, Sept. 2021

    2021

  85. [93]

    Millimeter-wave Channel Estimation based on 2-D Beamspace MUSIC Method,

    Z. Guo, X. Wang, and W. Heng, “Millimeter-wave Channel Estimation based on 2-D Beamspace MUSIC Method,” IEEE Transactions on Wireless Communications, vol. 16, no. 8, pp. 5384–5394, Aug. 2017

    2017

  86. [94]

    2D Unitary ESPRIT Based Super-Resolution Channel Estimation for Millimeter- Wave Massive MIMO With Hybrid Precoding,

    A. Liao, Z. Gao, Y . Wu, H. Wang, and M. Alouini, “2D Unitary ESPRIT Based Super-Resolution Channel Estimation for Millimeter- Wave Massive MIMO With Hybrid Precoding,” IEEE Access , vol. 5, pp. 24 747–24 757, Nov. 2017

    2017

  87. [95]

    High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO Systems,

    W. Ma, C. Qi, and G. Y . Li, “High-Resolution Channel Estimation for Frequency-Selective mmWave Massive MIMO Systems,” IEEE Trans. Wireless Commun., vol. 19, no. 5, pp. 3517–3529, May 2020

    2020

  88. [96]

    Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems,

    H. He, C. Wen, S. Jin, and G. Y . Li, “Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems,” IEEE Wireless Communications Letters, vol. 7, no. 5, pp. 852–855, Oct. 2018

    2018

  89. [97]

    Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems,

    X. Wei, C. Hu, and L. Dai, “Deep Learning for Beamspace Channel Estimation in Millimeter-Wave Massive MIMO Systems,” IEEE Trans- actions on Communications , vol. 69, no. 1, pp. 182–193, Jan. 2021

    2021

  90. [98]

    Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks,

    W. Yu, Y . Shen, H. He, X. Yu, J. Zhang, and K. B. Letaief, “Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks,” in Proc. of IEEE Global Commun. Conf. , Rio de Janeiro, Brazil, 2022, pp. 5384–5389

    2022

  91. [99]

    Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems,

    P. Dong, H. Zhang, G. Y . Li, I. S. Gaspar, and N. NaderiAlizadeh, “Deep CNN-Based Channel Estimation for mmWave Massive MIMO Systems,” IEEE Journal of Selected Topics in Signal Processing, vol. 13, no. 5, pp. 989–1000, Sept. 2019

    2019

  92. [100]

    Wideband Channel Estimation With a Generative Adversarial Network,

    E. Balevi and J. G. Andrews, “Wideband Channel Estimation With a Generative Adversarial Network,” IEEE Transactions on Wireless Communications, vol. 20, no. 5, pp. 3049–3060, Jan. 2021

    2021

  93. [101]

    Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO,

    W. Ma, C. Qi, Z. Zhang, and J. Cheng, “Sparse Channel Estimation and Hybrid Precoding Using Deep Learning for Millimeter Wave Massive MIMO,” IEEE Transactions on Communications , vol. 68, no. 5, pp. 2838–2849, May 2020

    2020

  94. [102]

    Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces,

    S. Liu, Z. Gao, J. Zhang, M. D. Renzo, and M.-S. Alouini, “Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces,” IEEE Transactions on Vehicular Technology, vol. 69, no. 8, pp. 9223–9228, Aug. 2020

    2020

  95. [103]

    Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning,

    A. Taha, M. Alrabeiah, and A. Alkhateeb, “Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning,” IEEE Access, vol. 9, pp. 44 304–44 321, March 2021

    2021

  96. [104]

    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, 2015

    2015

  97. [105]

    Dynamic mimo architecture design for near-field communications,

    Z. Zhang, Y . Liu, Z. Wang, J. Chen, and T. Q. S. Quek, “Dynamic mimo architecture design for near-field communications,” IEEE Transactions on Wireless Communications, vol. 23, no. 10, pp. 14 669–14 684, 2024

    2024

  98. [106]

    THzPrism: Frequency-Based Beam Spreading for Terahertz Communication Systems,

    B. Zhai, Y . Zhu, A. Tang, and X. Wang, “THzPrism: Frequency-Based Beam Spreading for Terahertz Communication Systems,” IEEE Wireless Communications Letters, vol. 9, no. 6, pp. 897–900, 2020

    2020

  99. [107]

    Energy-Efficient Dynamic-Subarray With Fixed True-Time-Delay Design for Terahertz Wideband Hybrid Beamforming,

    L. Yan, C. Han, and J. Yuan, “Energy-Efficient Dynamic-Subarray With Fixed True-Time-Delay Design for Terahertz Wideband Hybrid Beamforming,” IEEE Journal on Selected Areas in Communications , vol. 40, no. 10, pp. 2840–2854, 2022

    2022

  100. [108]

    Cross near- and far-field beamforming for wideband multi-user terahertz communications,

    J. Xie, C. Luo, and Y . Luo, “Cross near- and far-field beamforming for wideband multi-user terahertz communications,” IEEE Communications Letters, vol. 28, no. 10, pp. 2397–2401, 2024

    2024

  101. [109]

    Hybrid digital and analog beamforming design for large-scale antenna arrays,

    F. Sohrabi and W. Yu, “Hybrid digital and analog beamforming design for large-scale antenna arrays,” IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 3, pp. 501–513, 2016

    2016

  102. [110]

    Hybrid analog-digital beamforming design for se and ee maximization in massive mimo networks,

    K. Ardah, G. Fodor, Y . C. B. Silva, W. C. Freitas, and A. L. F. de Almeida, “Hybrid analog-digital beamforming design for se and ee maximization in massive mimo networks,” IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 377–389, 2020

    2020

  103. [111]

    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 Journal of Selected Topics in Signal Processing , vol. 10, no. 3, pp. 485–500, 2016

    2016

  104. [112]

    Robust hybrid beamforming in millimeter wave systems with closed-form least-square solutions,

    Z. Luo, L. Zhao, H. Liu, and Y . Li, “Robust hybrid beamforming in millimeter wave systems with closed-form least-square solutions,” IEEE Wireless Communications Letters, vol. 10, no. 1, pp. 156–160, 2021

    2021

  105. [113]

    Hybrid delay-phase precoding in wideband um-mimo systems under true time delay and phase shifter hardware limitations,

    A. Najjar, M. El-Absi, and T. Kaiser, “Hybrid delay-phase precoding in wideband um-mimo systems under true time delay and phase shifter hardware limitations,” IEEE Transactions on Wireless Communications, vol. 23, no. 7, pp. 7246–7262, 2024

    2024

  106. [114]

    An iteratively weighted mmse approach to distributed sum-utility maximization for a mimo interfering broadcast channel,

    Q. Shi, M. Razaviyayn, Z.-Q. Luo, and C. He, “An iteratively weighted mmse approach to distributed sum-utility maximization for a mimo interfering broadcast channel,” IEEE Transactions on Signal Processing, vol. 59, no. 9, pp. 4331–4340, 2011

    2011

  107. [115]

    Weighted sum-rate maximization using weighted mmse for mimo-bc beamforming design,

    S. S. Christensen, R. Agarwal, E. De Carvalho, and J. M. Cioffi, “Weighted sum-rate maximization using weighted mmse for mimo-bc beamforming design,” IEEE Transactions on Wireless Communications, vol. 7, no. 12, pp. 4792–4799, 2008

    2008

  108. [116]

    Hybrid mmse beamforming for multiuser millimeter-wave communication systems,

    J. Cong, T. Lin, and Y . Zhu, “Hybrid mmse beamforming for multiuser millimeter-wave communication systems,” IEEE Communications Let- ters, vol. 22, no. 11, pp. 2390–2393, 2018

    2018

  109. [117]

    Weighted spectral efficiency optimization for hybrid beamforming in multiuser massive mimo-ofdm systems,

    J. Du, W. Xu, C. Zhao, and L. Vandendorpe, “Weighted spectral efficiency optimization for hybrid beamforming in multiuser massive mimo-ofdm systems,” IEEE Transactions on Vehicular Technology , vol. 68, no. 10, pp. 9698–9712, 2019

    2019

  110. [118]

    Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming,

    Q. Wu and R. Zhang, “Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming,” IEEE Transactions on Wireless Communications, vol. 18, no. 11, pp. 5394–5409, 2019

    2019

  111. [119]

    Low-complexity hybrid precoding in massive multiuser mimo systems,

    L. Liang, W. Xu, and X. Dong, “Low-complexity hybrid precoding in massive multiuser mimo systems,” IEEE Wireless Communications Letters, vol. 3, no. 6, pp. 653–656, 2014

    2014

  112. [120]

    Hybrid beamforming for multi-user massive mimo systems,

    X. Wu, D. Liu, and F. Yin, “Hybrid beamforming for multi-user massive mimo systems,” IEEE Transactions on Communications , vol. 66, no. 9, pp. 3879–3891, 2018

    2018

  113. [122]

    Dynamic hybrid beamforming designs for elaa near-field communications,

    M. Liu, M. Li, R. Liu, and Q. Liu, “Dynamic hybrid beamforming designs for elaa near-field communications,” IEEE Journal on Selected Areas in Communications , vol. 43, no. 3, pp. 644–658, 2025

    2025

  114. [123]

    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 Transactions on Wireless Communications , vol. 13, no. 3, pp. 1499– 1513, 2014

    2014

  115. [124]

    Hybrid precoding and combining for millimeter wave/sub-thz mimo-ofdm systems with beam squint effects,

    Q. Wan, J. Fang, Z. Chen, and H. Li, “Hybrid precoding and combining for millimeter wave/sub-thz mimo-ofdm systems with beam squint effects,” IEEE Transactions on Vehicular Technology , vol. 70, no. 8, pp. 8314–8319, 2021

    2021

  116. [125]

    Hybrid block diagonalization for massive multiuser mimo systems,

    W. Ni and X. Dong, “Hybrid block diagonalization for massive multiuser mimo systems,” IEEE Transactions on Communications , vol. 64, no. 1, pp. 201–211, 2016. 30

    2016

  117. [126]

    Hybrid terahertz beamforming relying on channel sparsity and angular orthog- onality,

    Q. Yue, J. Hu, T. Shui, Q. Huang, K. Yang, and L. Hanzo, “Hybrid terahertz beamforming relying on channel sparsity and angular orthog- onality,” IEEE Transactions on Vehicular Technology, vol. 73, no. 4, pp. 4759–4773, 2024

    2024

  118. [127]

    Hybrid beamforming for terahertz multi-carrier systems over frequency selective fading,

    H. Yuan, N. Yang, K. Yang, C. Han, and J. An, “Hybrid beamforming for terahertz multi-carrier systems over frequency selective fading,” IEEE Transactions on Communications, vol. 68, no. 10, pp. 6186–6199, 2020

    2020

  119. [128]

    Wideband terahertz com- munications with aosa: Beam split aggregation and multiplexing,

    B. Ning, L. Li, W. Chen, and Z. Chen, “Wideband terahertz com- munications with aosa: Beam split aggregation and multiplexing,” in GLOBECOM 2022 - 2022 IEEE Global Communications Conference , 2022, pp. 1709–1714

    2022

  120. [129]

    Exploiting beam split-based multi-user diver- sity in terahertz mimo-ofdm systems,

    J. Wang and M. Kaneko, “Exploiting beam split-based multi-user diver- sity in terahertz mimo-ofdm systems,” IEEE Wireless Communications Letters, vol. 14, no. 1, pp. 28–32, 2025

    2025

  121. [130]

    Multi-user analog beamforming in mil- limeter wave mimo systems based on path angle information,

    L. Jiang and H. Jafarkhani, “Multi-user analog beamforming in mil- limeter wave mimo systems based on path angle information,” IEEE Transactions on Wireless Communications, vol. 18, no. 1, pp. 608–619, 2019

    2019

  122. [131]

    Dynamic-subarray with fixed phase shifters for energy-efficient terahertz hybrid beamforming under partial csi,

    L. Yan, C. Han, N. Yang, and J. Yuan, “Dynamic-subarray with fixed phase shifters for energy-efficient terahertz hybrid beamforming under partial csi,” IEEE Transactions on Wireless Communications , vol. 22, no. 5, pp. 3231–3245, 2023

    2023

  123. [132]

    Multiple Access for Near-Field Communications: SDMA or LDMA?

    Z. Wu and L. Dai, “Multiple Access for Near-Field Communications: SDMA or LDMA?” IEEE Journal on Selected Areas in Communica- tions, vol. 41, no. 6, pp. 1918–1935, 2023

    1918

  124. [133]

    Near-Field Wideband Beamforming for Extremely Large Antenna Arrays,

    M. Cui and L. Dai, “Near-Field Wideband Beamforming for Extremely Large Antenna Arrays,” IEEE Transactions on Wireless Communica- tions, vol. 23, no. 10, pp. 13 110–13 124, 2024

    2024

  125. [134]

    Hybrid Beamforming with Widely-spaced-array for Multi-user Cross-Near-and-Far-Field Commu- nications,

    H. Shen, Y . Chen, C. Han, and J. Yuan, “Hybrid Beamforming with Widely-spaced-array for Multi-user Cross-Near-and-Far-Field Commu- nications,” IEEE Transactions on Communications , pp. 1–1, to appear 2025

    2025

  126. [135]

    Hybrid- field-aware two-stage beamforming scheme for xl-mimo systems,

    T. Gao, Y . Song, C. Liu, Z. Yin, N. Cheng, and D. Ge, “Hybrid- field-aware two-stage beamforming scheme for xl-mimo systems,” IEEE Internet of Things Journal , pp. 1–1, 2025

    2025

  127. [136]

    Twenty- five years of advances in beamforming: From convex and nonconvex optimization to learning techniques,

    A. M. Elbir, K. V . Mishra, S. A. V orobyov, and R. W. Heath, “Twenty- five years of advances in beamforming: From convex and nonconvex optimization to learning techniques,” IEEE Signal Processing Magazine, vol. 40, no. 4, pp. 118–131, 2023

    2023

  128. [137]

    Fast beamforming design via deep learning,

    H. Huang, Y . Peng, J. Yang, W. Xia, and G. Gui, “Fast beamforming design via deep learning,” IEEE Transactions on Vehicular Technology, vol. 69, no. 1, pp. 1065–1069, 2020

    2020

  129. [138]

    Hy- brid beamforming based on an unsupervised deep learning network for downlink channels with imperfect csi,

    P. Zhang, L. Pan, T. Laohapensaeng, and M. Chongcheawchamnan, “Hy- brid beamforming based on an unsupervised deep learning network for downlink channels with imperfect csi,” IEEE Wireless Communications Letters, vol. 11, no. 7, pp. 1543–1547, 2022

    2022

  130. [139]

    Learning to beamform in joint mul- ticast and unicast transmission with imperfect csi,

    Z. Zhang, M. Tao, and Y .-F. Liu, “Learning to beamform in joint mul- ticast and unicast transmission with imperfect csi,” IEEE Transactions on Communications, vol. 71, no. 5, pp. 2711–2723, 2023

    2023

  131. [140]

    Codebook-based downlink beamforming with imperfect csi using model-driven deep learning,

    L. Schynol, M. Hemsing, and M. Pesavento, “Codebook-based downlink beamforming with imperfect csi using model-driven deep learning,” in 2024 58th Asilomar Conference on Signals, Systems, and Computers , 2024, pp. 583–590

    2024

  132. [141]

    Model-driven deep learning for massive mu-mimo with finite-alphabet precoding,

    H. He, M. Zhang, S. Jin, C.-K. Wen, and G. Y . Li, “Model-driven deep learning for massive mu-mimo with finite-alphabet precoding,” IEEE Communications Letters, vol. 24, no. 10, pp. 2216–2220, 2020

    2020

  133. [142]

    Data-driven-based ana- log beam selection for hybrid beamforming under mm-wave channels,

    Y . Long, Z. Chen, J. Fang, and C. Tellambura, “Data-driven-based ana- log beam selection for hybrid beamforming under mm-wave channels,” IEEE Journal of Selected Topics in Signal Processing , vol. 12, no. 2, pp. 340–352, 2018

    2018

  134. [143]

    Adaptive ttd configurations for near- field communications: An unsupervised transformer approach,

    H. Ting, Z. Wang, and Y . Liu, “Adaptive ttd configurations for near- field communications: An unsupervised transformer approach,” IEEE Transactions on Wireless Communications, vol. 24, no. 1, pp. 277–292, 2025

    2025

  135. [144]

    Wavefront hopping for physical layer security in 6g and beyond near-field thz communications,

    V . Petrov, H. Guerboukha, A. Singh, and J. M. Jornet, “Wavefront hopping for physical layer security in 6g and beyond near-field thz communications,” IEEE Transactions on Communications , pp. 1–1, 2024

    2024

  136. [145]

    Optimal receiver design for orbital angular momentum (oam) communication via sampled beam analysis,

    A. Mehra and S. Arnon, “Optimal receiver design for orbital angular momentum (oam) communication via sampled beam analysis,” Journal of Lightwave Technology, pp. 1–10, 2025

    2025

  137. [146]

    Irs-aided wireless relaying for high-speed train communication: Beam- forming design and channel estimation,

    J. Feng, B. Zheng, C. You, X. Xiong, J. Tang, F. Chen, and R. Zhang, “Irs-aided wireless relaying for high-speed train communication: Beam- forming design and channel estimation,” IEEE Transactions on Wireless Communications, vol. 23, no. 12, pp. 18 380–18 393, 2024

    2024

  138. [147]

    Near- field beamforming and doppler compensation for mmwave internet of vehicles,

    H. Tong, T. Cheng, X. Wang, T. Li, X. Su, and Y . Xu, “Near- field beamforming and doppler compensation for mmwave internet of vehicles,” IEEE Communications Letters , vol. 28, no. 3, pp. 702–706, 2024

    2024

  139. [148]

    Beam tracking for mobile millimeter wave communication systems,

    V . Va, H. Vikalo, and R. W. Heath, “Beam tracking for mobile millimeter wave communication systems,” in Proc. of IEEE Global Conference on Signal and Information Processing , 2016, pp. 743–747

    2016

  140. [149]

    Robust Beam- Tracking for mmWave Mobile Communications,

    S. Jayaprakasam, X. Ma, J. W. Choi, and S. Kim, “Robust Beam- Tracking for mmWave Mobile Communications,” IEEE Communica- tions Letters, vol. 21, no. 12, pp. 2654–2657, 2017

    2017

  141. [150]

    EKF-Based Beam Tracking for mmWave MIMO Systems,

    F. Liu, P. Zhao, and Z. Wang, “EKF-Based Beam Tracking for mmWave MIMO Systems,” IEEE Communications Letters , vol. 23, no. 12, pp. 2390–2393, 2019

    2019

  142. [151]

    Two-Timescale Joint Precoding Design and RIS Optimization for User Tracking in Near-Field MIMO Systems,

    S. Palmucci, A. Guerra, A. Abrardo, and D. Dardari, “Two-Timescale Joint Precoding Design and RIS Optimization for User Tracking in Near-Field MIMO Systems,” IEEE Transactions on Signal Processing , vol. 71, pp. 3067–3082, 2023

    2023

  143. [152]

    Beam Training and Tracking for Extremely Large-Scale MIMO Communications,

    K. Chen, C. Qi, C.-X. Wang, and G. Y . Li, “Beam Training and Tracking for Extremely Large-Scale MIMO Communications,”IEEE Transactions on Wireless Communications, vol. 23, no. 5, pp. 5048–5062, 2024

    2024

  144. [153]

    Adaptive Beam Tracking With the Unscented Kalman Filter for Millimeter Wave Communication,

    S. G. Larew and D. J. Love, “Adaptive Beam Tracking With the Unscented Kalman Filter for Millimeter Wave Communication,” IEEE Signal Processing Letters , vol. 26, no. 11, pp. 1658–1662, 2019

    2019

  145. [154]

    Adaptive Beamwidth Control for mmWave Beam Tracking,

    H. Chung, J. Kang, H. Kim, Y . M. Park, and S. Kim, “Adaptive Beamwidth Control for mmWave Beam Tracking,” IEEE Communica- tions Letters, vol. 25, no. 1, pp. 137–141, 2021

    2021

  146. [155]

    Beam Tracking Under Highly Nonlinear Mobile Millimeter-Wave Channel,

    J. Lim, H.-M. Park, and D. Hong, “Beam Tracking Under Highly Nonlinear Mobile Millimeter-Wave Channel,” IEEE Communications Letters, vol. 23, no. 3, pp. 450–453, 2019

    2019

  147. [156]

    Near-Field Tracking With Large Antenna Arrays: Fundamental Limits and Practical Algo- rithms,

    A. Guerra, F. Guidi, D. Dardari, and P. M. Djuri ´c, “Near-Field Tracking With Large Antenna Arrays: Fundamental Limits and Practical Algo- rithms,” IEEE Transactions on Signal Processing , vol. 69, pp. 5723– 5738, 2021

    2021

  148. [157]

    Learning and Adaptation for Millimeter- Wave Beam Tracking and Training: A Dual Timescale Variational Framework,

    M. Hussain and N. Michelusi, “Learning and Adaptation for Millimeter- Wave Beam Tracking and Training: A Dual Timescale Variational Framework,” IEEE Journal on Selected Areas in Communications , vol. 40, no. 1, pp. 37–53, 2022

    2022

  149. [158]

    Integrated Beam Tracking and Communication for (Sub-)mmWave Links With Stochastic Mobility,

    N. Ronquillo, C.-S. Gau, and T. Javidi, “Integrated Beam Tracking and Communication for (Sub-)mmWave Links With Stochastic Mobility,” IEEE Journal on Selected Areas in Information Theory , vol. 4, pp. 94– 111, 2023

    2023

  150. [159]

    Deep Learning-Based Beam Tracking for Millimeter-Wave Communications Under Mobility,

    S. H. Lim, S. Kim, B. Shim, and J. W. Choi, “Deep Learning-Based Beam Tracking for Millimeter-Wave Communications Under Mobility,” IEEE Transactions on Communications, vol. 69, no. 11, pp. 7458–7469, 2021

    2021

  151. [160]

    KF-LSTM Based Beam Tracking for UA V-Assisted mmWave HSR Wireless Networks,

    L. Yan, X. Fang, Y . Fang, L. Hao, Q. Xue, and C. Xu, “KF-LSTM Based Beam Tracking for UA V-Assisted mmWave HSR Wireless Networks,” IEEE Transactions on Vehicular Technology, vol. 71, no. 10, pp. 10 796– 10 807, 2022

    2022

  152. [161]

    AI-Empowered Beam Track- ing for Near-Field Communications,

    M. Zhang, R. Zhong, X. Mu, and Y . Liu, “AI-Empowered Beam Track- ing for Near-Field Communications,” in Proc. of IEEE International Conference on Communications , 2024, pp. 1643–1648

    2024

  153. [162]

    AI-Empowered Beam Tracking for Near-Field Communications,

    ——, “AI-Empowered Beam Tracking for Near-Field Communications,” in Proc. of IEEE International Conference on Communications , 2024, pp. 1643–1648

    2024

  154. [163]

    Dual-Path Beam Tracking for Service Continuity of Ultra-Reliable and Low-Latency Communications,

    R. Wang, C. She, C. Li, Y . Li, and B. Vucetic, “Dual-Path Beam Tracking for Service Continuity of Ultra-Reliable and Low-Latency Communications,” IEEE Transactions on Communications , vol. 73, no. 1, pp. 524–539, 2025

    2025

  155. [164]

    Online Learning for Joint Beam Tracking and Pattern Optimization in Massive MIMO Systems,

    J. Jeong, S. H. Lim, Y . Song, and S.-W. Jeon, “Online Learning for Joint Beam Tracking and Pattern Optimization in Massive MIMO Systems,” in Proc. of IEEE Conference on Computer Communications , 2020, pp. 764–773

    2020

  156. [165]

    Deep Q-Network Based Beam Tracking for Mobile Millimeter-Wave Communications,

    H. Park, J. Kang, S. Lee, J. W. Choi, and S. Kim, “Deep Q-Network Based Beam Tracking for Mobile Millimeter-Wave Communications,” IEEE Transactions on Wireless Communications , vol. 22, no. 2, pp. 961–971, 2023

    2023

  157. [166]

    Robust Near- field Beam Tracking via Deep Q-network for THz Communications,

    H. Park, H. Chung, A. Conti, M. Z. Win, and S. Kim, “Robust Near- field Beam Tracking via Deep Q-network for THz Communications,” in Proc. of International Conference on Information Fusion , 2024, pp. 1–5. 31

    2024

  158. [167]

    MAMBA: A Multi-armed Bandit Framework for Beam Tracking in Millimeter-wave Systems,

    I. Aykin, B. Akgun, M. Feng, and M. Krunz, “MAMBA: A Multi-armed Bandit Framework for Beam Tracking in Millimeter-wave Systems,” in IEEE INFOCOM 2020 - IEEE Conference on Computer Communica- tions, 2020, pp. 1469–1478

    2020

  159. [168]

    Beam Alignment and Tracking for Millimeter Wave Communications via Bandit Learning,

    J. Zhang, Y . Huang, Y . Zhou, and X. You, “Beam Alignment and Tracking for Millimeter Wave Communications via Bandit Learning,” IEEE Transactions on Communications , vol. 68, no. 9, pp. 5519–5533, 2020

    2020

  160. [169]

    Machine-Learning Beam Tracking and Weight Optimization for mmWave Multi-UA V Links,

    H.-L. Chiang, K.-C. Chen, W. Rave, M. Khalili Marandi, and G. Fet- tweis, “Machine-Learning Beam Tracking and Weight Optimization for mmWave Multi-UA V Links,”IEEE Transactions on Wireless Commu- nications, vol. 20, no. 8, pp. 5481–5494, 2021

    2021

  161. [170]

    Channel Fingerprint Based Beam Tracking for Millimeter Wave Communications,

    R. Deng, S. Chen, S. Zhou, Z. Niu, and W. Zhang, “Channel Fingerprint Based Beam Tracking for Millimeter Wave Communications,” IEEE Communications Letters, vol. 24, no. 3, pp. 639–643, 2020

    2020

  162. [171]

    Image and index fused sequence-to-sequence algorithm for vision-aided millimeter-wave beam tracking,

    Z. Hu and C. Han, “Image and index fused sequence-to-sequence algorithm for vision-aided millimeter-wave beam tracking,” in Proc. of ACM Workshop on Millimeter-Wave and Terahertz Networks and Sensing Systems, 2021, pp. 7–12

    2021

  163. [172]

    Computer Vision Aided Beam Tracking in A Real-World Millimeter Wave Deployment,

    S. Jiang and A. Alkhateeb, “Computer Vision Aided Beam Tracking in A Real-World Millimeter Wave Deployment,” in Proc. of IEEE Globecom Workshops, 2022, pp. 142–147

    2022

  164. [173]

    Computer Vision-Aided Reconfigurable Intelligent Surface-Based Beam Tracking: Prototyping and Experimental Results,

    M. Ouyang, F. Gao, Y . Wang, S. Zhang, P. Li, and J. Ren, “Computer Vision-Aided Reconfigurable Intelligent Surface-Based Beam Tracking: Prototyping and Experimental Results,” IEEE Transactions on Wireless Communications, vol. 22, no. 12, pp. 8681–8693, 2023

    2023

  165. [174]

    Event-Based Beam Tracking With Dynamic Beamwidth Adaptation in Terahertz (THz) Communications,

    Y . Karaçora, C. Chaccour, A. Sezgin, and W. Saad, “Event-Based Beam Tracking With Dynamic Beamwidth Adaptation in Terahertz (THz) Communications,” IEEE Transactions on Communications , vol. 71, no. 10, pp. 6195–6210, 2023

    2023

  166. [175]

    Context-Aware Beam Tracking for 5G mmWave V2I Communications,

    H. Ding and K. G. Shin, “Context-Aware Beam Tracking for 5G mmWave V2I Communications,” IEEE Transactions on Mobile Com- puting, vol. 22, no. 6, pp. 3257–3269, 2023

    2023

  167. [176]

    Beam Tracking and Optimization for UA V Communications,

    L. Yang and W. Zhang, “Beam Tracking and Optimization for UA V Communications,” IEEE Transactions on Wireless Communications , vol. 18, no. 11, pp. 5367–5379, 2019

    2019

  168. [177]

    Beam tracking based on variable step beam for millimeter wave massive mimo,

    Z. Xiao, C. Qi, and J. Nie, “Beam tracking based on variable step beam for millimeter wave massive mimo,” IEEE Communications Letters , vol. 27, no. 9, pp. 2417–2421, 2023

    2023

  169. [178]

    Codebook-Based Training Beam Sequence Design for Millimeter-Wave Tracking Systems,

    D. Zhang, A. Li, M. Shirvanimoghaddam, P. Cheng, Y . Li, and B. Vucetic, “Codebook-Based Training Beam Sequence Design for Millimeter-Wave Tracking Systems,” IEEE Transactions on Wireless Communications, vol. 18, no. 11, pp. 5333–5349, 2019

    2019

  170. [179]

    Near-Field Beam Tracking with Extremely Large Dynamic Metasurface Antennas,

    P. Gavriilidis and G. C. Alexandropoulos, “Near-Field Beam Tracking with Extremely Large Dynamic Metasurface Antennas,” arXiv preprint arXiv:2406.01488, 2024

    2024 arXiv

  171. [180]

    A Low-Overhead Hierarchical Beam-tracking Algorithm for THz Wireless Systems,

    G. Stratidakis, G. D. Ntouni, A. A. Boulogeorgos, D. Kritharidis, and A. Alexiou, “A Low-Overhead Hierarchical Beam-tracking Algorithm for THz Wireless Systems,” in Proc. of European Conf. Netw. Commun., Dubrovnik, Croatia, 2020, pp. 74–78

    2020

  172. [181]

    Tracking mm-Wave channel dynamics: Fast beam training strategies under mobility,

    J. Palacios, D. De Donno, and J. Widmer, “Tracking mm-Wave channel dynamics: Fast beam training strategies under mobility,” in Proc. of IEEE International Conference on Computer Communications , 2017, pp. 1–9

    2017

Pith tools

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