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REVIEW 2 major objections 4 minor 173 references

Advancing Fluid Antenna-Assisted Non-Terrestrial Networks in 6G and Beyond: Fundamentals, State of the Art, and Future Directions

T0 review · 2 major / 4 minor · reviewed 2026-08-04 · deepseek-v4-flash

Pith's one-line read Equipping non-terrestrial platforms with fluid antennas—antennas whose radiating element moves among preset ports—can deliver diversity, multiplexing, security, and energy-efficiency gains that fixed antennas cannot.

desk verdict A useful map of FA-assisted NTNs, but the central gain claims inherit rich-scattering assumptions that often don't hold in satellite and aerial links, and the paper needs mechanical fixes. read the letter →

arxiv 2511.00569 v2 pith:XUPSFGZI submitted 2025-11-01 cs.NI eess.SP

classification cs.NIeess.SP
keywords fluidantennanon-terrestrialnetworks6Gmovablemultipleaccesschannelstateinformationestimationjointoptimizationphysicallayersecurity
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a survey, and its load-bearing claim is that pairing fluid antennas with non-terrestrial networks is not just another optimization trick but a coherent 6G direction. Fluid antennas reshape a wireless channel by moving a radiating element among closely spaced ports within a small region, selecting the position with the best gain or SINR; the survey argues this added degree of freedom translates into higher diversity and multiplexing gains, lower power use, and stronger security than fixed-position antennas can offer. It reviews the evidence across hovering and mobile UAV networks, satellite networks, combinations with technologies like cell-free MIMO, full-duplex, NOMA, and reconfigurable intelligent surfaces, and integrated sensing/communication/computation, plus physical-layer security. The reader is meant to come away convinced that FA-assisted NTNs can mitigate the dynamic fading, interference, and energy constraints of 6G non-terrestrial links.

What carries the argument

The load-bearing mechanism is port selection under spatial correlation. A fluid antenna samples N ports in a small region; the channels at those ports are modeled as correlated Rayleigh fading using Bessel/Jakes spatial correlation (or eigen-decomposition of a correlation matrix), and the system moves the radiating element to the port with maximum channel gain or SINR. This mechanism underlies the diversity and multiplexing gains, and it is what the survey's reviewed optimization works exploit: alternating optimization, successive convex approximation, particle swarm, and deep reinforcement learning jointly adjust FA positions, beamforming vectors, transmit power, and platform trajectory. Th

What would settle it

A measurement campaign on a UAV-to-ground or LEO downlink that compares outage probability and achievable rate of a reconfigurable fluid-antenna prototype against a fixed antenna of the same size would settle the claim: if the measured spatial correlation makes port selection no better than fixed selection, or if movement latency exceeds the channel coherence time, the survey's transferable gains fail. A less expensive falsifier is a simulation that replaces the Bessel correlation model with a measured or ray-traced air-to-ground correlation matrix and checks whether the no-outage-floor divers

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim to be established is that the positional flexibility of fluid antennas converts the spatial variations of an NTN channel into usable diversity, multiplexing, and security gains. The survey presents FA port selection as an additional degree of freedom: with N ports distributed over a wavelength-scale region, the receiver (or transmitter) chooses the port maximizing channel gain or SINR, and the resulting gains—including no outage floor as N grows—are claimed to carry over to UAV, HAP, and satellite links when FA positions, beamforming, power, and platform motion are jointly optimized. It extends this to fluid antenna multiple access (FAMA), where pe

Load-bearing premise

The load-bearing premise is that the spatial-correlation channel models borrowed from terrestrial fluid-antenna work—Bessel/Jakes correlations and rich-scattering Rayleigh or eigen-decomposition models—describe real non-terrestrial links, and that the antenna hardware can move and switch ports fast enough with fresh CSI to exploit those correlations.

Editorial extensions

If this is right

  • If the survey's claims hold, NTN links can compensate for Doppler, path loss, and interference by repositioning antennas instead of adding RF chains or transmit power, which matters for energy-constrained UAVs and satellites.
  • FAMA would let a single base station serve hundreds or thousands of users on the same time-frequency resource without requiring transmitter-side CSI, directly addressing spectrum scarcity in satellite and aerial networks.
  • Jointly optimizing FA positions with platform trajectories and beamforming should improve minimum and sum rates relative to fixed-antenna baselines in hovering UAV, mobile UAV, and LEO satellite scenarios.
  • AI-based CSI reconstruction from a few ports makes real-time port selection plausible in fast-changing air-to-ground channels, though the survey notes practical inference-speed limits.
  • Physical-layer security and covert communication can be enhanced by FA position choices, but the survey leaves open the case where eavesdroppers also use FAs.

Reading between the lines

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

  • Editorial inference: the claims are shaped by terrestrial rich-scattering channel models; the strongest test of the survey's thesis is a measurement of spatial correlation on real LEO or UAV links, because if satellite channels are too LoS-dominated or too sparse, port-selection gains may shrink.
  • Editorial inference: symbol-level FAMA assumes switching fast enough to track instantaneous fades; practical mechanical or liquid latency might push systems toward slow-FAMA or statistical CSI, changing the tradeoffs the survey summarizes.
  • Editorial inference: the same position-flexibility argument could extend to alignment problems in high-frequency (THz or optical) inter-satellite links, where beam alignment is the bottleneck and moving a small aperture might be cheaper than steering a large array.
  • Editorial inference: a direct testable extension is to replace the Bessel correlation kernel with a measured or ray-traced air-to-ground correlation matrix and check whether the no-outage-floor diversity result survives.
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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 / 4 minor

Summary. This survey reviews fluid antenna (FA)-assisted non-terrestrial networks (NTNs) for 6G. It covers NTN platform fundamentals, FA hardware and spatial-correlation channel models, AI-based CSI estimation, joint optimization of FA positions/beamforming/trajectories in hovering UAV, mobile UAV, and satellite networks, compatibility with cell-free massive MIMO, full-duplex, NGMA/NOMA/FAMA, and RIS, integrated sensing/communication/computation architectures, physical-layer security and covert communication, and future directions including AI, THz, near-field, and ISCC. The paper's central claim is that FAs can provide higher diversity, multiplexing, energy efficiency, and security gains than fixed-position antennas in NTNs, and that FA-assisted NTNs are a coherent and maturing research direction. The survey contributes a synthesis and taxonomy rather than a new proof.

Significance. If taken as a state-of-the-art synthesis, the survey is timely and useful: it is, to my knowledge, the first survey devoted specifically to FA-assisted NTNs, and it consolidates a rapidly growing body of work (175 references), including recent optimization papers, security results, and intelligent-function-integration architectures. The structured taxonomy, summary tables, and figures provide a good entry point for researchers. The main weakness is that the survey's headline claim -- that FA gains transfer to NTNs -- is presented without the channel-model and hardware caveats that the survey itself partially acknowledges. Because the work is a survey, this is a framing issue rather than an internal inconsistency, but it affects the central message and should be corrected before publication.

major comments (2)
  1. [Section II-C / III-A, Eq. (14)] The diversity and multiplexing advantages of FAs are derived from rich-scattering Rayleigh/Jakes models (Eqs. (2)-(5), (8)-(9), (14)). In the NTN channels described by the survey itself -- e.g., the LoS probability model in Eq. (18) and the air-to-ground geometry of Section III-A -- links are frequently LoS-dominated or have a very small angular spread. Under such conditions, port correlations approach unity and the diversity order in Eq. (14) collapses to roughly one regardless of N. The abstract and conclusion nevertheless state the gains as established ('higher channel diversity and multiplexing gains'; 'unlock the enormous potential of 6G') without the rich-scattering qualifier. The only finite-scattering results shown (Fig. 12, Rician K=7 with two scattered paths) are still not representative of LoS-dominated satellite links. This is a load-bearing framing issue: the survey should e
  2. [Section IV-A-1 / V-C-2] The survey acknowledges practical limitations at several points -- e.g., 'high mechanical latency' in Section IV-A-1 and the statement that f-FAMA 'currently confines it to the theoretical simulation stage' in Section V-C-2 -- but these admissions are not integrated into the abstract or conclusion, which recommend FA-assisted NTNs as a maturing path. The reviewed performance gains assume port switching and CSI acquisition at speeds and accuracies that may be incompatible with NTN dynamics, Doppler shifts, and the latency constraints described in Section I-B. A survey of this scope should include a dedicated critical assessment of hardware feasibility, movement latency, CSI staleness, and the impact of these constraints on the summarized gains, and should temper the central claims accordingly.
minor comments (4)
  1. [Section VII-A] The sentence 'The FA-assisted UAV can also serve as a friendly jammer to ensure secure communication between Bob and Alice' is duplicated. Also, 'legitimate the transmitter' should read 'the legitimate transmitter.'
  2. [Eq. (20)] The Doppler phase-shift expression is malformed: 'sin β T' appears to be a typo (likely sin β(t) or a missing variable). In Eq. (15), 'τ_LoS and τ_LoS' should presumably be 'τ_LoS and τ_NLoS.'
  3. [Fig. 13 caption / Section V-C-2] The caption of Fig. 13 attributes s-FAMA results to reference [102], but [102] is the movable-antenna satellite beam-coverage paper; the text correctly cites [115] for s-FAMA. This citation mismatch should be corrected, and other figure captions citing specific references should be checked.
  4. [Fig. 1 caption / Section II-B-1] Minor language issues: 'An potential FA-assisted NTN architecture' should be 'A potential...'; 'we uses a µ' should be 'we use a µ'; 'spacial' should be 'spatial.'

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: survey reports externally checkable prior results; self-citations are not load-bearing in the derivation sense.

full rationale

This is a survey paper, not a derivation. Its central claim that FAs provide diversity/multiplexing gains in NTNs is supported by cited peer-reviewed papers (e.g., [9], [16], [51], [102], [116]) rather than by an argument that reduces to its own assumptions. The spatial-correlation models (Eqs. (2)-(9)) are explicitly attributed to prior literature and state their assumptions (Jakes/Bessel, Rayleigh rich scattering); Eq. (14) is presented as a quoted upper bound from [9], not derived anew, so there is no fitted parameter relabeled as prediction and no equation that is equivalent to its input by construction. The text repeatedly flags the main limitations that would bear on external validity: 'the updates of FA positions in every channel coherence time result in rapid instantaneous CSI changes and high mechanical latency' (Sec. IV-A-1), mechanically MAs have 'movement response time and spatial coupling' issues (Sec. II-B-3), and f-FAMA 'relies on instantaneous CSI and symbol-level port switching, which currently confines it to the theoretical simulation stage' (Sec. V-C-2). These caveats are correctness/robustness concerns for the surveyed gains, not evidence of circularity. Although many foundational FA references are by the authors (Wong, New, Zhu, Wu et al.), the survey does not invoke a uniqueness theorem or hidden ansatz from those papers to force its organizational conclusion; the cited results have independent, falsifiable content with stated assumptions. Hence no circular step is identifiable under the requested standard.

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

The survey introduces no free parameters or invented entities. Its conclusions rest on channel modeling assumptions inherited from the cited FA and UAV literature, plus the general assumption that the survey represents those citations accurately.

assumptions (4)
  • domain assumption FA port channels follow the Jakes/Clarke correlation model, with correlation coefficient μ_n = J0(2π(n−1)W/(N−1)) (Eqs. (3)-(5)).
    Used throughout Section II-B to quantify FA diversity gains; assumes rich isotropic scattering and small port spacing.
  • domain assumption LoS/NLoS pathloss with LoS probability Pr_LoS(t) = 1/(1 + a exp(−b(θ(t)−a))) (Eq. (18)) describes air-to-ground NTN channels.
    Imported from UAV channel literature and used in Section III-A for channel modeling of FA-assisted NTNs.
  • domain assumption NLoS channel matrix follows the 2-D FA eigen-decomposition H = Q_rx Λ_rx^(1/2) G Λ_tx^(1/2) Q_tx^H (Eq. (9)).
    Assumes the spatial correlation matrix is known and decomposable; basis for summarizing several optimization results.
  • domain assumption The survey faithfully represents the cited results.
    The survey does not re-derive results; its educational value depends on accurate summaries and citations, which is imperfect (e.g., Fig. 13 caption).

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Cite this review

Pith. "Pith review of Advancing Fluid Antenna-Assisted Non-Terrestrial Networks in 6G and Beyond: Fundamentals, State of the Art, and Future Directions." pith.science (2026). https://pith.science/paper/XUPSFGZI

@misc{pith2026251100569,
  author       = {Pith},
  title        = {Pith review of: Advancing Fluid Antenna-Assisted Non-Terrestrial Networks in 6G and Beyond: Fundamentals, State of the Art, and Future Directions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XUPSFGZI}},
  note         = {Machine review of arXiv:2511.00569}
}
read the original abstract

With the surging demand for ultra-reliable, low-latency, and ubiquitous connectivity in Sixth-Generation (6G) networks, Non-Terrestrial Networks (NTNs) emerge as a key complement to terrestrial networks by offering flexible access and global coverage. Despite the significant potential, NTNs still face critical challenges, including dynamic propagation environments, energy constraints, and dense interference. As a key 6G technology, Fluid Antennas (FAs) can reshape wireless channels by reconfiguring radiating elements within a limited space, such as their positions and rotations, to provide higher channel diversity and multiplexing gains. Compared to fixed-position antennas, FAs can present a promising integration path for NTNs to mitigate dynamic channel fading and optimize resource allocation. This paper provides a comprehensive review of FA-assisted NTNs. We begin with a brief overview of the classical structure and limitations of existing NTNs, the fundamentals and advantages of FAs, and the basic principles of FA-assisted NTNs. We then investigate the joint optimization solutions, detailing the adjustments of FA configurations, NTN platform motion modes, and resource allocations. We also discuss the combination with other emerging technologies and explore FA-assisted NTNs as a novel network architecture for intelligent function integrations. Furthermore, we delve into the physical layer security and covert communication in FA-assisted NTNs. Finally, we highlight the potential future directions to empower broader applications of FA-assisted NTNs.

Figures

Figures reproduced from arXiv: 2511.00569 by the authors.

Figure 1
Figure 1. An potential FA-assisted NTN architecture. FAs integrated with different NTN platforms can provide global signal coverage and flexible resource [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Organization of this survey. PLS and how to utilize FAs for covert communication are discussed. • We outline future directions and new research opportuni￾ties in FA-assisted NTNs, including AI applications, high￾frequency communication, near-field communication, as well as Integrated Sensing, Communication, and Compu￾tation (ISCC). The organization of this survey is shown in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Liquid-based FA. The antenna feeding mechanism gathers CSI from [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figures from the paper (12 more)
Figure 5
Figure 5. Figure 5: Programmable Meta-FA in [50]. (a) A potential architecture of Meta￾FA. A 2-D plane deploys massive controllable basic units that contain a current-carrying slot and four PIN diodes. The FPGA controller can selectively activate the basic unit to adjust the amplitude and…
Figure 6
Figure 6. Figure 6: Mechanically MA. (a) A potential architecture of MA. The digital [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 8
Figure 8. Figure 8: The FA-assisted NTN channel model. environments. Then we provide a detailed review of AI-based CSI estimation methods. A. Channel Modeling of FA-Assisted NTNs An FA-assisted NTN structure can be modeled as a lay￾ered, functionally integrated system architecture consist…
Figure 9
Figure 9. Figure 9: The model architecture of (a) LSTM-based, (b) transformer-based, [PITH_FULL_IMAGE:figures/full_fig_p011_9.png]
Figure 10
Figure 10. Figure 10: Minimum achievable rates versus number of users in a multiuser [PITH_FULL_IMAGE:figures/full_fig_p014_10.png]
Figure 11
Figure 11. Figure 11: Experimental results in [98]: (a) 3D UAV trajectory of the proposed algorithm; (b) The achievable data rate versus transmit power. faster convergence and higher sum rate than the fixed UAV trajectory and FPA scheme. In downlink data transmission, Liu et al. in [98] ut…
Figure 12
Figure 12. Figure 12: Achievable network rates of f-FAMA against the number of UEs [PITH_FULL_IMAGE:figures/full_fig_p017_12.png]
Figure 13
Figure 13. Figure 13: Achievable network rates of s-FAMA against the number of UEs [PITH_FULL_IMAGE:figures/full_fig_p017_13.png]
Figure 14
Figure 14. Figure 14: A potential RIS-FA-NTN system. In satellite networks, RIS-FA [PITH_FULL_IMAGE:figures/full_fig_p019_14.png]
Figure 15
Figure 15. Figure 15: An FA-assisted NTNs integrated mobile edge computing systems. [PITH_FULL_IMAGE:figures/full_fig_p020_15.png]
Figure 16
Figure 16. Figure 16: An FA-assisted NTNs integrated over-the-air FL systems. In the FA [PITH_FULL_IMAGE:figures/full_fig_p021_16.png]
Figure 17
Figure 17. Figure 17: An FA-assisted NTNs integrated ISAC system. FA-assisted NTNs [PITH_FULL_IMAGE:figures/full_fig_p022_17.png]

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Works this paper leans on

173 extracted references · 20 linked inside Pith

  1. [102]

    Dynamic Beam Coverage for Satellite Communications Aided by Movable-Antenna Array,

    L. Zhu, X. Pi, W. Ma, Z. Xiao, and R. Zhang, “Dynamic Beam Coverage for Satellite Communications Aided by Movable-Antenna Array,”IEEE Transactions on Wireless Communications, vol. 24, no. 3, pp. 1916–1933, Mar. 2025

  2. [1]

    A Survey on Space-Air- Ground-Sea Integrated Network Security in 6G,

    H. Guo, J. Li, J. Liu, N. Tian, and N. Kato, “A Survey on Space-Air- Ground-Sea Integrated Network Security in 6G,”IEEE Communications Surveys & Tutorials, vol. 24, no. 1, pp. 53–87, Firstquarter 2022

  3. [2]

    A Review on 6G for Space-Air-Ground Integrated Network: Key Enablers, Open Challenges, and Future Direction,

    P. P. Ray, “A Review on 6G for Space-Air-Ground Integrated Network: Key Enablers, Open Challenges, and Future Direction,”Journal of King Saud University-Computer and Information Sciences, vol. 34, no. 9, pp. 6949–6976, Oct. 2022

  4. [3]

    Evolution of Non-Terrestrial Networks from 5G to 6G: A survey,

    M. M. Azari, S. Solanki, S. Chatzinotas, O. Kodheli, H. Sallouha, A. Colpaert, J. F. Mendoza Montoya, S. Pollin, A. Haqiqatnejad, A. Mostaani, E. Lagunas, and B. Ottersten, “Evolution of Non-Terrestrial Networks from 5G to 6G: A survey,”IEEE Communications Surveys & Tutorials, vol. 24, no. 4, pp. 2633–2672, Fourthquarter 2022

  5. [4]

    Cellular, Aide-Area, and Nonterrestrial IoT: A Survey on 5G Advances and the Road Toward 6G,

    M. Vaezi, A. Azari, S. R. Khosravirad, M. Shirvanimoghaddam, M. M. Azari, D. Chasaki, and P. Popovski, “Cellular, Aide-Area, and Nonterrestrial IoT: A Survey on 5G Advances and the Road Toward 6G,”IEEE Communications Surveys & Tutorials, vol. 24, no. 2, pp. 1117–1174, Secondquarter 2022

  6. [5]

    Inte- grating Terrestrial and Non-Terrestrial Networks: 3D Opportunities and Challenges,

    G. Geraci, D. L ´opez-P´erez, M. Benzaghta, and S. Chatzinotas, “Inte- grating Terrestrial and Non-Terrestrial Networks: 3D Opportunities and Challenges,”IEEE Communications Magazine, vol. 61, no. 4, pp. 42–48, Apr. 2023

  7. [6]

    Review of 5G NTN Standards Development and Technical Challenges for Satellite Integration With the 5G Network,

    M. Hosseinian, J. P. Choi, S. H. Chang, and J. Lee, “Review of 5G NTN Standards Development and Technical Challenges for Satellite Integration With the 5G Network,”IEEE Aerospace and Electronic Systems Magazine, vol. 36, no. 8, pp. 22–31, Aug. 2021

  8. [7]

    Toward 6G Non-Terrestrial Networks,

    G. Araniti, A. Iera, S. Pizzi, and F. Rinaldi, “Toward 6G Non-Terrestrial Networks,”IEEE Network, vol. 36, no. 1, pp.113–120, Nov. 2021

Show all 173 references
  1. [8]

    Large Language Models for UA Vs: Current State and Pathways to the Future,

    S. Javaid, H. Fahim, B. He, and N. Saeed, “Large Language Models for UA Vs: Current State and Pathways to the Future,”IEEE Open Journal of V ehicular Technology, vol. 5, pp. 1166–1192, Aug. 2024

  2. [9]

    Fluid Antenna Systems,

    K. K. Wong, A. Shojaeifard, K. F. Tong, and Y . Zhang, “Fluid Antenna Systems,”IEEE Transactions on Wireless Communications, vol. 20, no. 3, pp. 1950–1962, Mar. 2021

  3. [10]

    Historical Review of Fluid Antenna and Movable Antenna,

    L. Zhu and K. K. Wong, “Historical Review of Fluid Antenna and Movable Antenna,”arXiv preprint arXiv:2401.02362v2, 2024

  4. [11]

    Fluid Antenna System: New Insights on Outage Probability and Diversity Gain,

    W. K. New, K. K. Wong, X. Hao, K. F. Tong, and C. B. Chae, “Fluid Antenna System: New Insights on Outage Probability and Diversity Gain,”IEEE Transactions on Wireless Communications, vol. 23, No. 1, pp. 128–140, Jan. 2024

  5. [12]

    Fluid Antenna System for 6G: When Bruce Lee Inspires Wireless Communications,

    K. K. Wong, K. F. Tong, Y . Zhang, and Z. Zheng, “Fluid Antenna System for 6G: When Bruce Lee Inspires Wireless Communications,” Electronics Letters, vol. 56, no. 24, pp. 1288–1290, Nov. 2020

  6. [13]

    Flexible-Position MIMO for Wireless Communications: Fundamentals, Challenges, and Future Directions,

    J. Zheng, J. Zhang, H. Du, D. Niyato, S. Sun, B. Ai, and K. B. Letaief, “Flexible-Position MIMO for Wireless Communications: Fundamentals, Challenges, and Future Directions,”IEEE Wireless Communications, vol. 31, no. 5, pp. 18–26, Oct. 2024

  7. [14]

    Fluid Antenna Systems Enabling 6G: Principles, Applications, and Research Directions,

    T. Wu, K. Zhi, J. Yao, X. Lai, J. Zheng, H. Niu, M. Elkashlan, K. K. Wong, C. B. Chae, Z. Ding, et al., “Fluid Antenna Systems Enabling 6G: Principles, Applications, and Research Directions,”arXiv preprint arXiv:2412.03839, 2024

  8. [15]

    MIMO Evolution Beyond 5G Through Reconfigurable Intelligent Surfaces and Fluid Antenna Systems,

    A. Shojaeifard, K. K. Wong, K. F. Tong, Z. Chu, A. Mourad, A. Haghighat, I. Hemadeh, N. T. Nguyen, V . Tapio, and M. Juntti, “MIMO Evolution Beyond 5G Through Reconfigurable Intelligent Surfaces and Fluid Antenna Systems,”Proceedings of the IEEE, vol. 110, no. 9, pp. 1244–1265...

  9. [16]

    Fluid Antenna Multiple Access,

    K. K. Wong and K. F. Tong, “Fluid Antenna Multiple Access,”IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 4801– 4815, Jul. 2022

  10. [18]

    6-D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UA V Communications,

    T. Ren, X. Zhang, L. Zhu, W. Ma, X. Gao, and R. Zhang, “6-D Movable Antenna Enhanced Interference Mitigation for Cellular-Connected UA V Communications,”IEEE Wireless Communications Letters, vol. 14, no. 6, pp. 1618–1622, Jun. 2025

  11. [19]

    Bruce Lee Inspired Fluid Antenna System: Six Research Topics and the Potentials for 6G,

    K. K. Wong, K. F. Tong, Y . Shen, Y . Chen, and Y . Zhang, “Bruce Lee Inspired Fluid Antenna System: Six Research Topics and the Potentials for 6G,”Frontiers in Communications and Networks, vol. 3, no. 853416, Mar. 2022

  12. [20]

    A Survey on Fluid Antenna Multiple Access for 6G: A New Multiple Access Technology That Provides Great Diversity in a Small Space,

    A. F. M. S. Shah, M. Ali Karabulut, E. Cinar, and K. M. Rabie, “A Survey on Fluid Antenna Multiple Access for 6G: A New Multiple Access Technology That Provides Great Diversity in a Small Space,” IEEE Access, vol. 12, pp. 88410–88425, Jun. 2024

  13. [21]

    AI-Empowered Fluid Antenna Systems: Opportunities, Challenges, and Future Directions,

    C. Wang, Z. Li, K. K. Wong, R. Murch, C. B. Chae, and S. Jin, “AI-Empowered Fluid Antenna Systems: Opportunities, Challenges, and Future Directions,”IEEE Wireless Communications, vol. 31, no. 5, pp. 34–41, Oct. 2024

  14. [22]

    Integrated Sensing and Communication Meets Smart Propagation En- gineering: Opportunities and Challenges,

    K. Meng, C. Masouros, K. K. Wong, A. P. Petropulu, and L. Hanzo, “Integrated Sensing and Communication Meets Smart Propagation En- gineering: Opportunities and Challenges,”IEEE Network, vol. 39, no. 2, pp. 278–285, Mar. 2025

  15. [23]

    A Tutorial on Fluid Antenna System for 6G Networks: Encom- passing Communication Theory, Optimization Methods and Hardware Designs,

    W. K. New, K. K. Wong, H. Xu, C. Wang, F. R. Ghadi, J. Zhang, J. Rao, R. Murch, P. Ramirez-Espinosa, D. Morales-Jimenez, C. B. Chae, and K. F. Tong, “A Tutorial on Fluid Antenna System for 6G Networks: Encom- passing Communication Theory, Optimization Methods and Hardware Desi...

  16. [24]

    A Tutorial on Movable Antennas for Wireless Networks,

    L. Zhu, W. Ma, W. Mei, Y . Zeng, Q. Wu, B. Ning, Z. Xiao, X. Shao, J. Zhang, and R. Zhang, “A Tutorial on Movable Antennas for Wireless Networks,”IEEE Communications Surveys & Tutorials, early access, doi: 10.1109/COMST.2025.3546373

  17. [25]

    A Tutorial on Six-Dimensional Movable Antenna for 6G Networks: Synergizing Positionable and Rotatable Antennas,

    X. Shao, W. Mei, C. You, Q. Wu, B. Zheng, C. X. Wang, J. Li, R. Zhang, R. Schober, L. Zhu, W. Zhuang, and X. Shen, “A Tutorial on Six-Dimensional Movable Antenna for 6G Networks: Synergizing Positionable and Rotatable Antennas,”IEEE Communications Surveys & Tutorials, early ac...

  18. [26]

    Non-Terrestrial Networks in 5G & Beyond: A Survey,

    F. Rinaldi, H.-L. Maattanen, J. Torsner, S. Pizzi, S. Andreev, A. Iera, Y . Koucheryavy, and G. Araniti, “Non-Terrestrial Networks in 5G & Beyond: A Survey,”IEEE Access, vol. 8, pp. 165178–165200, Sept. 2020

  19. [27]

    Study on New Radio to Support Non- Terrestrial Networks,

    3rd Generation Partnership Project (3GPP); Technical Specification Group Radio Access Network, “Study on New Radio to Support Non- Terrestrial Networks,”Release 15, 3GPP TR 38.811 V15.4.0, Oct. 2020

  20. [28]

    Enhanced LTE support for aerial ve- hicles,

    3rd Generation Partnership Project (3GPP); Technical Specification Group Radio Access Network, “Enhanced LTE support for aerial ve- hicles,”Release 15, 3GPP TR 36.777 V15.0.0, Jan. 2018

  21. [29]

    Solutions for NR to support non- terrestrial networks,

    3rd Generation Partnership Project (3GPP); Technical Specification Group Radio Access Network, “Solutions for NR to support non- terrestrial networks,”Release 16, 3GPP TR 38.821 V16.2.0, Apr. 2023

  22. [30]

    A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles,

    W. Khawaja, I. Guvenc, D. W. Matolak, U. C. Fiebig, and N. Schnecken- burger, “A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles,”IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2361–2391, Thirdquarter 2019

  23. [31]

    A Survey of Channel Modeling for UA V Communications,

    A. A. Khuwaja, Y . Chen, N. Zhao, M. S. Alouini, and P. Dobbins, “A Survey of Channel Modeling for UA V Communications,”IEEE Communications Surveys & Tutorials, vol. 20, no. 4, pp. 2804–2821, Fourthquarter 2018

  24. [32]

    UA V-Assisted Data Collection for Internet of Things: A Survey,

    Z. Wei, M. Zhu, N. Zhang, L. Wang, Y . Zou, Z. Meng, H. Wu, and Z. Feng, “UA V-Assisted Data Collection for Internet of Things: A Survey,” IEEE Internet of Things Journal, vol. 9, no. 17, pp. 15460–15483, Sept. 2022

  25. [33]

    UA V-Enabled Integrated Sensing and Communication: Opportunities and Challenges,

    K. Meng, Q. Wu, J. Xu, W. Chen, Z. Feng, R. Schober, and A. L. Swindlehurst, “UA V-Enabled Integrated Sensing and Communication: Opportunities and Challenges,”IEEE Wireless Communications, vol. 31, no. 2, pp. 97–104, Apr. 2024

  26. [34]

    A Survey on the Convergence of Edge Computing and AI for UA Vs: Opportunities and Challenges,

    P. McEnroe, S. Wang, and M. Liyanage, “A Survey on the Convergence of Edge Computing and AI for UA Vs: Opportunities and Challenges,” IEEE Internet of Things Journal, vol. 9, no. 17, pp. 15435–15459, Sept. 2022. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, SEPTEMBER 2025 27

  27. [35]

    UA V-Enabled Wireless Power Transfer: A Tutorial Overview,

    L. Xie, X. Cao, J. Xu, and R. Zhang, “UA V-Enabled Wireless Power Transfer: A Tutorial Overview,”IEEE Transactions on Green Commu- nications and Networking, vol. 5, no. 4, pp. 2042–2064, Dec. 2021

  28. [36]

    A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future,

    G. Karabulut Kurt, M. G. Khoshkholgh, S. Alfattani, A. Ibrahim, T. S. J. Darwish, M. S. Alam, H. Yanikomeroglu, and A. Yongacoglu, “A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future,”IEEE Communications Surveys & Tutorials, vol. 23, no...

  29. [37]

    Satellite Communica- tions in the New Space Era: A Survey and Future Challenges,

    O. Kodheli, E. Lagunas, N. Maturo, S. K. Sharma, B. Shankar, J. F. M. Montoya, J. C. M. Duncan, D. Spano, S. Chatzinotas, S. Kisseleff, J. Querol, L. Lei, T. X. Vu, and G. Goussetis, “Satellite Communica- tions in the New Space Era: A Survey and Future Challenges,”IEEE Communi...

  30. [38]

    Integrated Communications and Localization for Massive MIMO LEO Satellite Systems,

    L. You, X. Qiang, Y . Zhu, F. Jiang, C. G. Tsinos, W. Wang, H. Wymeersch, X. Gao, and B. Ottersten, “Integrated Communications and Localization for Massive MIMO LEO Satellite Systems,”IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 11061– 11075, Sept. 2024

  31. [39]

    Multiple-User Transmission in Space Information Networks: Architecture and Key Techniques,

    X. Zhang, L. Zhu, T. Li, Y . Xia, and W. Zhuang, “Multiple-User Transmission in Space Information Networks: Architecture and Key Techniques,”IEEE Wireless Communications, vol. 26, no. 2, pp. 17– 23, Apr. 2019

  32. [40]

    Nonterrestrial Commu- nications Assisted by Reconfigurable Intelligent Surfaces,

    J. Ye, J. Qiao, A. Kammoun, and M. S. Alouini, “Nonterrestrial Commu- nications Assisted by Reconfigurable Intelligent Surfaces,”Proceedings of the IEEE, vol. 110, no. 9, pp. 1423–1465, May 2022

  33. [41]

    Service Coordination in the Space-Air-Ground Integrated Network,

    Y . Guo, Q. Li, Y . Li, N. Zhang, and S. Wang, “Service Coordination in the Space-Air-Ground Integrated Network,”IEEE Network, vol. 35, no. 5, pp. 168–173, Nov. 2021

  34. [42]

    HAP- Assisted Multi-Aerial Base Station Deployment for Capacity Enhance- ment via Federated Deep Reinforcement Learning,

    L. Liu H. He, F. Qi, Y . Zhao, W. Xie, F. Zhou, and L. Feng, “HAP- Assisted Multi-Aerial Base Station Deployment for Capacity Enhance- ment via Federated Deep Reinforcement Learning,”Journal of Cloud Computing, vol. 12, no. 1, Sept. 2023

  35. [43]

    HAPS in the Non- Terrestrial Network Nexus: Prospective Architectures and Performance Insights,

    Z. Lou, B. E. Y . Belmekki, and M. S. Alouini, “HAPS in the Non- Terrestrial Network Nexus: Prospective Architectures and Performance Insights,”IEEE Wireless Communications, vol. 30, no. 6, pp. 52–58, Dec. 2023

  36. [44]

    Toward Liquid Reconfigurable Antenna Arrays for Wireless Communications,

    J. O. Mart ´ınez, J. R. Rodr ´ıguez, Y . Shen, K. F. Tong, K. K. Wong, and A. G. Armada, “Toward Liquid Reconfigurable Antenna Arrays for Wireless Communications,”IEEE Communications Magazine, vol. 60, no. 12, pp. 145–151, Dec. 2022

  37. [45]

    Closed-Form Expressions for Spatial Correlation Parameters for Performance Analysis of Fluid Antenna Systems,

    K. Wong, K. Tong, Y . Chen, and Y . Zhang, “Closed-Form Expressions for Spatial Correlation Parameters for Performance Analysis of Fluid Antenna Systems,”Electronics Letters, vol. 58, no. 11, pp. 454–457, Apr. 2022

  38. [46]

    A New Spatial Block-Correlation Model for Fluid Antenna Systems,

    P. Ram ´ırez-Espinosa, D. Morales-Jimenez, and K. K. Wong, “A New Spatial Block-Correlation Model for Fluid Antenna Systems,”IEEE Transactions on Wireless Communications, vol. 23, no. 11, pp. 15829– 15843, Nov. 2024

  39. [47]

    A New Analytical Approximation of the Fluid Antenna System Channel,

    M. Khammassi, A. Kammoun, and M.-S. Alouini, “A New Analytical Approximation of the Fluid Antenna System Channel,”IEEE Transac- tions on Wireless Communications, vol. 22, no. 12, pp. 8843–8858, Dec. 2023

  40. [48]

    Design and Optimization of Multiport Pixel Antennas,

    S. Soltani, P. Lotfi, and R. D. Murch, “Design and Optimization of Multiport Pixel Antennas,”IEEE Transactions on Antennas and Propagation, vol. 66, no. 4, pp. 2049–2054, Apr. 2018

  41. [49]

    Pixel Antenna Optimization Using N-Port Characteristic Mode Analysis,

    F. Jiang, C. Y . Chiu, S. Shen, Q. S. Cheng, and R. Murch, “Pixel Antenna Optimization Using N-Port Characteristic Mode Analysis,” IEEE Transactions on Antennas and Propagation, vol. 68, no. 5, pp. 3336–3347, May 2020

  42. [50]

    Pro- grammable meta-fluid antenna for spatial multiplexing in fast fluctuating radio channels,

    B. Liu, K. F. Tong, K. K. Wong, C. B. Chae, and H. Wong, “Pro- grammable meta-fluid antenna for spatial multiplexing in fast fluctuating radio channels,”Optics Express, vol. 33, no. 13, pp. 28898–28915, 2025

  43. [52]

    Movable Antennas for Wireless Communication: Opportunities and Challenges,

    L. Zhu, W. Ma, and R. Zhang, “Movable Antennas for Wireless Communication: Opportunities and Challenges,”IEEE Communications Magazine, vol. 62, no. 6, pp. 114–120, Jun. 2024

  44. [53]

    6D Movable Antenna Based on User Distribution: Modeling and Optimization,

    X. Shao, Q. Jiang, and R. Zhang, “6D Movable Antenna Based on User Distribution: Modeling and Optimization,”IEEE Transactions on Wireless Communications, vol. 24, no. 1, pp. 355–370, Jan. 2025

  45. [54]

    Performance Limits of Fluid Antenna Systems,

    K. K. Wong, A. Shojaeifard, K. F. Tong, and Y . Zhang, “Performance Limits of Fluid Antenna Systems,”IEEE Communications Letters, vol. 24, no. 11, pp. 2469–2472, Nov. 2020

  46. [55]

    Energy Efficiency Maximization Under Delay-Outage Probability Constraints Using Fluid Antenna Systems,

    Y . Xu, Y . Chen, Y . Hou, K. K. Wong, Q. Cui, and X. Tao, “Energy Efficiency Maximization Under Delay-Outage Probability Constraints Using Fluid Antenna Systems,” inProceeding of the 2023 IEEE Statis- tical Signal Processing Workshop (SSP), pp. 105–109, Hanoi, Vietnam, Jul. 2023

  47. [56]

    Fluid Antenna System Enhancing Orthogonal and Non-Orthogonal Multiple Access,

    W. K. New, K. K. Wong, H. Xu, K. F. Tong, C. B. Chae, and Y . Zhang, “Fluid Antenna System Enhancing Orthogonal and Non-Orthogonal Multiple Access,”IEEE Communications Letters, vol. 28, no. 1, pp. 218–222, Jan. 2024

  48. [57]

    MIMO Capacity Characterization for Movable Antenna Systems,

    W. Ma, L. Zhu, and R. Zhang, “MIMO Capacity Characterization for Movable Antenna Systems,”IEEE Transactions on Wireless Communi- cations, vol. 23, no. 4, pp. 3392–3407, Apr. 2024

  49. [58]

    Transmit and Receive Antenna Port Selection for Channel Capacity Maximization in Fluid-MIMO Systems,

    C. N. Efrem and I. Krikidis, “Transmit and Receive Antenna Port Selection for Channel Capacity Maximization in Fluid-MIMO Systems,” IEEE Wireless Communications Letters, vol. 13, no. 11, pp. 3202-3206, Nov. 2024

  50. [59]

    6DMA Enhanced Wireless Network with Flexible Antenna Position and Rotation: Opportunities and Challenges,

    Shao X and Zhang R., “6DMA Enhanced Wireless Network with Flexible Antenna Position and Rotation: Opportunities and Challenges,” IEEE Communications Magazine, vol. 63, no. 4, pp. 121–128, Apr. 2025

  51. [60]

    Movable-Antenna Array Enhanced Beamforming: Achieving Full Array Gain with Null Steering,

    L. Zhu, W. Ma, and R. Zhang, “Movable-Antenna Array Enhanced Beamforming: Achieving Full Array Gain with Null Steering,”IEEE Communications Letters, vol. 27, no. 12, pp. 3340–3344, Dec. 2023

  52. [61]

    Joint Beamforming and Antenna Movement Design for Moveable Antenna Systems Based on Statistical CSI,

    X. Chen, B. Feng, Y . Wu, D. W. Kwan Ng, and R. Schober, “Joint Beamforming and Antenna Movement Design for Moveable Antenna Systems Based on Statistical CSI,” inProceeding of the 2023 IEEE Global Communications Conference (GLOBECOM 2023), pp. 4387– 4392, Kuala Lumpur, Malaysi...

  53. [62]

    Dynamic Channel Modeling of Fluid Antenna Systems in UA V Com- munications,

    H. Jiang, W. Shi, Z. Chen, Z. Zhang, K. K. Wong, and H. Shin, “Dynamic Channel Modeling of Fluid Antenna Systems in UA V Com- munications,”IEEE Wireless Communications Letters, vol. 14, no. 10, pp. 3169–3173, Oct. 2025

  54. [63]

    Fast Fluid Antenna Multiple Access Enabling Massive Connectivity,

    K. K. Wong, K. F. Tong, Y . Chen, and Y . Zhang, “Fast Fluid Antenna Multiple Access Enabling Massive Connectivity,”IEEE Communications Letters, vol. 27, no. 2, pp. 711–715, Feb. 2023

  55. [64]

    Port Selection for Fluid Antenna Systems,

    Z. Chai, K. K. Wong, K. F. Tong, Y . Chen, and Y . Zhang, “Port Selection for Fluid Antenna Systems,”IEEE Communications Letters, vol. 26, no. 5, pp. 1180–1184, May 2022

  56. [65]

    Fast Port Selection Using Temporal and Spatial Correlation for Fluid Antenna Systems,

    S. Zhang, J. Mao, Y . Hou, Y . Chen, K. K. Wong, Q. Cui, and X. Tao, “Fast Port Selection Using Temporal and Spatial Correlation for Fluid Antenna Systems,” inProceeding of the 2023 IEEE Statistical Signal Processing Workshop (SSP), pp. 95–99, Hanoi, Vietnam, Jul. 2023

  57. [66]

    Deep Learning Enabled Slow Fluid Antenna Multiple Access,

    N. Waqar, K. K. Wong, K. F. Tong, A. Sharples, and Y . Zhang, “Deep Learning Enabled Slow Fluid Antenna Multiple Access,”IEEE Communications Letters, vol. 27, no. 3, pp. 861–865, Mar. 2023

  58. [67]

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

    H. He, C. K. 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

  59. [68]

    cGAN-Based Slow Fluid Antenna Multiple Access,

    M. Eskandari, A. G. Burr, K. Cumanan, and K. K. Wong, “cGAN-Based Slow Fluid Antenna Multiple Access,”IEEE Wireless Communications Letters, vol. 13, no. 10, pp. 2907–2911, Oct. 2024

  60. [69]

    Virtual FAS by Learning-Based Imaginary Antennas,

    K. K. Wong, C. Wang, H. Zhang, G. Li, C. Wang, C. B. Chae, and R. Murch, “Virtual FAS by Learning-Based Imaginary Antennas,”IEEE Wireless Communications Letters, vol. 13, no. 6, pp. 1581–1585, Jun. 2024

  61. [70]

    Learning-Induced Channel Extrapolation for Fluid Antenna Systems Using Asymmetric Graph Masked Autoencoder,

    H. Zhang, J. Wang, C. Wang, C. Wang, K. K. Wong, B. Wang, and C. B. Chae, “Learning-Induced Channel Extrapolation for Fluid Antenna Systems Using Asymmetric Graph Masked Autoencoder,”IEEE Wireless Communications Letters, vol. 13, no. 6, pp. 1665–1669, Jun. 2024

  62. [71]

    Accurate and Fast Channel Estimation for Fluid Antenna Systems with Diffusion Models,

    E. Tang, W. Guo, H. He, S. Song, J. Zhang, and K. B. Letaief, “Accurate and Fast Channel Estimation for Fluid Antenna Systems with Diffusion Models,”arXiv preprint arXiv:2505.04930, 2025

  63. [72]

    Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Ap- proach,

    C. He, Y . Lu, W. Chen, B. Ai, K. K. Wong, and D. Niyato, “Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Ap- proach,”IEEE Transactions on V ehicular Technology, early access, doi: 10.1109/TVT.2025.3570319

  64. [73]

    Fluid Antenna with Linear MMSE Channel Estimation for Large-scale cellular networks,

    C. Skouroumounis and I. Krikidis, “Fluid Antenna with Linear MMSE Channel Estimation for Large-scale cellular networks,”IEEE Transac- tions on Communications, vol. 71, no. 2, pp. 1112–1125, Feb. 2023

  65. [74]

    Channel Estimation for FAS-Assisted Multiuser mmWave Systems,

    H. Xu, G. Zhou, K. K. Wong, W. K. New, C. Wang, C. B. Chae, R. Murch, S. Jin, and Y . Zhang, “Channel Estimation for FAS-Assisted Multiuser mmWave Systems,”IEEE Communications Letters, vol. 28, no. 3, pp. 632-636, Mar. 2024

  66. [75]

    Estimation of Channel Parameters for Port Selection in Millimeter-Wave Fluid Antenna Systems,

    R. Wang, Y . Chen, Y . Hou, K. K. Wong, and X. Tao, “Estimation of Channel Parameters for Port Selection in Millimeter-Wave Fluid Antenna Systems,” inProceeding of the 2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops), pp. 1–6, Dalian, China, 2...

  67. [76]

    Compressed Sensing Based Channel Estimation for Movable Antenna Communications,

    W. Ma, L. Zhu, and R. Zhang, “Compressed Sensing Based Channel Estimation for Movable Antenna Communications,”IEEE Communica- tions Letters, vol. 27, no. 10, pp. 2747–2751, Oct. 2023

  68. [77]

    Channel Estimation for Movable Antenna Communication Systems: A Framework Based on Compressed Sensing,

    Z. Xiao, S. Cao, L. Zhu, Y . Liu, B. Ning, X. Xia, and R. Zhang, “Channel Estimation for Movable Antenna Communication Systems: A Framework Based on Compressed Sensing,”IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 11814–11830, Sept. 2024

  69. [78]

    Successive Bayesian Reconstructor for Channel Estimation in Fluid Antenna Systems,

    Z. Zhang, J. Zhu, L. Dai, and R. W. Heath, “Successive Bayesian Reconstructor for Channel Estimation in Fluid Antenna Systems,”IEEE Transactions on Wireless Communications, vol. 24, no. 3, pp. 1992– 2006, Mar. 2025

  70. [79]

    Transforming Time- Varying to Static Channels: The Power of Fluid Antenna Mobility,

    W. Li, H. Yin, F. Fu, Y . Cao, and M. Debbah, “Transforming Time- Varying to Static Channels: The Power of Fluid Antenna Mobility,”IEEE Transactions on Wireless Communications, vol. 24, no. 6, pp. 4767– 4780, Jun. 2025

  71. [80]

    Channel Estimation and Reconstruction in Fluid Antenna System: Oversampling is Essential,

    W. K. New, K. K. Wong, H. Xu, F. Rostami Ghadi, R. Murch, and C. B. Chae, “Channel Estimation and Reconstruction in Fluid Antenna System: Oversampling is Essential,”IEEE Transactions on Wireless Communications, vol. 24, no. 1, pp. 309–322, Jan. 2025

  72. [81]

    Transformer based Collaborative Reinforcement Learning for Fluid Antenna System (FAS)-enabled 3D UA V Positioning,

    X. Xu, H. Xu, D. Wei, W. Saad, M. Bennis, and M. Chen, “Transformer based Collaborative Reinforcement Learning for Fluid Antenna System (FAS)-enabled 3D UA V Positioning,”arXiv preprint arXiv:2507.09094, 2025

  73. [82]

    Capacity Maximization of Uplink with Fluid Antenna System at Both Ends,

    B. Tang, H. Xu, K. K. Wong, L. You, W. K. New, and C. B. Chae, “Capacity Maximization of Uplink with Fluid Antenna System at Both Ends,”IEEE Transactions on Wireless Communications, early access, doi: 10.1109/TWC.2025.3580506

  74. [83]

    Multiuser Communi- cations with Movable-Antenna Base Station: Joint Antenna Positioning, Receive Combining, and Power Control,

    Z. Xiao, X. Pi, L. Zhu, X. G. Xia, and R. Zhang, “Multiuser Communi- cations with Movable-Antenna Base Station: Joint Antenna Positioning, Receive Combining, and Power Control,”IEEE Transactions on Wireless Communications, vol. 23, no. 12, pp. 19744–19759, Dec. 2024

  75. [84]

    Uplink Transmission Design for Fluid Antenna-Enabled Multiuser MIMO Systems with Imperfect CSI,

    L. Hu, L. Li, C. Pan, and H. Ren, “Uplink Transmission Design for Fluid Antenna-Enabled Multiuser MIMO Systems with Imperfect CSI,” arXiv preprint arXiv:2503.01668, 2025

  76. [85]

    Fluid Antenna-Assisted MIMO Transmission Exploiting Statistical CSI,

    Y . Ye, L. You, J. Wang, H. Xu, K. K. Wong, and X. Gao, “Fluid Antenna-Assisted MIMO Transmission Exploiting Statistical CSI,”IEEE Communications Letters, vol. 28, no. 1, pp. 223–227, Jan. 2024

  77. [86]

    An Information-Theoretic Characterization of MIMO-FAS: Optimization, Diversity-Multiplexing Tradeoff and q-Outage Capacity,

    W. K. New, K. K. Wong, H. Xu, K. F. Tong, and C. B. Chae, “An Information-Theoretic Characterization of MIMO-FAS: Optimization, Diversity-Multiplexing Tradeoff and q-Outage Capacity,”IEEE Transac- tions on Wireless Communications, vol. 23, no. 6, pp. 5541–5556, Jun. 2024

  78. [87]

    Sum-Rate Maximization for Fluid Antenna Enabled Multiuser Communications,

    Z. Cheng, N. Li, J. Zhu, X. She, C. Ouyang, and P. Chen, “Sum-Rate Maximization for Fluid Antenna Enabled Multiuser Communications,” IEEE Communications Letters, vol. 28, no. 5, pp. 1206-1210, May 2024

  79. [88]

    Rethinking Hardware Impairments in Multi-User Systems: Can FAS Make a Difference?

    J. Yao, T. Wu, L. Zhou, M. Jin, C. Pan, M. Elkashlan, F. Adachi, G. K. Karagiannidis, N. Al-Dhahir, and C. Yuen, “Rethinking Hardware Impairments in Multi-User Systems: Can FAS Make a Difference?” arXiv preprint arXiv:2412.15843, 2024

  80. [89]

    Movable Antenna-Aided Hybrid Beamforming for Multi-User Communications,

    Y . Zhang, Y . Zhang, L. Zhu, S. Xiao, W. Tang, Y . C. Eldar, and R. Zhang, “Movable Antenna-Aided Hybrid Beamforming for Multi-User Communications,”IEEE Transactions on V ehicular Technology, vol. 74, no. 6, pp. 9899–9903, Jun. 2025

  81. [90]

    Optimizing Reconfigurable Antenna MIMO Systems with Coherent Ising Machines,

    I. Krikidis, A. K. Singh, and K. Jamieson, “Optimizing Reconfigurable Antenna MIMO Systems with Coherent Ising Machines,” inProceeding of the 2024 IEEE International Conference on Communications Work- shops (ICC Workshops), pp. 1134–1139, Denver, USA, 2024

  82. [91]

    Learning-Based Joint Beamforming and Antenna Movement Design for Movable Antenna Systems,

    C. Weng, Y . Chen, L. Zhu, and Y . Wang, “Learning-Based Joint Beamforming and Antenna Movement Design for Movable Antenna Systems,”IEEE Wireless Communications Letters, vol. 13, no. 8, pp. 2120–2124, Aug. 2024

  83. [92]

    Fluid Antenna Relay Assisted Communication Systems Through An- tenna Location Optimization,

    R. Xu, Y . Chen, J. Kang, M. Xu, Z. Yang, C. Huang, and N. Dusit, “Fluid Antenna Relay Assisted Communication Systems Through An- tenna Location Optimization,” inProceeding of the IEEE International Conference on Communications Workshops (ICC Workshops), pp. 1140– 1145, Denver...

  84. [93]

    Energy Efficient Fluid Antenna Relay (FAR)-Assisted Wireless Communications,

    R. Xu, Z. Yang, Z. Zhang, M. Shikh-Bahaei, K. Huang, and D. Niyato, “Energy Efficient Fluid Antenna Relay (FAR)-Assisted Wireless Communications,”IEEE Journal on Selected Areas in Communications, early access, doi: 10.1109/JSAC.2025.3617892

  85. [94]

    UA V-Mounted Movable Antenna: Joint Optimization of UA V Placement and Antenna Config- uration,

    X. Tang, Y . Shi, Y . Huang, and Q. Wu, “UA V-Mounted Movable Antenna: Joint Optimization of UA V Placement and Antenna Config- uration,”arXiv preprint arXiv:2409.02469, 2024

  86. [95]

    Robust Design for Movable-Antenna Array Enabled UA V Communications With Jittering,

    H. Mao, L. Zhu, X. Pi, Z. Xiao, X. G. Xia, and R. Zhang, “Robust Design for Movable-Antenna Array Enabled UA V Communications With Jittering,”IEEE Wireless Communications Letters, early access, doi: 10.1109/LWC.2025.3595114

  87. [96]

    Wireless Communication for Low-Altitude Economy with UA V Swarm Enabled Two-Level Movable Antenna System,

    H. Lu, Y . Zeng, S. Ma, B. Li, S. Jin, and R. Zhang, “Wireless Communication for Low-Altitude Economy with UA V Swarm Enabled Two-Level Movable Antenna System,”arXiv preprint arXiv:2505.22286, 2025

  88. [97]

    Movable-Antenna Empowered AA V-Enabled Data Collection over Low-Altitude Wireless Networks,

    X. Zhang, W. Liu, J. Ren, C. Wang, H. Xing, Y . Shen, and S. Cui, “Movable-Antenna Empowered AA V-Enabled Data Collection over Low-Altitude Wireless Networks,”arXiv preprint arXiv:2507.15515, 2025

  89. [98]

    UA V- Enabled Wireless Networks With Movable-Antenna Array: Flexible Beamforming and Trajectory Design,

    W. Liu, X. Zhang, H. Xing, J. Ren, Y . Shen, and S. Cui, “UA V- Enabled Wireless Networks With Movable-Antenna Array: Flexible Beamforming and Trajectory Design,”IEEE Wireless Communications Letters, vol. 14, no. 3, pp. 566–570, Mar. 2025

  90. [99]

    Movable Antenna Array for Improving UA V Relaying Networks,

    W. Zhou, D. Yang, Y . Xu, L. Xiao, F. Wu, and T. Zhang, “Movable Antenna Array for Improving UA V Relaying Networks,”IEEE Wireless Communications Letters, doi: 10.1109/LWC.2025.3614489

  91. [100]

    Movable Antenna- Equipped UA V for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-Based Approach,

    Y . Bai, B. Xie, R. Zhu, Z. Chang, and R. J ¨antti, “Movable Antenna- Equipped UA V for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-Based Approach,” inProceeding of the IEEE International Conference on Communications, Montreal (ICC 2025), pp. ...

  92. [101]

    Per- formance Analysis of Fluid Antenna System Aided OTFS Satellite Communications,

    H. Yang, M. Derakhshani, S. Lambotharan, and L. Hanzo, “Per- formance Analysis of Fluid Antenna System Aided OTFS Satellite Communications,”arXiv preprint arXiv:2505.09028, 2025, 2025

  93. [103]

    Fluid Antenna Enabled Compact Ultra Massive Antenna Array for Satellite Commu- nications,

    T. Han, Y . Zhu, G. Zheng, and P. D. Arapoglou, “Fluid Antenna Enabled Compact Ultra Massive Antenna Array for Satellite Commu- nications,”IEEE Journal on Selected Areas in Communications, doi: 10.1109/JSAC.2025.3616279

  94. [104]

    Power-Efficient Full-Duplex Satellite Communications Aided by Movable Antennas,

    L. Lin, J. Ding, Z. Zhou, and B. Jiao, “Power-Efficient Full-Duplex Satellite Communications Aided by Movable Antennas,”IEEE Wireless Communications Letters, vol. 14, no. 3, pp. 656–660, Mar. 2025

  95. [105]

    Deep Energy-Efficient Optimization Network for URLLC Over Cell-Free Massive MIMO,

    D. Li, J. Li, D. Niyato, W. Feng, and W. Jiang, “Deep Energy-Efficient Optimization Network for URLLC Over Cell-Free Massive MIMO,” IEEE Internet of Things Journal, vol. 12, no. 12, pp. 20973–20987, Jun. 2025

  96. [106]

    User-Centric Cell-Free Massive MIMO with Access Points Empowered by Fluid Antennas,

    M. Olyaee and S. Buzzi, “User-Centric Cell-Free Massive MIMO with Access Points Empowered by Fluid Antennas,” inProceeding of the IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPA WC), pp. 666–670, Lucca, Italy, 2024

  97. [107]

    6DMA-Aided Cell- Free Massive MIMO Communication,

    X. Shi, X. Shao, B. Zheng, and R. Zhang, “6DMA-Aided Cell- Free Massive MIMO Communication,”IEEE Wireless Communications Letters, vol. 14, no. 5, pp. 1361–1365, May 2025

  98. [108]

    Movable Antenna-Enabled Co-Frequency Co-Time Full-Duplex Wireless Com- munication,

    J. Ding, Z. Zhou, W. Li, C. Wang, L. Lin, and B. Jiao, “Movable Antenna-Enabled Co-Frequency Co-Time Full-Duplex Wireless Com- munication,”IEEE Communications Letters, vol. 28, no. 10, pp. 2412– 2416, Oct. 2024

  99. [109]

    Fluid Antenna-Aided Full Duplex Communications: A Macroscopic Point-of-View,

    C. Skouroumounis and I. Krikidis, “Fluid Antenna-Aided Full Duplex Communications: A Macroscopic Point-of-View,”IEEE Journal on Selected Areas in Communications, vol. 41, no. 9, pp. 2879–2892, Sept. 2023

  100. [110]

    Full-Duplex Cooperative NOMA-based mmWave Networks with Fluid Antenna System (FAS) Receivers,

    L. Tlebaldiyeva, S. Arzykulov, T. A. Tsiftsis, and G. Nauryzbayev, “Full-Duplex Cooperative NOMA-based mmWave Networks with Fluid Antenna System (FAS) Receivers,” inProceeding of the 2023 In- ternational Balkan Conference on Communications and Networking (BalkanCom), pp. 1–6, ...

  101. [111]

    Secure Full- Duplex Communication via Movable Antennas,

    J. Ding, Z. Zhou, C. Wang, W. Li, L. Lin, and B. Jiao, “Secure Full- Duplex Communication via Movable Antennas,” inProceeding of the 2024 IEEE Global Communications Conference (GLOBECOM 2024), pp. 885-890, Cape Town, South Africa, 2024

  102. [112]

    Movable Antenna-Aided Secure Full- Duplex Multi-User Communications,

    J. Ding, Z. Zhou and B. Jiao, “Movable Antenna-Aided Secure Full- Duplex Multi-User Communications,”IEEE Transactions on Wireless Communications, vol. 24, no. 3, pp. 2389–2403, Mar. 2025

  103. [113]

    Sum-Rate Maximization for UA V Relay-Aided Fluid Antenna System with NOMA,

    S. B. S. Abdou, W. K. New, C. Y . Leow, S. Won, K. K. Wong, and Z. Ding, “Sum-Rate Maximization for UA V Relay-Aided Fluid Antenna System with NOMA,” inProceeding of the IEEE 7th International Symposium on Telecommunication Technologies (ISTT), pp. 53–58, Langkawi Island, Mala...

  104. [114]

    Movable Antennas Enabled Wireless- Powered NOMA: Continuous and Discrete Positioning Designs,

    Y . Gao, Q. Wu, and W. Chen, “Movable Antennas Enabled Wireless- Powered NOMA: Continuous and Discrete Positioning Designs,”arXiv preprint arXiv:2409.20485, 2024

  105. [115]

    Slow Fluid Antenna Multiple Access,

    K. K. Wong, D. Morales-Jimenez, K. F. Tong, and C. B. Chae, “Slow Fluid Antenna Multiple Access,”IEEE Transactions on Communica- tions, vol. 71, no. 5, pp. 2831–2846, May 2023. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, SEPTEMBER 2025 29

  106. [116]

    Compact Ultra Massive Antenna Array: A Simple Open-Loop Massive Connectivity Scheme,

    K. K. Wong, C. B. Chae, and K. F. Tong, “Compact Ultra Massive Antenna Array: A Simple Open-Loop Massive Connectivity Scheme,” IEEE Transactions on Wireless Communications, vol. 23, no. 6, pp. 6279–6294, Jun. 2024

  107. [117]

    Transmitter CSI-Free RIS-Randomized CUMA for Extreme Massive Connectivity,

    K. K. Wong, “Transmitter CSI-Free RIS-Randomized CUMA for Extreme Massive Connectivity,”IEEE Open Journal of the Communi- cations Society, vol. 5, pp. 6890–6902, Oct. 2024

  108. [118]

    On Rate Performance of M- Ary Amplitude Shift Keying Compact Ultra Massive Array Systems for Massive Connectivity,

    K. K. Wong, C. B. Chae, and Y . Zhang, “On Rate Performance of M- Ary Amplitude Shift Keying Compact Ultra Massive Array Systems for Massive Connectivity,”Electronics Letters, vol. 60, no. 5, Mar. 2024

  109. [119]

    Compact Ultra Massive Array (CUMA) with 4 RF Chains for Massive Connectivity,

    K. K. Wong, “Compact Ultra Massive Array (CUMA) with 4 RF Chains for Massive Connectivity,” inProceeding of the 2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPA WC), pp. 286–290, Lucca, Italy, 2024

  110. [120]

    QoE-Driven Multiple UA Vs-Mounted Reconfigurable Intelligent Surface Communication,

    H. Xu, J. Chen, and M. Jiang, “QoE-Driven Multiple UA Vs-Mounted Reconfigurable Intelligent Surface Communication,”IEEE Communica- tions Letters, vol. 28, no. 12, pp. 2824–2828, Dec. 2024

  111. [121]

    On Performance of RIS-Aided Fluid Antenna Systems,

    F. R. Ghadi, K. K. Wong, W. K. New, H. Xu, R. Murch, and Y . Zhang, “On Performance of RIS-Aided Fluid Antenna Systems,”IEEE Wireless Communications Letters, vol. 13, no. 8, pp. 2175–2179, Aug. 2024

  112. [122]

    FAS-RIS: A Block-Correlation Model Analysis,

    X. Lai, J. Yao, K. Zhi, T. Wu, D. Morales-Jimenez, and K. K. Wong, “FAS-RIS: A Block-Correlation Model Analysis,”IEEE Transactions on V ehicular Technology, vol. 74, no. 2, pp. 3412–3417, Feb. 2025

  113. [123]

    Movable Antennas Meet Intelligent Reflecting Surface: When Do We Need Movable Antennas?

    X. Wei, W. Mei, Q. Wu, B. Ning, and Z. Chen, “Movable Antennas Meet Intelligent Reflecting Surface: When Do We Need Movable Antennas?” inProceeding of the IEEE Wireless Communications and Networking Conference (WCNC 2025), pp. 1–6, Milan, Italy, 2025

  114. [124]

    A Frame- work of FAS-RIS Systems: Performance Analysis and Throughput Optimization,

    J. Yao, X. Lai, K. Zhi, T. Wu, M. Jin, and C. Pan, “A Frame- work of FAS-RIS Systems: Performance Analysis and Throughput Optimization,”IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2025.3590458

  115. [125]

    FAS-RIS Communication: Model, Analysis, and Optimization,

    J. Yao, J. Zheng, T. Wu, M. Jin, C. Yuen, K. K. Wong, and F. Adachi, “FAS-RIS Communication: Model, Analysis, and Optimization,”IEEE Transactions on V ehicular Technology, vol. 74, no. 6, pp. 9938–9943, Jun. 2025

  116. [126]

    Fluid Antenna Meets RIS: Random Matrix Analysis and Two- Timescale Design for Multi-User Communications,

    X. Zhang, D. Xu, J. Wang, S. Song, D. W. K. Ng, and M. Deb- bah, “Fluid Antenna Meets RIS: Random Matrix Analysis and Two- Timescale Design for Multi-User Communications,”arXiv preprint arXiv:2503.06080, 2025

  117. [127]

    RIS-Aided Fluid Antenna Array-Mounted UA V Networks,

    L. H. Shen and Y . H. Chiu, “RIS-Aided Fluid Antenna Array-Mounted UA V Networks,”IEEE Wireless Communications Letters, vol. 14, no. 4, pp. 1049–1053, Apr. 2025

  118. [128]

    Joint User Association and Deployment Optimization for Delay-Minimized UA V-Aided MEC Net- works,

    Z. Han, T. Zhou, T. Xu, and H. Hu, “Joint User Association and Deployment Optimization for Delay-Minimized UA V-Aided MEC Net- works,”IEEE Wireless Communications Letters, vol. 12, no. 10, pp. 1791–1795, Oct. 2023

  119. [129]

    Blocked Job Scheduling and Redundant Computing Resource Allocation in Edge Computing Systems,

    P. Peng, Y . Rui, T. Xu, Y . Zou, X. Chen, X. Jiang, C. C. Zarakovitis, and M. Guizani, “Blocked Job Scheduling and Redundant Computing Resource Allocation in Edge Computing Systems,”IEEE Internet of Things Journal, vol. 12, no. 11, pp. 15572–15586, Jun. 2025

  120. [130]

    Fluid Antenna for Mobile Edge Computing,

    Y . Zuo, J. Guo, B. Sheng, C. Dai, F. Xiao, and S. Jin, “Fluid Antenna for Mobile Edge Computing,”IEEE Communications Letters, vol. 28, no. 7, pp. 1728–1732, Jul. 2024

  121. [131]

    Delay Minimization for Movable Antennas-Enabled Anti-Jamming Communications With Mobile Edge Computing,

    Y . Xiu, Y . Zhao, S. Yang, M. Xu, D. Niyato, Y . Li, and N. Wei, “Delay Minimization for Movable Antennas-Enabled Anti-Jamming Communications With Mobile Edge Computing,”arXiv preprint arXiv:2409.14418, 2024

  122. [132]

    Latency Minimization for Movable Antennas-Enabled Relay- aided D2D Mobile Edge Computing Communication Systems,

    Y . Xiu, Y . Zhao, R. Yang, H. Tang, L. Qu, M. Khabbaz, C. Assi, and N. Wei, “Latency Minimization for Movable Antennas-Enabled Relay- aided D2D Mobile Edge Computing Communication Systems,”arXiv preprint arXiv:2412.11351, 2024

  123. [133]

    Movable- Antenna-Enhanced Wireless-Powered Mobile-Edge Computing Sys- tems,

    P. Chen, Y . Yang, B. Lyu, Z. Yang, and A. Jamalipour, “Movable- Antenna-Enhanced Wireless-Powered Mobile-Edge Computing Sys- tems,”IEEE Internet of Things Journal, vol. 11, no. 21, pp. 35505– 35518, Nov. 2024

  124. [134]

    Mov- able Antenna-Empowered AirComp,

    Z. Cheng, N. Li, J. Zhu, X. She, C. Ouyang, and P. Chen, “Mov- able Antenna-Empowered AirComp,”arXiv preprint arXiv:2309.12596, 2023

  125. [135]

    Fluid Antenna Array Enhanced Over-the-Air Computation,

    D. Zhang, S. Ye, M. Xiao, K. Wang, M. Di Renzo, and M. Skoglund, “Fluid Antenna Array Enhanced Over-the-Air Computation,”IEEE Wireless Communications Letters, vol. 13, no. 6, pp. 1541–1545, Jun. 2024

  126. [136]

    Over-the-Air Computation via 2-D Movable Antenna Array,

    N. Li, P. Wu, B. Ning, L. Zhu, and W. Mei, “Over-the-Air Computation via 2-D Movable Antenna Array,”IEEE Wireless Communications Letters, vol. 14, no. 1, pp. 33–37, Jan. 2025

  127. [137]

    Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence,

    S. Park and H. Seo, “Federated Learning Meets Fluid Antenna: Towards Robust and Scalable Edge Intelligence,”arXiv preprint arXiv:2503.03054, 2025

  128. [138]

    Fluid Antenna Enabled Over-the-Air Federated Learning: Joint Optimization of Positioning, Beamforming, and User Selection,

    Y . Zhao, M. Xu, P. Wang, and D. Niyato, “Fluid Antenna Enabled Over-the-Air Federated Learning: Joint Optimization of Positioning, Beamforming, and User Selection,”arXiv preprint arXiv:2503.00011, 2025

  129. [139]

    Enhanced Over-the-Air Federated Learning Using AI-Based Fluid Antenna System,

    M. Ahmadzadeh, S. Pakravan, G. A. Hodtani, M. Zeng, and J. Y . Chouinard, “Enhanced Over-the-Air Federated Learning Using AI-Based Fluid Antenna System,” inProceeding of the 2025 IEEE Wireless Communications and Networking Conference (WCNC 2025), pp. 1–6, Milan, Italy, 2025

  130. [140]

    Movable Antenna Design for UA V-Aided Federated Learning via Deep Reinforcement Learning,

    M. Ahmadzadeh, S. Pakravan, and G. A. Hodtani, “Movable Antenna Design for UA V-Aided Federated Learning via Deep Reinforcement Learning,” inProceeding of the 15th International Conference on Information and Knowledge Technology (IKT), pp. 91–95, Isfahan, Iran, 2024

  131. [141]

    Movable Antenna-Assisted Integrated Sensing and Communication Systems,

    C. Jiang, C. Zhang, C. Huang, J. Ge, D. Niyato, and C. Yuen, “Movable Antenna-Assisted Integrated Sensing and Communication Systems,” IEEE Transactions on Wireless Communications, vol. 24, no. 8, pp. 6397–6412, Aug. 2025

  132. [142]

    Y . Xiu, S. Yang, W. Lyu, P. Lep Yeoh, Y . Li, and Y . Ai, ”Movable Antenna Enabled ISAC Beamforming Design for Low-Altitude Airborne Vehicles,”IEEE Wireless Communications Letters, vol. 14, no. 5, pp. 1311–1315, May 2025

  133. [143]

    Fluid Antenna- Assisted ISAC Systems,

    L. Zhou, J. Yao, M. Jin, T. Wu, and K. K. Wong, “Fluid Antenna- Assisted ISAC Systems,”IEEE Wireless Communications Letters, vol. 13, no. 12, pp. 3533–3537, Dec. 2024

  134. [144]

    Fluid-Antenna En- hanced Integrated Sensing and Communication: Joint Antenna Posi- tioning and Beamforming Design,

    T. Hao, C. Shi, Y . Guo, B. Xia, and F. Yang, “Fluid-Antenna En- hanced Integrated Sensing and Communication: Joint Antenna Posi- tioning and Beamforming Design,” inProceeding of the IEEE/CIC International Conference on Communications in China (ICCC), pp. 956– 961, Hangzhou, C...

  135. [145]

    Shifting the ISAC Trade-Off With Fluid Antenna Systems,

    J. Zou, H. Xu, C. Wang, L. Xu, S. Sun, K. Meng, C. Masouros, and K. K. Wong, “Shifting the ISAC Trade-Off With Fluid Antenna Systems,” IEEE Wireless Communications Letters, vol. 13, no. 12, pp. 3479–3483, Dec. 2024

  136. [146]

    Movable-Antenna Array Empowered ISAC Systems for Low-Altitude Economy,

    Z. Kuang, W. Liu, C. Wang, Z. Jin, J. Ren, X. Zhang, and Y Shen, “Movable-Antenna Array Empowered ISAC Systems for Low-Altitude Economy,” inProceeding of the IEEE/CIC International Conference on Communications in China (ICCC Workshops), pp. 776–781, Hangzhou, China, 2024

  137. [147]

    An Efficient Sum-Rate Maximization Algorithm for Fluid Antenna-Assisted ISAC System,

    Q. Zhang, M. Shao, T. Zhang, G. Chen, J. Liu, and P. C. Ching, “An Efficient Sum-Rate Maximization Algorithm for Fluid Antenna-Assisted ISAC System,”IEEE Communications Letters, vol. 29, no. 1, pp. 200– 204, Jan. 2025

  138. [148]

    Fluid Antenna System Liberating Multiuser MIMO for ISAC via Deep Reinforcement Learning,

    C. Wang, G. Li, H. Zhang, K.-K. Wong, Z. Li, D. W. K. Ng, and C. B. Chae, “Fluid Antenna System Liberating Multiuser MIMO for ISAC via Deep Reinforcement Learning,”IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 10879–10894, Sept. 2024

  139. [149]

    Secure Wireless Communication via Movable-Antenna Array,

    G. Hu, Q. Wu, K. Xu, J. Si, and N. Al-Dhahir, “Secure Wireless Communication via Movable-Antenna Array,”IEEE Signal Processing Letters, vol. 31, pp. 516–520, Jan. 2024

  140. [150]

    Secure MIMO Communication Relying on Movable Antennas,

    J. Tang, C. Pan, Y . Zhang, H. Ren, and K. Wang, “Secure MIMO Communication Relying on Movable Antennas,”IEEE Transactions on Communications, vol. 73, no. 4, pp. 2159–2175, Apr. 2025

  141. [151]

    Fluid Antenna Enabling Secret Communications,

    B. Tang, H. Xu, K. K. Wong, K. F. Tong, Y . Zhang, and C. B. Chae, “Fluid Antenna Enabling Secret Communications,”IEEE Communica- tions Letters, vol. 27, no. 6, pp. 1491–1495, Jun. 2023

  142. [152]

    Can Movable Antenna-enabled Micro-Mobility Replace UA V-enabled Macro-Mobility? A Physical Layer Security Perspective,

    K. Li, K. Yu, D. Ma, Y . Zhao, X. Liu, Q. Zhang, and Z. Feng, “Can Movable Antenna-enabled Micro-Mobility Replace UA V-enabled Macro-Mobility? A Physical Layer Security Perspective,”arXiv preprint arXiv:2506.19456, 2025

  143. [153]

    Movable Antennas-Assisted Secure Transmission Without Eavesdroppers’ Instan- taneous CSI,

    G. Hu, Q. Wu, D. Xu, K. Xu, J. Si, Y . Cai, and N. Al-Dhahir, “Movable Antennas-Assisted Secure Transmission Without Eavesdroppers’ Instan- taneous CSI,”IEEE Transactions on Mobile Computing, vol. 23, no. 12, pp. 14263–14279, Dec. 2024

  144. [154]

    Movable Antenna Empowered Physical Layer Security without Eve’s CSI: Joint Optimization of Beamforming and Antenna Positions,

    Z. Feng, Y . Zhao, K. Yu, and D. Li, “Movable Antenna Empowered Physical Layer Security without Eve’s CSI: Joint Optimization of Beamforming and Antenna Positions,”arXiv preprint arXiv:2405.16062, 2024

  145. [155]

    Movable Antenna Aided Physical Layer Security with No Eavesdropper CSI,

    Z. Cheng, C. Ouyang, and X. Zhang, “Movable Antenna Aided Physical Layer Security with No Eavesdropper CSI,” inProceeding of the 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2025), pp. 1–5, Hyderabad, India, 2025. JOURNAL OF LATEX CLASS...

  146. [156]

    Fluid- Antenna-aided AA V Secure Communications in Eavesdropper Uncertain Location,

    Y . Wu, J. Luo, W. Chen, S. Wang, F. Wang, and H. Ding, “Fluid- Antenna-aided AA V Secure Communications in Eavesdropper Uncertain Location,”arXiv preprint arXiv:2509.08432, 2025

  147. [157]

    Coding-Enhanced Cooperative Jamming for Secret Communication in Fluid Antenna Systems,

    H. Xu, K. K. Wong, W. K. New, G. Li, F. R. Ghadi, Y . Zhu, S. Jin, C. B. Chae, and Y . Zhang, “Coding-Enhanced Cooperative Jamming for Secret Communication in Fluid Antenna Systems,”IEEE Communications Letters, vol. 28, no. 9, pp. 1991–1995, Sept. 2024

  148. [158]

    Energy-Efficient Secure Communica- tions via Joint Optimization of UA V Trajectory and Movable-Antenna Array Beamforming,

    S. Kim, J. Gong, and J. Kang, “Energy-Efficient Secure Communica- tions via Joint Optimization of UA V Trajectory and Movable-Antenna Array Beamforming,”arXiv preprint arXiv:2507.20489, 2025

  149. [159]

    Predictive Posi- tion Control for Movable Antenna Arrays in UA V Communications: A Spatio-Temporal Transformer-LSTM Framework,

    K. Yu, K. Li, X. Liu, Q. Zhang, and Z. Feng, “Predictive Posi- tion Control for Movable Antenna Arrays in UA V Communications: A Spatio-Temporal Transformer-LSTM Framework,”arXiv preprint arXiv:2508.10720, 2025

  150. [160]

    Movable Antennas in Wireless Systems: A Tool for Connectivity or A New Security Threat?

    Y . Maghrebi, M. Elhattab, C. Assi, A. Ghrayeb, and G. Kaddoum, “Movable Antennas in Wireless Systems: A Tool for Connectivity or A New Security Threat?” inProceeding of the IEEE International Conference on Communications (ICC 2025), pp. 3351–3356, Montreal, QC, Canada, 2025

  151. [161]

    Movable Antenna Array Aided Ultra Reliable Covert Communications,

    Y . Wang, G. Hu, X. Hu, X. Lu, and Y . Huang, “Movable Antenna Array Aided Ultra Reliable Covert Communications,”arXiv preprint arXiv:2412.20417, 2024

  152. [162]

    Movable-Antenna Enabled Covert Communication,

    P. Liu, J. Si, Z. Cheng, Z. Li, and H. Hu, “Movable-Antenna Enabled Covert Communication,”IEEE Wireless Communications Letters, vol. 14, no. 2, pp. 280–284, Feb. 2025

  153. [163]

    Sum Rate Maximization for Movable Antenna Enhanced Multiuser Covert Communications,

    H. Mao, X. Pi, L. Zhu, Z. Xiao, X.-G. Xia, and R. Zhang, “Sum Rate Maximization for Movable Antenna Enhanced Multiuser Covert Communications,”IEEE Wireless Communications Letters, vol. 14, no. 3, pp. 611–615, Mar. 2025

  154. [164]

    Movable Antenna-Assisted Covert Communications with Reconfig- urable Intelligent Surfaces,

    W. Xie, Z. Li, C. Yu, H. Xu, J. Wang, W. Wu, X. Li, and L. Yang, “Movable Antenna-Assisted Covert Communications with Reconfig- urable Intelligent Surfaces,”IEEE Internet of Things Journal, vol. 12, no. 9, pp. 12369–12382, May 2025

  155. [165]

    Generative AI for Advanced UA V Networking,

    G. Sun, W. Xie, D. Niyato, H. Du, J. Kang, J. Wu, S. Sun and P. Zhang, “Generative AI for Advanced UA V Networking,”IEEE Network, vol. 39, no. 4, pp. 244–253, Jul. 2025

  156. [166]

    Large Models for Aerial Edges: An Edge- Cloud Model Evolution and Communication Paradigm,

    S. Zhang, Q. Liu, K. Chen, B. Di, H. Zhang, W. Yang, D. Niyato, Z. Han, and H. V . Poor, “Large Models for Aerial Edges: An Edge- Cloud Model Evolution and Communication Paradigm,”IEEE Journal on Selected Areas in Communications, vol. 43, no. 1, pp. 21–35, Jan. 2025

  157. [167]

    Deepseekmoe: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models,

    D. Dai, C. Deng, C. Zhao, R. X. Xu, H. Gao, D. Chen, J. Li, W. Zeng, X. Yu, Y . Wu, et al., “Deepseekmoe: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models,”arXiv preprint arXiv:2401.06066, 2024

  158. [168]

    Deepseek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning,

    D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q. Zhu, S. Ma, P. Wang, X. Bi et al., “Deepseek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning,”arXiv preprint arXiv:2501.12948, 2025

  159. [169]

    Generative AI Agents With Large Language Model for Satellite Networks via a Mixture of Experts Transmission,

    R. Zhang, H. Du, Y . Liu, D. Niyato, J. Kang, Z. Xiong, A. Jamalipour and D. I. Kim, “Generative AI Agents With Large Language Model for Satellite Networks via a Mixture of Experts Transmission,”IEEE Journal on Selected Areas in Communications, vol. 42, no. 12, pp. 3581– 3596,...

  160. [170]

    Multi-Agent Cooperation for Computing Power Scheduling in UA Vs Empowered Aerial Computing Systems,

    M. Tao, X. Li, J. Feng, D. Lan, J. Du and C. Wu, “Multi-Agent Cooperation for Computing Power Scheduling in UA Vs Empowered Aerial Computing Systems,”IEEE Journal on Selected Areas in Com- munications, vol. 42, no. 12, pp. 3521–3535, Dec. 2024

  161. [171]

    Semantics-Empowered Communications: A Tutorial-Cum- Survey,

    Z. Lu, R. Li, K. Lu, X. Chen, E. Hossain, Z. Zhao, and H. Zhang, “Semantics-Empowered Communications: A Tutorial-Cum- Survey,”IEEE Communications Surveys & Tutorials, vol. 26, no. 1, pp. 41–79, Firstquarter 2024

  162. [172]

    Outage Performance of Fluid Antenna System (FAS)-aided Terahertz Communication Networks,

    L. Tlebaldiyeva, S. Arzykulov, K. M. Rabie, X. Li, and G. Nau- ryzbayev, “Outage Performance of Fluid Antenna System (FAS)-aided Terahertz Communication Networks,” inProceeding of the IEEE Inter- national Conference on Communications (ICC 2023), pp. 1922-1927, Rome, Italy, 2023

  163. [173]

    Analytical Framework for Effective Degrees of Freedom in Near-Field XL-MIMO,

    Z. Wang, J. Zhang, W. Yi, H. Xiao, H. Du, D. Niyato, B. Ai, and D. W. K. Ng, “Analytical Framework for Effective Degrees of Freedom in Near-Field XL-MIMO,”IEEE Transactions on Wireless Communications, vol. 24, no. 4, pp. 3465–3482, Apr. 2025

  164. [174]

    Radiation Pattern and Polarization Reconfigurable Antenna Using Dielectric Liquid,

    J. Ren, Z. Zhou, Z. H. Wei, H. M. Ren, Z. Chen, Y . Liu, and Y . Z. Yin, “Radiation Pattern and Polarization Reconfigurable Antenna Using Dielectric Liquid,”IEEE Transactions on Antennas and Propagation, vol. 68, no. 12, pp. 8174–8179, Dec. 2020

  165. [175]

    A Survey on Integrated Sensing, Communication, and Computa- tion,

    D. Wen, Y . Zhou, X. Li, Y . Shi, K. Huang and K. B. Letaief, “A Survey on Integrated Sensing, Communication, and Computa- tion,”IEEE Communications Surveys & Tutorials, early access, doi: 10.1109/COMST.2024.3521498

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.