pith. sign in

arxiv: 2605.23649 · v1 · pith:JLX2FAGKnew · submitted 2026-05-22 · 📡 eess.SP · math.ST· stat.TH

Diffusion Fluid Antenna Systems for Resilient ISAC

Noor Waqar , Kai-Kit Wong , Chan-Byoung Chae , Ross Murch This is my paper

Pith reviewed 2026-05-25 03:04 UTC · model grok-4.3

classification 📡 eess.SP math.STstat.TH
keywords fluid antenna systemsintegrated sensing and communicationdiffusion modelsspatial selectiongenerative AIelectromagnetic fadingISACport selection
0
0 comments X

The pith

Diffusion FAS lets objects choose fluid-antenna ports to suppress their own sensing visibility by two orders of magnitude or isolate nearby targets.

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

The paper argues that existing ISAC work focuses on the base station while leaving objects with little control over how visible they appear to sensing. It introduces diffusion FAS, which treats port selection on a fluid antenna as a generative spatial-selection task solved by a conditional DDPM that reconstructs the full aperture correlation from sparse measurements. If this works, objects gain a new degree of freedom to create deep fades for stealth or spatial nulls for isolation without altering waveforms or power. The approach therefore moves ISAC design from power-domain optimization to dynamic reconfiguration of the electromagnetic fading manifold.

Core claim

Diffusion FAS casts ISAC as a dynamic spatial selection problem in which a conditional denoising diffusion probabilistic model reconstructs the latent spatial correlation structure of the full fluid-antenna aperture from a small set of observed ports; this reconstruction enables two operating modes in which port choice either identifies localized deep fades that suppress a target’s sensing visibility by up to two orders of magnitude or synthesizes spatial nulls that reject interference from adjacent objects.

What carries the argument

Conditional denoising diffusion probabilistic model (DDPM) that reconstructs the latent spatial correlation structure of the fluid-antenna aperture from sparse port observations.

If this is right

  • Objects gain an independent spatial DoF to reduce their detectability without waveform changes at the base station.
  • Adjacent objects can be rejected by synthesizing spatial nulls at the target’s fluid antenna.
  • ISAC systems can operate under sparse port observations while still exploring the full reconfigurable aperture.
  • Sensing reliability improves when dominant interferers are spatially nulled at the object side.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same port-selection mechanism could be applied to other reconfigurable apertures beyond fluid antennas.
  • Security applications may emerge in which devices deliberately minimize their radar cross-section via learned spatial choices.
  • Network-level ISAC protocols may need to account for object-side adaptation rather than assuming passive targets.

Load-bearing premise

The conditional DDPM can accurately reconstruct the full spatial correlation structure from only a small set of observed ports.

What would settle it

Real-world channel measurements on a fluid antenna that show the DDPM-reconstructed correlations deviate substantially from the measured full-aperture correlations, or that the achieved suppression or isolation falls short of the reported two-order-of-magnitude levels.

Figures

Figures reproduced from arXiv: 2605.23649 by Chan-Byoung Chae, Kai-Kit Wong, Noor Waqar, Ross Murch.

Figure 1
Figure 1. Figure 1: ISAC with an FAS-enabled object (i.e., User B): (a) Use Case I (single-user stealth); and (b) Use Case II (cooperative interference shaping). [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Impact of the number of active ports (Mactive) on localization and detection performance under rich isotropic scattering. • No User B (Theoretical Upper Bound)—This scheme evaluates the FAS performance in a pristine, interference￾free vacuum where User B does not exist. The spatial correlation is solely driven by the target, serving as the absolute physical upper limit for detection probability. • Random F… view at source ↗
Figure 3
Figure 3. Figure 3: Impact of the number of active ports (Mactive) on localization and detection performance under finite scattering. free upper bound (No User B). This steep ascent demonstrates the generative prior’s ability to efficiently leverage emerging spatial DoFs to synthesize precise spatial nulls against the in￾terferer. Crucially, the performance of Diffusion-FAS smoothly saturates at a highly compact active subset… view at source ↗
Figure 5
Figure 5. Figure 5: Impact of the number of observed context ports ( [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 8
Figure 8. Figure 8: Impact of the target false alarm rate (PFA) on localization and detection performance under rich isotropic scattering. Diffusion-FAS maintains a consistent stealth floor nearly two orders of magnitude lower than the uninformed Random FAS selection. These results confirm that by intelligently navigating the environmental spatial correlation, FAS can effectively “hide” the user’s signature from adversarial s… view at source ↗
Figure 9
Figure 9. Figure 9: Impact of the clutter standard deviation ( [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Impact of the spatial target offset (∆) on localization and detection performance under rich isotropic scattering. [21] W. K. New et al., “Fluid antenna systems: Redefining reconfigurable wireless communications,” IEEE J. Sel. Areas Commun., vol. 44, pp. 1013–1044, 2026. [22] W.-J. Lu et al., “Fluid antennas: Reshaping intrinsic properties for flexible radiation characteristics in intelligent wireless net… view at source ↗
Figure 13
Figure 13. Figure 13: Probability of detection (PD) vs. Mactive for Case 1. “An information-theoretic characterization of MIMO-FAS: Optimiza￾tion, diversity-multiplexing tradeoff and q-outage capacity,” IEEE Trans. Wireless Commun., vol. 23, no. 6, pp. 5541–5556, Jun. 2024. [42] H. Xu et al., “Channel estimation for FAS-assisted multiuser mmWave systems,” IEEE Commun. Lett., vol. 28, no. 3, pp. 632–636, Mar. 2024. [43] Z. Zhan… view at source ↗
read the original abstract

Most existing integrated sensing and communication (ISAC) studies focus on enabling a base station (BS) to support sensing and communication over shared resources through advanced waveform design and power allocation. In contrast, the object-side perspective remains underexplored. For example, an object may wish to remain difficult to detect for security reasons, while another object in close proximity may generate dominant reflections that confuse the BS and impair sensing reliability for the intended target. These challenges motivate the fluid antenna system (FAS) paradigm which introduces a reconfigurable spatial degree of freedom (DoF) that can reshape sensing signatures via port selection, beyond what waveform and power control alone can provide. In this paper, we devise diffusion FAS, a generative artificial intelligence (AI)-driven framework that exploits spatial agility to steer ISAC performance over the electromagnetic fading manifold. Instead of optimizing ISAC solely in the power domain, diffusion FAS casts ISAC as a \emph{dynamic spatial selection} problem in which antenna states (i.e., ports) are chosen to shape sensing signatures while maintaining communication objectives. To work under sparse measurements, we employ a conditional denoising diffusion probabilistic model (DDPM) to reconstruct the latent spatial correlation structure from a small set of observed ports, enabling efficient exploration of the reconfigurable aperture. We demonstrate two FAS-enabled ISAC modes: (1) \emph{generative spatial stealth}, which identifies localized deep fades to suppress a target's sensing visibility by up to two orders of magnitude, and (2) \emph{target isolation}, which synthesizes spatial nulls that reject interference from adjacent objects.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes diffusion FAS, a generative AI framework for fluid antenna systems (FAS) in integrated sensing and communication (ISAC). It employs a conditional denoising diffusion probabilistic model (DDPM) to reconstruct the latent spatial correlation structure of the full fluid aperture from sparse port observations, enabling two modes: generative spatial stealth (identifying deep fades to suppress target sensing visibility by up to two orders of magnitude) and target isolation (synthesizing spatial nulls to reject interference from adjacent objects). The approach frames ISAC as a dynamic spatial selection problem over the electromagnetic fading manifold rather than relying solely on waveform or power-domain optimization.

Significance. If the conditional DDPM reconstruction step holds with sufficient accuracy across electromagnetic scenarios, the work would introduce a new spatial degree of freedom for object-side ISAC resilience, allowing reconfigurable apertures to achieve sensing signature shaping that complements existing waveform design techniques. The generative sampling approach could enable exploration of reconfigurable states under measurement sparsity, with potential impact on secure sensing and multi-object isolation scenarios.

major comments (2)
  1. [Abstract] Abstract: The headline performance claims (suppression of sensing visibility by up to two orders of magnitude via generative spatial stealth, plus spatial null synthesis) rest on the unvalidated assumption that the conditional DDPM accurately recovers deep-fade locations and nulls across the full aperture from only a small observed port subset. No quantitative reconstruction metrics (NMSE, fade-location error, or cross-scenario generalization) or error bars are provided to confirm reliability at the operating sparsity levels, directly undermining assessment of the reported gains.
  2. [Abstract] The manuscript provides no derivations, simulation setup details, or validation experiments for the DDPM reconstruction accuracy under the electromagnetic fading manifold, which is the load-bearing step for mapping sparse observations to correct port states and achieving the claimed suppression levels.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback emphasizing the need to validate the conditional DDPM reconstruction step, which is foundational to the proposed diffusion FAS framework. We provide point-by-point responses to the major comments and will incorporate the requested details and metrics in the revised manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline performance claims (suppression of sensing visibility by up to two orders of magnitude via generative spatial stealth, plus spatial null synthesis) rest on the unvalidated assumption that the conditional DDPM accurately recovers deep-fade locations and nulls across the full aperture from only a small observed port subset. No quantitative reconstruction metrics (NMSE, fade-location error, or cross-scenario generalization) or error bars are provided to confirm reliability at the operating sparsity levels, directly undermining assessment of the reported gains.

    Authors: We agree that the performance claims require explicit support from reconstruction accuracy metrics, which are not currently quantified in the manuscript. While end-to-end ISAC gains are demonstrated via simulation, the DDPM step itself lacks standalone evaluation. In the revision we will add a dedicated validation subsection reporting: NMSE versus sparsity ratio (5–30% observed ports), fade-location error rates for deep-fade identification, cross-scenario generalization across varying scatterer densities and Rician factors, and error bars from 100 Monte Carlo trials. These will be placed before the ISAC performance results to substantiate the operating conditions. revision: yes

  2. Referee: [Abstract] The manuscript provides no derivations, simulation setup details, or validation experiments for the DDPM reconstruction accuracy under the electromagnetic fading manifold, which is the load-bearing step for mapping sparse observations to correct port states and achieving the claimed suppression levels.

    Authors: We acknowledge that the manuscript does not include derivations or detailed validation for the DDPM reconstruction. The current text assumes standard conditional DDPM mechanics and focuses on the ISAC application. In the revised version we will insert: (i) a concise derivation of the conditional reverse diffusion process showing how sparse port observations are encoded as conditioning input; (ii) full simulation setup details including the geometry-based stochastic electromagnetic channel model, DDPM hyperparameters (T=1000 steps, U-Net architecture), training dataset size and generation procedure, and port-selection hardware assumptions; (iii) explicit reconstruction accuracy experiments under the fading manifold. These additions will appear in Sections III and IV. revision: yes

Circularity Check

0 steps flagged

No significant circularity; generative framework applies external DDPM without self-referential reduction

full rationale

The paper presents diffusion FAS as a conditional DDPM-based generative method for reconstructing spatial correlations from sparse ports and selecting antenna states for ISAC objectives. No equations, fitted parameters, or derivations are shown that reduce the claimed suppression or null synthesis to self-defined quantities by construction. The approach relies on an established external technique (DDPM) rather than a closed-form chain or self-citation load-bearing premise. The performance claims rest on the model's reconstruction accuracy as an assumption, not on any internal equivalence that would constitute circularity per the enumerated patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on the assumption that a small number of port observations suffice to reconstruct the full spatial correlation via DDPM, plus standard assumptions about the fading manifold and the ability of port selection to create deep fades or nulls.

axioms (2)
  • domain assumption A conditional denoising diffusion probabilistic model can reconstruct the latent spatial correlation structure from sparse port observations.
    Invoked when the paper states that the DDPM enables efficient exploration of the reconfigurable aperture under sparse measurements.
  • domain assumption Port selection on a fluid antenna can reshape sensing signatures independently of waveform and power control.
    Central to the motivation that FAS provides a spatial DoF beyond existing ISAC techniques.

pith-pipeline@v0.9.0 · 5827 in / 1414 out tokens · 16809 ms · 2026-05-25T03:04:35.477836+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

77 extracted references · 77 canonical work pages

  1. [1]

    A speculative study on 6G,

    F. Tariqet al., “A speculative study on 6G,”IEEE Wireless Commun., vol. 27, no. 4, pp. 118–125, Aug. 2020. 11 (a) (b) Fig. 9. Impact of the clutter standard deviation (σ c) on localization and detection performance under rich isotropic scattering

    work page 2020

  2. [2]

    On the road to 6G: Visions, requirements, key technologies, and testbeds,

    C.-X. Wanget al., “On the road to 6G: Visions, requirements, key technologies, and testbeds,”IEEE Commun. Surv. & Tut., vol. 25, no. 2, pp. 905–974, 2nd Quart. 2023

    work page 2023

  3. [3]

    The road towards 6G: A comprehensive survey,

    W. Jiang, B. Han, M. A. Habibi and H. D. Schotten, “The road towards 6G: A comprehensive survey,”IEEE Open J. Commun. Soc., vol. 2, pp. 334–366, 2021

    work page 2021

  4. [4]

    Integrated sensing and communications: Toward dual- functional wireless networks for 6G and beyond,

    F. Liuet al., “Integrated sensing and communications: Toward dual- functional wireless networks for 6G and beyond,”IEEE J. Select. Areas Commun., vol. 40, no. 6, pp. 1728–1767, Jun. 2022

    work page 2022

  5. [5]

    Integrated sensing and communication (ISAC) transceiver: Hardware architectures, enabling technologies, and emerging trends,

    K. Wu, Y . Bigdeli, S. A. Keivaan, J. Deng and P. Burasa, “Integrated sensing and communication (ISAC) transceiver: Hardware architectures, enabling technologies, and emerging trends,”IEEE J. Select. Topics Electromag., Antennas & Propag., vol. 1, no. 1, pp. 37–64, Sept. 2025

    work page 2025

  6. [6]

    A unified future: Integrated sensing and communication (ISAC) in 6G,

    A. Ghoshet al., “A unified future: Integrated sensing and communication (ISAC) in 6G,”IEEE J. Select. Topics Electromag., Antennas & Propag., vol. 1, no. 1, pp. 365–374, Sept. 2025

    work page 2025

  7. [7]

    A survey on fundamental limits of integrated sensing and communication,

    A. Liuet al., “A survey on fundamental limits of integrated sensing and communication,”IEEE Commun. Surv. & Tuts., vol. 24, no. 2, pp. 994–1034, Secondquarter 2022

    work page 2022

  8. [8]

    Enabling joint communication and radar sensing in mobile networks—A survey,

    J. A. Zhanget al., “Enabling joint communication and radar sensing in mobile networks—A survey,”IEEE Commun. Surv. & Tuts., vol. 24, no. 1, pp. 306–345, Firstquarter 2022

    work page 2022

  9. [9]

    Integrated sensing and communication with massive MIMO: A unified tensor approach for channel and target parameter estimation,

    R. Zhanget al., “Integrated sensing and communication with massive MIMO: A unified tensor approach for channel and target parameter estimation,”IEEE Trans. Wireless Commun., vol. 23, no. 8, pp. 8571– 8587, Aug. 2024

    work page 2024

  10. [10]

    Extremely large-scale MIMO: Fundamentals, chal- lenges, solutions, and future directions,

    Z. Wanget al., “Extremely large-scale MIMO: Fundamentals, chal- lenges, solutions, and future directions,”IEEE Wireless Commun., vol. 31, no. 3, pp. 117–124, Jun. 2024

    work page 2024

  11. [11]

    D. A. U. Villalonga, H. OdetAlla, M. J. Fern ´andez-Getino Garc´ıa, and A. Flizikowski, “Spectral efficiency of precoded 5G-NR in single and multi-user scenarios under imperfect channel knowledge: A comprehen- (a) (b) Fig. 10. Impact of the clutter standard deviation (σ c) on localization and detection performance under finite scattering. sive guide for i...

    work page 2022

  12. [12]

    PAPR analysis and mitigation algo- rithms for beamforming MIMO OFDM systems,

    Y .-C. Hung and S.-H. L. Tsai, “PAPR analysis and mitigation algo- rithms for beamforming MIMO OFDM systems,”IEEE Trans. Wireless Commun., vol. 13, no. 5, pp. 2588–2600, May 2014

    work page 2014

  13. [13]

    Toward multi- functional 6G wireless networks: Integrating sensing, communication, and security,

    Z. Wei, F. Liu, C. Masouros, N. Su and A. P. Petropulu, “Toward multi- functional 6G wireless networks: Integrating sensing, communication, and security,”IEEE Commun. Mag., vol. 60, no. 4, pp. 65–71, Apr. 2022

    work page 2022

  14. [14]

    Privacy and security in ubiquitous integrated sensing and communication: Threats, challenges and future directions,

    K. Qu, J. Ye, X. Li and S. Guo, “Privacy and security in ubiquitous integrated sensing and communication: Threats, challenges and future directions,”IEEE Internet Things Mag., vol. 7, no. 4, pp. 52–58, Jul. 2024

    work page 2024

  15. [15]

    Sensing-resistance-oriented design for privacy-concerned secure transmission in ISAC scenarios,

    T. Maet al., “Sensing-resistance-oriented design for privacy-concerned secure transmission in ISAC scenarios,”IEEE Trans. Wireless Commun., vol. 24, no. 11, pp. 9391–9406, Nov. 2025

    work page 2025

  16. [16]

    Enabling intelligent connectivity: A survey of secure ISAC in 6G networks,

    X. Zhuet al., “Enabling intelligent connectivity: A survey of secure ISAC in 6G networks,”IEEE Commun. Surv. & Tuts., vol. 27, no. 2, pp. 748–781, Apr. 2025

    work page 2025

  17. [17]

    Performance limits of fluid antenna systems,

    K. K. Wong, A. Shojaeifard, K.-F. Tong, and Y . Zhang, “Performance limits of fluid antenna systems,”IEEE Commun. Lett., vol. 24, no. 11, pp. 2469–2472, Nov. 2020

    work page 2020

  18. [18]

    Fluid antenna systems,

    K. K. Wong, A. Shojaeifard, K.-F. Tong, and Y . Zhang, “Fluid antenna systems,”IEEE Trans. Wireless Commun., vol. 20, no. 3, pp. 1950–1962, Mar. 2021

    work page 1950

  19. [19]

    A tutorial on fluid antenna system for 6G networks: Encompassing communication theory, optimization methods and hard- ware designs,

    W. K. Newet al., “A tutorial on fluid antenna system for 6G networks: Encompassing communication theory, optimization methods and hard- ware designs,”IEEE Commun. Surv. & Tut., vol. 27, no. 4, pp. 2325– 2377, Aug. 2025

    work page 2025

  20. [20]

    A contemporary survey on fluid antenna systems: Fundamentals and networking perspectives,

    H. Honget al., “A contemporary survey on fluid antenna systems: Fundamentals and networking perspectives,”IEEE Trans. Netw. Sci. Eng., vol. 13, pp. 2305–2328, 2026. 12 (a) (b) Fig. 11. Impact of the spatial target offset (∆) on localization and detection performance under rich isotropic scattering

    work page 2026

  21. [21]

    Fluid antenna systems: Redefining reconfigurable wireless communications,

    W. K. Newet al., “Fluid antenna systems: Redefining reconfigurable wireless communications,”IEEE J. Sel. Areas Commun., vol. 44, pp. 1013–1044, 2026

    work page 2026

  22. [22]

    Fluid antennas: Reshaping intrinsic properties for flexible radiation characteristics in intelligent wireless networks,

    W.-J. Luet al., “Fluid antennas: Reshaping intrinsic properties for flexible radiation characteristics in intelligent wireless networks,”IEEE Commun. Mag., vol. 63, no. 5, pp. 40–45, May 2025

    work page 2025

  23. [23]

    Fluid antenna systems enabling 6G: Principles, applica- tions, and research directions,

    T. Wuet al., “Fluid antenna systems enabling 6G: Principles, applica- tions, and research directions,” to appear inIEEE Wireless Commun., DOI:10.1109/MWC.2025.3629597, 2025

    work page doi:10.1109/mwc.2025.3629597 2025

  24. [24]

    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:2401.02362, 2024

    work page arXiv 2024

  25. [25]

    Design and experimental validation of mmWave surface wave enabled fluid antennas for future wireless communications,

    Y . Shenet al., “Design and experimental validation of mmWave surface wave enabled fluid antennas for future wireless communications,” to appear inIEEE Antennas & Wireless Propag. Lett., DOI:10.1109/LAWP. 2026.3657059, 2026

    work page doi:10.1109/lawp 2026

  26. [26]

    Electromagnetically reconfigurable fluid antenna system for wireless communications: Design, modeling, algorithm, fabrication, and experiment,

    R. Wanget al., “Electromagnetically reconfigurable fluid antenna system for wireless communications: Design, modeling, algorithm, fabrication, and experiment,”IEEE J. Sel. Areas Commun., vol. 44, pp. 1464–1479, 2026

    work page 2026

  27. [27]

    Fluid antenna systems enabled by reconfigurable holographic surfaces: Beamforming design and experimental validation,

    S. Zhanget al., “Fluid antenna systems enabled by reconfigurable holographic surfaces: Beamforming design and experimental validation,” IEEE J. Select. Areas Commun., vol. 44, pp. 1417–1431, 2026

    work page 2026

  28. [28]

    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

    work page 2025

  29. [29]

    A novel pixel-based reconfigurable antenna applied in fluid antenna systems with high switching speed,

    J. Zhanget al., “A novel pixel-based reconfigurable antenna applied in fluid antenna systems with high switching speed,”IEEE Open J. Antennas & Propag., vol. 6, no. 1, pp. 212–228, Feb. 2025

    work page 2025

  30. [30]

    Wideband pixel-based fluid antenna system: An antenna design for smart city,

    B. Liu, T. Wu, K. K. Wong, H. Wong, and K. F. Tong, “Wideband pixel-based fluid antenna system: An antenna design for smart city,” IEEE Internet Things J., vol. 13, no. 4, pp. 6850–6862, Feb. 2026

    work page 2026

  31. [31]

    K. K. Wong, C. Wang, S. Shen, C.-B. Chae and R. Murch, “Recon- figurable pixel antennas meet fluid antenna systems: A paradigm shift (a) (b) Fig. 12. Impact of the spatial target offset (∆) on localization and detection performance under finite scattering. to electromagnetic signal and information processing,”IEEE Wireless Commun., vol. 33, no. 1, pp. 191...

    work page 2026

  32. [32]

    A new analytical ap- proximation of the fluid antenna system channel,

    M. Khammassi, A. Kammoun, and M.-S. Alouini, “A new analytical ap- proximation of the fluid antenna system channel,”IEEE Trans. Wireless Commun., vol. 22, no. 12, pp. 8843–8858, Dec. 2023

    work page 2023

  33. [33]

    Fluid antenna system: New insights on outage probability and diversity gain,

    W. K. New, K. K. Wong, H. Xu, and K.-F. Tong, “Fluid antenna system: New insights on outage probability and diversity gain,”IEEE Trans. Wireless Commun., vol. 23, no. 5, pp. 4846–4859, Jan. 2024

    work page 2024

  34. [34]

    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 Trans. Wireless Commun., vol. 23, no. 11, pp. 15829–15843, Nov. 2024

    work page 2024

  35. [35]

    On the diversity and coded modulation design of fluid antenna systems,

    C. Psomas, G. M. Kraidy, K. K. Wong, and I. Krikidis, “On the diversity and coded modulation design of fluid antenna systems,”IEEE Trans. Wireless Commun., vol. 23, no. 3, pp. 2082–2096, Mar. 2024

    work page 2082

  36. [36]

    A simple method for the performance analysis of fluid antenna systems under correlated Nakagami-mfading,

    J. D. Vega-S ´anchez, L. F. Urquiza-Aguiar, M. C. P. Paredes, and D. P. M. Osorio, “A simple method for the performance analysis of fluid antenna systems under correlated Nakagami-mfading,”IEEE Wireless Commun. Lett., vol. 13, no. 2, pp. 377–381, Feb. 2024

    work page 2024

  37. [37]

    Novel expressions for the outage probability and diversity gains in fluid antenna system,

    J. D. Vega-S ´anchez, A. E. L ´opez-Ram´ırez, L. Urquiza-Aguiar, and D. P. M. Osorio, “Novel expressions for the outage probability and diversity gains in fluid antenna system,”IEEE Wireless Commun. Lett., vol. 13, no. 2, pp. 372–376, Feb. 2024

    work page 2024

  38. [38]

    On the performance of fluid antennas systems under α-µfading channels,

    P. D. Alvimet al., “On the performance of fluid antennas systems under α-µfading channels,”IEEE Wireless Commun. Lett., vol. 13, no. 1, pp. 108–112, Jan. 2024

    work page 2024

  39. [39]

    Copula-based performance analysis for fluid antenna systems under arbitrary fading channels,

    F. Rostami Ghadi, K. K. Wong, F. Javier L ´opez-Mart´ınez, and K. F. Tong, “Copula-based performance analysis for fluid antenna systems under arbitrary fading channels,”IEEE Commun. Lett., vol. 27, no. 11, pp. 3068–3072, Nov. 2023

    work page 2023

  40. [40]

    A Gaussian copula approach to the performance analysis of fluid antenna systems,

    F. Rostami Ghadiet al., “A Gaussian copula approach to the performance analysis of fluid antenna systems,”IEEE Trans. Wireless Commun., vol. 23, no. 11, pp. 17573–17585, Nov. 2024

    work page 2024

  41. [41]

    An information-theoretic characterization of MIMO-FAS: Optimiza- tion, diversity-multiplexing tradeoff andq-outage capacity,

    W. K. New, K. K. Wong, H. Xu, K. F. Tong, and C.-B. Chae, 13 (a) (b) Fig. 13. Probability of detection (P D) vs.M active for Case 1. “An information-theoretic characterization of MIMO-FAS: Optimiza- tion, diversity-multiplexing tradeoff andq-outage capacity,”IEEE Trans. Wireless Commun., vol. 23, no. 6, pp. 5541–5556, Jun. 2024

    work page 2024

  42. [42]

    Channel estimation for FAS-assisted multiuser mmWave systems,

    H. Xuet al., “Channel estimation for FAS-assisted multiuser mmWave systems,”IEEE Commun. Lett., vol. 28, no. 3, pp. 632–636, Mar. 2024

    work page 2024

  43. [43]

    Successive Bayesian reconstructor for channel estimation in fluid antenna systems,

    Z. Zhang, J. Zhu, L. Dai, and R. W. Heath Jr., “Successive Bayesian reconstructor for channel estimation in fluid antenna systems,”IEEE Trans. Wireless Commun., doi:10.1109/TWC.2024.3515135, 2024

    work page doi:10.1109/twc.2024.3515135 2024

  44. [44]

    Sparse Bayesian learning-based channel estimation for fluid antenna systems,

    B. Xu, Y . Chen, Q. Cui, X. Tao, and K. K. Wong, “Sparse Bayesian learning-based channel estimation for fluid antenna systems,”IEEE Wireless Commun. Lett., vol. 14, no. 2, pp. 325–329, Feb. 2025

    work page 2025

  45. [45]

    Fluid antenna multiple access,

    K. K. Wong and K.-F. Tong, “Fluid antenna multiple access,”IEEE Trans. Wireless Commun., vol. 21, no. 7, pp. 4801–4815, Jul. 2022

    work page 2022

  46. [46]

    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 Commun. Lett., vol. 27, no. 2, pp. 711–715, Feb. 2023

    work page 2023

  47. [47]

    Slow fluid antenna multiple access,

    K. K. Wong, D. Morales-Jimenez, K. F. Tong, and C.-B. Chae, “Slow fluid antenna multiple access,”IEEE Trans. Commun., vol. 71, no. 5, pp. 2831–2846, May 2023

    work page 2023

  48. [48]

    Revisiting outage probability analysis for two-user fluid antenna multiple access system,

    H. Xuet al., “Revisiting outage probability analysis for two-user fluid antenna multiple access system,”IEEE Trans. Wireless Commun., vol. 23, no. 8, pp. 9534–9548, Aug. 2024

    work page 2024

  49. [49]

    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 Trans. Wireless Commun., vol. 23, no. 6, pp. 6279–6294, Jun. 2024

    work page 2024

  50. [50]

    Oppor- tunistic fluid antenna multiple access,

    K. K. Wong, K.-F. Tong, Y . Chen, Y . Zhang, and C.-B. Chae, “Oppor- tunistic fluid antenna multiple access,”IEEE Trans. Wireless Commun., vol. 22, no. 11, pp. 7819–7833, Nov. 2023

    work page 2023

  51. [51]

    Coded fluid antenna multiple access over fast fading channels,

    H. Hong, K. K. Wong, K.-F. Tong, H. Shin and Y . Zhang, “Coded fluid antenna multiple access over fast fading channels,”IEEE Wireless Commun. Lett, vol. 14, no. 4, pp. 1249–1253, Apr. 2025

    work page 2025

  52. [52]

    5G-coded fluid antenna multiple access over block fading channels

    H. Hong, K. K. Wong, K.-F. Wong, H. Xu and H. Li, “5G-coded fluid antenna multiple access over block fading channels”Electron. Lett., vol. 61, no. 1, pp. e70166, Jan. 2025

    work page 2025

  53. [53]

    Downlink OFDM-FAMA in 5G-NR systems,

    H. Honget al., “Downlink OFDM-FAMA in 5G-NR systems,”IEEE Trans. Wireless Commun., vol. 24, no. 12, pp. 10116–10132, Dec. 2025

    work page 2025

  54. [54]

    Turbocharging fluid antenna multiple access,

    N. Waqar, K. K. Wong, C.-B. Chae, and R. Murch, “Turbocharging fluid antenna multiple access,”IEEE Transactions on Wireless Communica- tions, vol. 25, pp. 4038–4052, 2026

    work page 2026

  55. [55]

    Attentional copula- aided turbo fluid antenna massive access,

    N. Waqar, K. K. Wong, C.-B. Chae, and R. Murch, “Attentional copula- aided turbo fluid antenna massive access,”IEEE Wireless Communica- tions Letters, vol. 15, pp. 1951–1955, 2026

    work page 1951

  56. [56]

    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 Commun. Lett., vol. 27, no. 3, pp. 861–865, Mar. 2023

    work page 2023

  57. [57]

    cGAN-based slow fluid antenna multiple access,

    M. Eskandari, A. Burr, K. Cumanan, and K. K. Wong, “cGAN-based slow fluid antenna multiple access,”IEEE Wireless Commun. Lett., vol. 13, no. 10, pp. 2907–2911, Oct. 2024

    work page 2024

  58. [58]

    Opportunistic fluid antenna multiple access via team-inspired reinforcement learning,

    N. Waqaret al., “Opportunistic fluid antenna multiple access via team-inspired reinforcement learning,”IEEE Transactions on Wireless Communications, vol. 23, no. 9, pp. 12068–12083, Sept. 2024

    work page 2024

  59. [59]

    Fluid antenna multiple access for 6G: A holistic review,

    H. Honget al., “Fluid antenna multiple access for 6G: A holistic review,” IEEE Open J. Commun. Soc., vol. 7, pp. 2607–2633, 2026

    work page 2026

  60. [60]

    AI-empowered fluid antenna systems: Opportunities, challenges and future directions,

    C. Wanget al., “AI-empowered fluid antenna systems: Opportunities, challenges and future directions,”IEEE Wireless Commun., vol. 31, no. 5, pp. 34–41, Oct. 2024

    work page 2024

  61. [61]

    Large language model empowered design of fluid antenna systems: Challenges, frameworks, and case studies for 6G,

    C. Wanget al., “Large language model empowered design of fluid antenna systems: Challenges, frameworks, and case studies for 6G,” IEEE Wireless Commun., vol. 33, no. 2, pp. 117–125, Apr. 2026

    work page 2026

  62. [62]

    LLM-driven design for fluid antenna systems,

    C. Wanget al., “LLM-driven design for fluid antenna systems,”IEEE Wireless Commun. Lett., vol. 15, pp. 1065–1069, 2026

    work page 2026

  63. [63]

    Fluid antenna enabling secret communications,

    B. Tanget al., “Fluid antenna enabling secret communications,”IEEE Commun. Lett., vol. 27, no. 6, pp. 1491–1495, Jun. 2023

    work page 2023

  64. [64]

    Coding-enhanced cooperative jamming for secret commu- nication in fluid antenna systems,

    H. Xuet al., “Coding-enhanced cooperative jamming for secret commu- nication in fluid antenna systems,”IEEE Commun. Lett., vol. 28, no. 9, pp. 1991–1995, Sept. 2024

    work page 1991

  65. [65]

    Physical layer security over fluid antenna systems: Secrecy performance analysis,

    F. R. Ghadiet al., “Physical layer security over fluid antenna systems: Secrecy performance analysis,”IEEE Trans. Wireless Commun., vol. 23, no. 12, pp. 18201–18213, Dec. 2024

    work page 2024

  66. [66]

    Fluid antenna system: Secrecy outage probability analysis,

    J. D. Vega-S ´anchez, L. F. Urquiza-Aguiar, H. R. C. Mora, N. V . O. Garz´on, and D. P. M. Osorio, “Fluid antenna system: Secrecy outage probability analysis,”IEEE Trans. Veh. Technol., vol. 73, no. 8, pp. 11458–11469, Aug. 2024

    work page 2024

  67. [67]

    On performance of FAS-aided covert communications,

    F. Rostami Ghadi, M. Kaveh, R. J ¨antti, and F. J. L ´opez-Mart´ınez, “On performance of FAS-aided covert communications,” inProc. IEEE Veh. Technol. Conf. (VTC-Spring), 17-20 Jun. 2025, Oslo, Norway

    work page 2025

  68. [68]

    Fluid antenna system liberating multiuser MIMO for ISAC via deep reinforcement learning,

    C. Wanget al., “Fluid antenna system liberating multiuser MIMO for ISAC via deep reinforcement learning,”IEEE Trans. Wireless Commun., vol. 23, no. 9, pp. 10879–10894, Sept. 2024

    work page 2024

  69. [69]

    Fluid antenna-assisted ISAC systems,

    L. Zhou, J. Yao, M. Jin, T. Wu, and K. K. Wong, “Fluid antenna-assisted ISAC systems,”IEEE Wireless Commun. Lett., vol. 13, no. 12, pp. 3533– 3537, Dec. 2024

    work page 2024

  70. [70]

    Shifting the ISAC trade-off with fluid antenna sys- tems,

    J. Zouet al., “Shifting the ISAC trade-off with fluid antenna sys- tems,”IEEE Wireless Commun. Lett., vol. 13, no. 12, pp. 3479–3483, Dec. 2024

    work page 2024

  71. [71]

    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 Netw., vol. 39, no. 2, pp. 278–285, Mar. 2025

    work page 2025

  72. [72]

    Diffusion models for wireless communications,

    M. Letafati, S. Ali, and M. Latva-aho, “Diffusion models for wireless communications,”arXiv preprint, arXiv:2310.07312, 2023

    work page arXiv 2023

  73. [73]

    CDDM: Channel denoising diffusion models for wireless semantic communications,

    T. Wuet al., “CDDM: Channel denoising diffusion models for wireless semantic communications,”IEEE Trans. Wireless Commun., vol. 23, no. 9, pp. 11168–11183, Sept. 2024

    work page 2024

  74. [74]

    Diffusion- based generative prior for low-complexity MIMO channel estima- tion,

    B. Fesl, M. Baur, F. Strasser, M. Joham, and W. Utschick, “Diffusion- based generative prior for low-complexity MIMO channel estima- tion,”IEEE Wireless Commun. Lett., vol. 13, no. 12, pp. 3493–3497, Dec. 2024

    work page 2024

  75. [75]

    GDM4MMIMO: Generative diffusion models for massive MIMO communications,

    Z. Jinet al., “GDM4MMIMO: Generative diffusion models for massive MIMO communications,”arXiv preprint, arXiv:2412.18281, 2024

    work page arXiv 2024

  76. [76]

    Enhancing deep reinforcement learning: A tutorial on generative diffusion models in network optimization,

    H. Duet al., “Enhancing deep reinforcement learning: A tutorial on generative diffusion models in network optimization,”IEEE Commun. Surv. & Tut., vol. 26, no. 4, pp. 2611–2646, Fourthquarter 2024

    work page 2024

  77. [77]

    Erasing noise in signal detection with diffusion model: From theory to application,

    X. Wang, P. Zheng, and N. Cheng, “Erasing noise in signal detection with diffusion model: From theory to application,”arXiv preprint, arXiv: 2501.07030, 2025

    work page arXiv 2025