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arxiv: 2512.02461 · v2 · submitted 2025-12-02 · 💻 cs.IT · math.IT

Artificial-Noise-Aided Secure Near-Field MIMO With Fluid Antenna Systems

Peng Zhang , Jian Dang , Miaowen Wen , Ziyang Liu , Chen Zhao , Huaifeng Shi , Chengsheng Pan , Zaichen Zhang This is my paper

Pith reviewed 2026-05-17 02:57 UTC · model grok-4.3

classification 💻 cs.IT math.IT
keywords physical layer securityfluid antenna systemsnear-field MIMOartificial noisebeamformingport selectionhybrid beamforming
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0 comments X

The pith

Artificial noise and fluid antenna port selection improve secrecy in near-field MIMO systems.

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

The paper develops an artificial-noise-aided physical layer security scheme for near-field fluid-antenna MIMO systems to protect high-rate mobile links. It introduces an alternating-optimization framework that splits the problem into a continuous beamforming and artificial noise design solved by generalized spectral water-filling and a discrete port selection step using a row-energy prune-refit rule. This matters because it supplies extra position-domain flexibility for secrecy when arrays are compact or moderate in size and pure near-field focusing proves insufficient. Simulations indicate the approach exploits geometry and antenna positioning to raise secrecy rates under hardware constraints.

Core claim

The paper proposes an AN-aided PLS scheme for NF FA-MIMO systems. An AO framework addresses the sparsity-constrained non-convex design by splitting it into a continuous BF/AN joint-design subproblem and a discrete FAS port-selection subproblem. Closed-form fully digital BF/AN solutions are obtained via a generalized spectral water-filling procedure within a BCD surrogate and realized by a hardware-consistent HBF architecture with a shared RF network and independent digital BF/AN branches while preserving the target BF/AN power split under constant-modulus RF constraints. For FAS port selection a row-energy based prune-refit rule aligned with KKT conditions of a group-sparsity surrogate is in

What carries the argument

Alternating-optimization framework that alternates between continuous beamforming/artificial-noise joint design and discrete fluid-antenna port selection via row-energy prune-refit rule aligned with KKT conditions of a group-sparsity surrogate.

If this is right

  • The design exploits the geometry and position-domain degrees of freedom of fluid antenna systems.
  • Secrecy performance improves significantly particularly for non-extremely-large arrays where near-field beam focusing alone is inadequate.
  • Closed-form beamforming and artificial noise solutions are obtained while preserving power split under constant-modulus constraints.
  • The hybrid beamforming architecture supports practical hardware implementation with a shared RF network.
  • The approach offers a practical secure-transmission architecture for location-aware and hardware-constrained mobile systems.

Where Pith is reading between the lines

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

  • Position control in fluid antennas may complement artificial noise to achieve security without requiring extremely large arrays.
  • The port-selection rule could extend to dynamic user scenarios where antenna positions adapt in real time.
  • Similar sparsity-aware optimization might apply to other reconfigurable antenna or surface technologies for physical layer security.
  • Integration with user location data could further tailor the design for enhanced privacy in specific environments.

Load-bearing premise

The alternating-optimization framework converges reliably and the row-energy based prune-refit rule produces near-optimal port selections that satisfy the KKT conditions of the group-sparsity surrogate.

What would settle it

Simulations or measurements that show no meaningful secrecy-rate gain for non-extremely-large arrays when the proposed port selection and artificial noise are used versus standard near-field beamforming without port selection would refute the central claim.

Figures

Figures reproduced from arXiv: 2512.02461 by Chengsheng Pan, Chen Zhao, Huaifeng Shi, Jian Dang, Miaowen Wen, Peng Zhang, Zaichen Zhang, Ziyang Liu.

Figure 1
Figure 1. Figure 1: Illustration of FA port array at the transmitter and FPA array at the receiver. through positioning or integrated sensing and communi￾cation techniques [36], [37]. As a result, the CSIs of the Alice–Bob and Alice–Eve links are regarded as known. A. FA-MIMO based Near-Field Channel Model As illustrated in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustration of FA port array at the transmitter and FPA array at the receiver. phase of the response vector at the nu-th receive antenna, which arises from the propagation difference between the l-th FA port and the reference point. The distances dnu,l and dnu denote, respectively, the radial distances from the nu-th receive antenna element to the l-th FA port and to the reference point at the transmitter… view at source ↗
Figure 3
Figure 3. Figure 3: SC for FA–MIMO with L = 64 and Nt = 16; comparison with an FPA-based BF-only baseline, with ablations over array type (FA vs. FPA), AN co-design (with/without), and implementation (DBF vs. HBF after power balancing). independent realizations of the beamforming matrices and AN vectors, whose entries are initialized as independent complex Gaussian samples [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 6
Figure 6. Figure 6: presents the received signal power (RSP) and interference-plus-noise power (INP) distributions over free￾space locations at Pt = 10 dBm, illustrating the impact of beam focusing and AN in near-field FA–MIMO. For the small array configuration (L = 64, Nt = 16), Subfigure (a) shows the BF-only case, where a pronounced null is formed at Eve, with RSP values of −61.21 dBm at Eve and −40.53 dBm at Bob. When AN … view at source ↗
Figure 7
Figure 7. Figure 7: Port selection and per-port transmit power allocation with L = 128 candidate ports and Nt = 3 active ports: (a) FA array; (b) FPA array. VI. Conclusion This paper investigated AN-aided PLS for NF FA– MIMO systems with discretized FAS architectures. An AO-based framework was developed that jointly optimizes the BF/AN structure and selects a sparse set of FA port positions under an HBF implementation with co… view at source ↗
read the original abstract

With the evolution of mobile communication systems toward large-scale arrays, high-frequency operation, and reconfigurable antenna architectures, fluid antenna systems (FAS) operating in the near-field (NF) regime provide new degrees of freedom (DoF) for secure and privacy-sensitive mobile access. This paper proposes an artificial-noise (AN)-aided physical layer security (PLS) scheme for NF fluid-antenna multiple-input multiple-output (FA-MIMO) systems, aiming to protect high-rate mobile service links supported by compact or large arrays. An alternating-optimization (AO) framework addresses the sparsity-constrained non-convex design by splitting it into a continuous BF/AN joint-design subproblem and a discrete FAS port-selection subproblem. Closed-form fully digital beamforming (BF)/AN solutions are obtained via a generalized spectral water-filling procedure within a block coordinate descent (BCD) surrogate and realized by a hardware-consistent hybrid beamforming (HBF) architecture with a shared RF network and independent digital BF/AN branches, while preserving the target BF/AN power split under constant-modulus RF constraints. For FAS port selection, a row-energy based prune--refit rule, aligned with Karush--Kuhn--Tucker (KKT) conditions of a group-sparsity surrogate, enables efficient active-port determination under a finite RF-chain budget. Simulation results confirm that the proposed design exploits the geometry and position-domain DoF of FAS and significantly improves secrecy performance, particularly for non-extremely-large arrays where NF beam focusing alone is inadequate. These results demonstrate the potential of AN-aided NF FA-MIMO as a practical secure-transmission architecture for future location-aware and hardware-constrained mobile computing systems.

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

1 major / 2 minor

Summary. The paper proposes an artificial-noise-aided physical-layer security scheme for near-field fluid-antenna MIMO (FA-MIMO) systems. An alternating-optimization framework decomposes the sparsity-constrained non-convex problem into a continuous beamforming/AN joint-design subproblem solved by a generalized spectral water-filling procedure inside a block coordinate descent surrogate, and a discrete FAS port-selection subproblem solved by a row-energy prune-refit rule claimed to satisfy the KKT conditions of a group-sparsity surrogate. Closed-form fully digital solutions are realized via a hardware-consistent hybrid beamforming architecture with shared RF network and independent digital branches. Simulations are used to show that the design exploits geometry and position-domain DoF of FAS to improve secrecy rates, especially for non-extremely-large arrays where NF beam focusing alone is inadequate.

Significance. If the AO framework and prune-refit heuristic produce solutions whose secrecy performance is close to optimal, the work supplies a practical, hardware-aware method for secure NF transmission that augments conventional beam focusing with fluid-antenna position DoF. The closed-form BF/AN expressions and the explicit preservation of the target power split under constant-modulus constraints are concrete strengths that could aid reproducibility and implementation.

major comments (1)
  1. [FAS port-selection subproblem (Section IV)] The central secrecy-gain claims rest on simulation results generated by the AO framework whose discrete subproblem is solved by the row-energy prune-refit heuristic. The manuscript states that this rule is aligned with the KKT conditions of the group-sparsity surrogate, yet provides neither a convergence proof for the overall AO procedure to a stationary point of the original problem nor small-scale exhaustive-search comparisons or multi-initialization statistics that would confirm the heuristic yields near-optimal port selections under the finite RF-chain budget. This verification is load-bearing for the reported improvement over NF beam focusing alone, particularly in the non-extremely-large array regime highlighted in the abstract and conclusion.
minor comments (2)
  1. [System model (Section II)] The channel model assumptions (e.g., the precise near-field spherical-wave model and fluid-antenna position discretization) are only sketched; a short dedicated paragraph or table listing the key parameters and their ranges would improve clarity for readers outside the immediate subfield.
  2. [Numerical results (Section V)] Figure captions for the secrecy-rate versus SNR and versus array-size plots should explicitly state the number of Monte-Carlo realizations and the exact RF-chain budget used in each curve.

Simulated Author's Rebuttal

1 responses · 1 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the major comment below and indicate the revisions we will make to strengthen the empirical validation of the port-selection heuristic.

read point-by-point responses

  1. Referee: [FAS port-selection subproblem (Section IV)] The central secrecy-gain claims rest on simulation results generated by the AO framework whose discrete subproblem is solved by the row-energy prune-refit heuristic. The manuscript states that this rule is aligned with the KKT conditions of the group-sparsity surrogate, yet provides neither a convergence proof for the overall AO procedure to a stationary point of the original problem nor small-scale exhaustive-search comparisons or multi-initialization statistics that would confirm the heuristic yields near-optimal port selections under the finite RF-chain budget. This verification is load-bearing for the reported improvement over NF beam focusing alone, particularly in the non-extremely-large array regime highlighted in the abstract and conclusion.

    Authors: We appreciate the referee highlighting the importance of verifying the port-selection heuristic. The row-energy prune-refit rule is derived to satisfy the KKT conditions of the group-sparsity surrogate problem introduced in Section IV, providing a structured rather than purely heuristic approach to the discrete subproblem. The continuous BF/AN joint-design subproblem is solved to optimality at each iteration via the generalized spectral water-filling procedure within the BCD surrogate. We acknowledge that a formal convergence proof for the overall AO procedure to a stationary point of the original sparsity-constrained problem is not supplied, given the mixed continuous-discrete structure and the surrogate approximation in the discrete step. To address the verification request, the revised manuscript will include multi-initialization statistics for the port-selection procedure across multiple random starts in the primary simulation setups. We will also add small-scale exhaustive-search comparisons for instances with limited ports and RF chains (where exhaustive enumeration remains computationally feasible), demonstrating that the prune-refit rule achieves secrecy rates close to the exhaustive optimum. These additions will provide direct support for the reliability of the reported secrecy gains, especially in the non-extremely-large array regime. revision: partial

standing simulated objections not resolved
  • Formal convergence proof of the full AO procedure to a stationary point of the original non-convex problem

Circularity Check

0 steps flagged

No significant circularity; derivation uses standard optimization techniques and external models.

full rationale

The paper's alternating-optimization framework splits the problem into a continuous BF/AN subproblem solved via generalized spectral water-filling inside a BCD surrogate and a discrete port-selection subproblem handled by a row-energy prune-refit heuristic aligned with KKT conditions of a group-sparsity surrogate. These steps rely on established convex optimization tools and external near-field channel models rather than defining any secrecy metric or performance prediction in terms of itself. Simulation results validate gains without reducing outputs to fitted inputs or self-citations. The central claims remain independent of any internal redefinition or load-bearing self-reference.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard near-field channel models, perfect CSI assumptions, and convergence of block coordinate descent; no new physical entities are postulated.

free parameters (1)
  • RF-chain budget
    Finite number of RF chains that limits active FAS ports; value chosen to match hardware constraint.
axioms (2)
  • domain assumption Standard near-field spherical-wave channel model
    Invoked to capture geometry and position DoF of FAS in the NF regime.
  • domain assumption Convergence of alternating optimization to a stationary point
    Used to justify the AO framework splitting BF/AN design and port selection.

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Reference graph

Works this paper leans on

42 extracted references · 42 canonical work pages

  1. [1]

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

    C.-X. Wang, X. You, X. Gao, X. Zhu, Z. Li, and et al., “On the road to 6G: Visions, requirements, key technologies, and testbeds,” IEEE Commun. Surveys Tuts., vol. 25, no. 2, pp. 905–974, Secondquarter 2023

    work page 2023

  2. [2]

    Sensor-based continuous authentication of smartphones’ users using behavioral biometrics: A contemporary survey,

    M. Abuhamad, A. Abusnaina, D. Nyang, and D. Mohaisen, “Sensor-based continuous authentication of smartphones’ users using behavioral biometrics: A contemporary survey,” IEEE Internet Things J., vol. 8, no. 1, pp. 65–84, Jan. 2021

    work page 2021

  3. [3]

    Reliable intelligent reflecting surface- assisted mobile edge computing systems: A physical layer se- curity and encryption design,

    T. T. Nguyen, V. N. Ha, T.-D. Le, D.-D. Tran, S. Chatzino- tas, and K.-K. Nguyen, “Reliable intelligent reflecting surface- assisted mobile edge computing systems: A physical layer se- curity and encryption design,” IEEE Trans. Mobile Comput., early access, 2025, doi: 10.1109/TMC.2025.3607599

    work page doi:10.1109/tmc.2025.3607599 2025

  4. [4]

    Multi-RIS-aided VLC physical layer security for 6G wireless networks,

    S. Soderi, A. Brighente, S. Xu, and M. Conti, “Multi-RIS-aided VLC physical layer security for 6G wireless networks,” IEEE Trans. Mobile Comput., vol. 23, no. 12, pp. 15 182–15 195, Dec. 2024

    work page 2024

  5. [5]

    The Gaussian wire-tap channel,

    S. Leung-Yan-Cheong and M. Hellman, “The Gaussian wire-tap channel,” IEEE Trans. Inf. Theory, vol. 24, no. 4, pp. 451–456, Jul. 1978

    work page 1978

  6. [6]

    Power alloca- tion optimization for secure OFDM-NOMA downlink systems,

    H. Yue, C. Guo, Q. Li, H. Chen, and Q. Zhang, “Power alloca- tion optimization for secure OFDM-NOMA downlink systems,” IEEE Open J. Commun. Soc., vol. 6, pp. 7555–7566, Sept. 2025

    work page 2025

  7. [7]

    Joint artificial noise and repetition coding for secure wireless communications in TDD systems,

    H. He and P. Ren, “Joint artificial noise and repetition coding for secure wireless communications in TDD systems,” IEEE Wireless Commun. Lett., vol. 8, no. 6, pp. 1700–1703, Dec. 2019

    work page 2019

  8. [8]

    Joint RIS and beamforming design for secure and energy-efficient two-way relay communications,

    S. Zhao, X. Zhu, Y. Zhang, Z. Zhang, and Y. Shen, “Joint RIS and beamforming design for secure and energy-efficient two-way relay communications,” IEEE Trans. Mobile Comput., vol. 24, no. 8, pp. 7440–7457, Aug. 2025

    work page 2025

  9. [9]

    A Survey on Artificial Noise for Physical Layer Security: Opportunities, Technologies, Guidelines, Advances, and Trends,

    H. Niu, Y. Xiao, X. Lei, J. Chen, Z. Xiao, M. Li, and C. Yuen, “A survey on artificial noise for physical layer se- curity: Opportunities, technologies, guidelines, advances, and trends,” IEEE Commun. Surveys Tuts., early access, 2025, doi: 10.1109/COMST.2025.3610758

    work page doi:10.1109/comst.2025.3610758 2025

  10. [10]

    Secure beamforming for MIMO broadcasting with wireless information and power transfer,

    Q. Shi, W. Xu, J. Wu, E. Song, and Y. Wang, “Secure beamforming for MIMO broadcasting with wireless information and power transfer,” IEEE Trans. Wireless Commun., vol. 14, no. 5, pp. 2841–2853, May 2015

    work page 2015

  11. [11]

    Intelligent omni surface-assisted secure MIMO communication networks with artificial noise,

    S. Fang, G. Chen, Z. Abdullah, and Y. Li, “Intelligent omni surface-assisted secure MIMO communication networks with artificial noise,” IEEE Commun. Lett., vol. 26, no. 6, pp. 1231– 1235, Jun. 2022

    work page 2022

  12. [12]

    Artificial noise-aided secure gen- eralized spatial modulation for multiuser transmission,

    P. Yang, X. Qiu, and F. Mu, “Artificial noise-aided secure gen- eralized spatial modulation for multiuser transmission,” IEEE Commun. Lett., vol. 24, no. 11, pp. 2416–2420, Nov. 2020

    work page 2020

  13. [13]

    Secrecy energy efficiency max- imization for ARIS-assisted multiuser MISO systems,

    Y. Yi, X. Hu, and C. Kai, “Secrecy energy efficiency max- imization for ARIS-assisted multiuser MISO systems,” IEEE Commun. Lett., vol. 29, no. 3, pp. 467–471, Mar. 2025

    work page 2025

  14. [14]

    Joint secure beamforming and power splitting design for MIMO relay assisted over-the-air computation networks with imperfect CSI,

    H. Luo, Q. Li, and Q. Zhang, “Joint secure beamforming and power splitting design for MIMO relay assisted over-the-air computation networks with imperfect CSI,” IEEE Trans. Inf. Forensics Secur., vol. 19, pp. 7075–7090, Jul. 2024

    work page 2024

  15. [15]

    Near-field channel estimation in mixed LoS/NLoS environments for extremely large-scale MIMO sys- tems,

    Y. Lu and L. Dai, “Near-field channel estimation in mixed LoS/NLoS environments for extremely large-scale MIMO sys- tems,” IEEE Trans. Commun., vol. 71, no. 6, pp. 3694–3707, Jun. 2023

    work page 2023

  16. [16]

    Enabling more users to benefit from near-field communications: From linear to circular array,

    Z. Wu, M. Cui, and L. Dai, “Enabling more users to benefit from near-field communications: From linear to circular array,” IEEE Trans. Wireless Commun., vol. 23, no. 4, pp. 3735–3748, Apr. 2024

    work page 2024

  17. [17]

    Phys- ical layer security for near-field communications via directional modulation,

    J. Chen, Y. Xiao, K. Liu, Y. Zhong, X. Lei, and M. Xiao, “Phys- ical layer security for near-field communications via directional modulation,” IEEE Trans. Veh. Technol., vol. 73, no. 8, pp. 12 242–12 246, Aug. 2024

    work page 2024

  18. [18]

    Max–min secrecy rate optimization through beam focusing in near-field communications,

    A. A. Nasir, “Max–min secrecy rate optimization through beam focusing in near-field communications,” IEEE Commun. Lett., vol. 28, no. 7, pp. 1594–1598, Jul. 2024

    work page 2024

  19. [19]

    Performance analysis and low-complexity beamforming design for near-field physical layer security,

    Y. Zhang, Y. Fang, C. You, Y.-J. A. Zhang, and H. C. So, “Performance analysis and low-complexity beamforming design for near-field physical layer security,” IEEE Trans. Commun., early access, 2025, doi: 10.1109/TCOMM.2025.3626021

    work page doi:10.1109/tcomm.2025.3626021 2025

  20. [20]

    Low-complexity artificial noise-aided beam focusing design in near-field THz communications,

    Z. Tang, N. Yang, X. Zhou, S. Durrani, M. Juntti, and J. M. Jornet, “Low-complexity artificial noise-aided beam focusing design in near-field THz communications,” IEEE Trans. Veh. Technol., early access, 2025, doi: 10.1109/TVT.2025.3625557

    work page doi:10.1109/tvt.2025.3625557 2025

  21. [21]

    Wave- front hopping for physical layer security in 6G and beyond near- field THz communications,

    V. Petrov, H. Guerboukha, A. Singh, and J. M. Jornet, “Wave- front hopping for physical layer security in 6G and beyond near- field THz communications,” IEEE Trans. Commun., vol. 73, no. 5, pp. 2998–3011, May 2025

    work page 2025

  22. [22]

    Physical layer security in near-field communications,

    Z. Zhang, Y. Liu, Z. Wang, X. Mu, and J. Chen, “Physical layer security in near-field communications,” IEEE Trans. Veh. Technol., vol. 73, no. 7, pp. 10 761–10 766, Jul. 2024

    work page 2024

  23. [23]

    On fundamental limits for fluid antenna-assisted integrated sensing and communications for unsourced random access,

    Z. Zhang, K.-K. Wong, J. Dang, Z. Zhang, and C.-B. Chae, “On fundamental limits for fluid antenna-assisted in- tegrated sensing and communications for unsourced random access,” IEEE J. Sel. Areas Commun., early access, 2025, doi: 10.1109/JSAC.2025.3608113

    work page doi:10.1109/jsac.2025.3608113 2025

  24. [24]

    Fluid antenna-assisted rectangular differential index modulation: A non-coherent system design, optimization, and performance analysis,

    P. Zhang, J. Dang, M. Wen, Z. Zhang, L. Wu, and Y. Yao, “Fluid antenna-assisted rectangular differential index modulation: A non-coherent system design, optimization, and performance analysis,” IEEE J. Sel. Areas Commun., early access, 2025, doi: 10.1109/JSAC.2025.3616068

    work page doi:10.1109/jsac.2025.3616068 2025

  25. [25]

    On fundamental limits of slow-fluid antenna multiple access for unsourced random access,

    Z. Zhang, K.-K. Wong, J. Dang, Z. Zhang, C. Masouros, and C.- B. Chae, “On fundamental limits of slow-fluid antenna multiple access for unsourced random access,” IEEE Wireless Commun. Lett., vol. 14, no. 11, pp. 3455–3459, Nov. 2025. IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 0, NO. 0, AUGUST 2025 12

    work page 2025

  26. [26]

    F AS for secure and covert communications,

    J. Yao, L. Xin, T. Wu, M. Jin, K.-K. Wong, C. Yuen, and H. Shin, “F AS for secure and covert communications,” IEEE Internet Things J., vol. 12, no. 11, pp. 18 414–18 418, Jun. 2025

    work page 2025

  27. [27]

    Rectangular differential reflecting spatial modulation: A noncoherent joint index modulation of RIS-assisted MIMO system,

    P. Zhang, X. Jin, C. Wan, S. Xing, C. Huang, M. Wen, and Y. Yao, “Rectangular differential reflecting spatial modulation: A noncoherent joint index modulation of RIS-assisted MIMO system,” IEEE Trans. Commun., vol. 72, no. 12, pp. 7387–7400, Dec. 2024

    work page 2024

  28. [28]

    Space shift keying for pinching- antenna systems,

    Q. Li, M. Wen, and Z. Ding, “Space shift keying for pinching- antenna systems,” IEEE Wireless Commun. Lett., early access, 2025, doi: 10.1109/L WC.2025.3621491

    work page doi:10.1109/l 2025

  29. [29]

    Index modulation for covert transmission in continuous-trajectory F AS,

    M. Liu, Y. Xiao, L. Zhang, S. Yang, C. Wu, and X. Lei, “Index modulation for covert transmission in continuous-trajectory F AS,” IEEE Trans. Veh. Technol., early access, 2025, doi: 10.1109/TVT.2025.3612838

    work page doi:10.1109/tvt.2025.3612838 2025

  30. [30]

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

    F. R. Ghadi, K.-K. Wong, F. J. López-Martínez, W. K. New, H. Xu, and C.-B. Chae, “Physical layer security over fluid antenna systems: Secrecy performance analysis,” IEEE Trans. Wireless Commun., vol. 23, no. 12, pp. 18 201–18 213, Dec. 2024

    work page 2024

  31. [31]

    Secrecy performance analysis of RIS- aided fluid antenna systems,

    F. R. Ghadi, K.-K. Wong, M. Kaveh, F. J. López-Martínez, W. K. New, and H. Xu, “Secrecy performance analysis of RIS- aided fluid antenna systems,” in Proc. IEEE Wireless Commun. Netw. Conf. (WCNC), Milan, Italy, Mar. 2025, pp. 1–6

    work page 2025

  32. [32]

    Reliable and secure communications through compact ultra-massive antenna arrays,

    J. D. V. Sánchez, H. R. C. Mora, N. V. O. Garzón, and F. J. López-Martínez, “Reliable and secure communications through compact ultra-massive antenna arrays,” IEEE Open J. Commun. Soc., vol. 5, pp. 7641–7652, Nov. 2024

    work page 2024

  33. [33]

    Antenna trajectory-aware mechanical fluid antenna-enhanced secure wireless communications,

    B. Feng and Y. Wu, “Antenna trajectory-aware mechanical fluid antenna-enhanced secure wireless communications,” in Proc. IEEE 101st Veh. Technol. Conf. (VTC2025-Spring), Oslo, Norway, Jun. 2025, pp. 1–6

    work page 2025

  34. [34]

    Three- dimensional fluid antenna-assisted covert communications with friendly jamming,

    W. Chen, J. Luo, H. Ding, S. Wang, and F. Gong, “Three- dimensional fluid antenna-assisted covert communications with friendly jamming,” IEEE Trans. Wireless Commun., early ac- cess, 2025, doi: 10.1109/TWC.2025.3609276

    work page doi:10.1109/twc.2025.3609276 2025

  35. [35]

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

    J. Zhang, J. Rao, Z. Li, Z. Ming, C.-Y. Chiu, K.-K. Wong, K.- F. Tong, and R. Murch, “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

  36. [36]

    Sensing-assisted eavesdropper estimation: An ISAC breakthrough in physical layer security,

    N. Su, F. Liu, and C. Masouros, “Sensing-assisted eavesdropper estimation: An ISAC breakthrough in physical layer security,” IEEE Trans. Wireless Commun., vol. 23, no. 4, pp. 3162–3174, Apr. 2024

    work page 2024

  37. [37]

    Optimal beamforming for secure inte- grated sensing and communication exploiting target location distribution,

    K. Hou and S. Zhang, “Optimal beamforming for secure inte- grated sensing and communication exploiting target location distribution,” IEEE J. Sel. Areas Commun., vol. 42, no. 11, pp. 3125–3139, Nov. 2024

    work page 2024

  38. [38]

    S. P. Boyd and L. Vandenberghe, Convex Optimization. Cam- bridge Univ. Press, 2004

    work page 2004

  39. [39]

    Model selection and estimation in regression with grouped variables,

    M. Yuan and Y. Lin, “Model selection and estimation in regression with grouped variables,” J. Roy. Stat. Soc. B (Statist. Methodol.), vol. 68, no. 1, pp. 49–67, Feb. 2006

    work page 2006

  40. [40]

    Structured variable selection with sparsity-inducing norms,

    R. Jenatton, J.-Y. Audibert, and F. Bach, “Structured variable selection with sparsity-inducing norms,” J. Mach. Learn. Res., vol. 12, pp. 2777–2824, Nov. 2011

    work page 2011

  41. [41]

    Enhancing sparsity by reweighted ℓ1 minimization,

    E. J. Candès, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighted ℓ1 minimization,” J. Fourier Anal. Appl., vol. 14, no. 5, pp. 877–905, Dec. 2008

    work page 2008

  42. [42]

    Iterative hard thresholding for compressed sensing,

    T. Blumensath and M. E. Davies, “Iterative hard thresholding for compressed sensing,” Appl. Comput. Harmon. Anal., vol. 27, no. 3, pp. 265–274, Nov. 2009

    work page 2009