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arxiv: 2605.00306 · v1 · submitted 2026-05-01 · 💻 cs.IT · eess.SP· math.IT

Artificial-Noise Aided Design for Movable-Antenna Enabled Physical-Layer Service Integration

Zhifeng Tang , Guangchen Wang , Nan Yang , Xiangyun Zhou , Salman Durrani This is my paper

Pith reviewed 2026-05-09 19:24 UTC · model grok-4.3

classification 💻 cs.IT eess.SPmath.IT
keywords movable antennaartificial noisephysical-layer service integrationsecrecymulticastoptimization
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The pith

Movable antennas combined with artificial noise allow better secrecy when sending multicast and confidential messages together.

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

The paper introduces a design that lets movable antennas change their positions while artificial noise is shaped to interfere with eavesdroppers, all to support simultaneous reliable multicast and protected confidential transmission. The authors derive closed-form expressions for the artificial noise direction and its power share relative to the secret signal, which reduces the complexity of the joint problem. They then apply an alternating block coordinate ascent method that refines transmission variables and antenna locations in turn. A sympathetic reader would care because the approach promises stronger protection of private data without hurting the shared messages and with modest computation.

Core claim

By jointly exploiting the spatial reconfiguration of movable antennas and the interference-shaping role of artificial noise, the non-convex optimization of antenna positions and transmit variables can be solved via block coordinate ascent after deriving a closed-form artificial noise direction and power allocation ratio, yielding improved secrecy while meeting multicast reliability constraints.

What carries the argument

The block coordinate ascent scheme that alternates between closed-form transmit design (including artificial noise direction and power ratio) and movable antenna position optimization.

Load-bearing premise

The non-convex joint optimization of movable antenna positions and transmit variables can be solved effectively by alternating between the two subproblems without large losses from local optima.

What would settle it

Simulations that compare the proposed alternating scheme against a global optimizer or exhaustive search over antenna positions and show no secrecy improvement or slower convergence under perfect channel knowledge.

Figures

Figures reproduced from arXiv: 2605.00306 by Guangchen Wang, Nan Yang, Salman Durrani, Xiangyun Zhou, Zhifeng Tang.

Figure 1
Figure 1. Figure 1: plots the secrecy rate, RS, versus the AN-to￾confidential power ratio, ρ. We first observe that the optimal AN-to-confidential power allocation ratio derived in Lemma 2 achieves the maximum secrecy rate, which validates the accuracy of our analysis and its effectiveness in guiding power allocation design. We then observe that RS first increases and then decreases as ρ increases. This observation is due 0 0… view at source ↗
Figure 2
Figure 2. Figure 2: The secrecy rate, Rs, versus the BCA iteration index, t. to the inherent tradeoff introduced by AN. When ρ is small, increasing ρ enhances the AN power received at U2, thereby effectively degrading the eavesdropping capability of U2 and improving the secrecy rate. However, when ρ becomes large, further increasing ρ significantly reduces the power allocated to the confidential signal, which degrades the rec… view at source ↗
Figure 3
Figure 3. Figure 3: The secrecy rate, RS, versus the number of MAs, M. improve the secrecy rate, which is consistent with Remark 1. The secrecy rate improvement from deploying more MAs is more pronounced in the small M regime, but gradually diminishes for large M due to the limited aperture size. Specif￾ically, when M is small, newly added MAs can effectively explore distinct spatial locations to enhance beamforming or AN des… view at source ↗
read the original abstract

This paper pioneers a novel scheme for artificial-noise (AN)-aided movable-antenna (MA)-enabled physical-layer service integration (PLSI) to harmonize the simultaneous delivery of multicast and confidential messages. By jointly exploiting the spatial reconfiguration capability of MAs and the interference shaping capability of AN, we aim to enhance secrecy performance while guaranteeing multicast reliability. The joint design of MA positions and transmit variables results in a highly coupled and non-convex optimization problem. To address this, we first provide key insights into the role of spatial degrees of freedom in AN design. We then characterize the AN direction under a structured transmission design and derive a closed-form expression for the AN-to-confidential power allocation ratio, which significantly simplifies the overall design. To solve the resulting problem, we further develop a low-complexity block coordinate ascent (BCA)-based scheme that alternates between transmit design and MA position optimization. Numerical results demonstrate that the proposed scheme achieves significant secrecy performance gains with low computational complexity and fast convergence, highlighting its effectiveness for MA-enabled PLSI 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

2 major / 1 minor

Summary. The paper proposes an artificial-noise (AN) aided movable-antenna (MA) enabled physical-layer service integration scheme that jointly optimizes MA positions and transmit variables to deliver multicast and confidential messages simultaneously. It derives a closed-form AN-to-confidential power allocation ratio under a structured beamformer to simplify the non-convex problem and applies a block coordinate ascent (BCA) algorithm alternating between transmit design and MA position updates, claiming significant secrecy gains, low complexity, and fast convergence in numerical results.

Significance. If the numerical claims hold under robust optimization, the work offers a practical low-complexity method for MA-enabled secure PLSI by exploiting spatial reconfiguration and AN interference shaping; the closed-form AN power ratio is a clear strength that reduces design complexity and could enable efficient implementations in future wireless systems.

major comments (2)
  1. §IV: The BCA algorithm is applied to the non-convex MA-position subproblem (continuous positions in a finite region with secrecy-rate objective depending on channel vectors) but provides no multiple random restarts, statistics on final secrecy rates across initializations, or proof that iterates reach a stationary point of the joint problem; without this, the reported numerical gains cannot be confidently attributed to the scheme rather than favorable initialization.
  2. Numerical results (implied in abstract and §V): The performance claims lack reported simulation parameters, channel models, error bars, or explicit verification that the BCA avoids poor local optima, which directly impacts evaluation of the central secrecy-gain assertions.
minor comments (1)
  1. Abstract and §IV: The closed-form AN power ratio is presented as derived under structured design, but the manuscript should explicitly state whether this ratio is independent of the secrecy metric or introduces any dependence that could affect the alternation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and propose revisions to improve clarity and robustness where appropriate.

read point-by-point responses

  1. Referee: §IV: The BCA algorithm is applied to the non-convex MA-position subproblem (continuous positions in a finite region with secrecy-rate objective depending on channel vectors) but provides no multiple random restarts, statistics on final secrecy rates across initializations, or proof that iterates reach a stationary point of the joint problem; without this, the reported numerical gains cannot be confidently attributed to the scheme rather than favorable initialization.

    Authors: We acknowledge that the manuscript does not include multiple random restarts or a formal proof that the BCA iterates reach a stationary point of the joint non-convex problem. The joint optimization is non-convex, and establishing global convergence guarantees is difficult. However, the closed-form AN-to-confidential power allocation ratio derived under the structured beamformer reduces the coupling and stabilizes the iterates. Each block update is solved to a local optimum, and the algorithm exhibits fast convergence in all reported simulations. To strengthen the evaluation, we will add numerical results showing secrecy rates across multiple random MA initializations and a brief discussion of the observed convergence behavior based on block coordinate ascent properties. revision: partial

  2. Referee: Numerical results (implied in abstract and §V): The performance claims lack reported simulation parameters, channel models, error bars, or explicit verification that the BCA avoids poor local optima, which directly impacts evaluation of the central secrecy-gain assertions.

    Authors: Section V of the manuscript does specify the simulation parameters (antenna count, MA movement region, transmit power, noise variance, and channel model details). To improve reproducibility and address the concern, we will expand the simulation setup description into a dedicated subsection, add error bars to the performance curves (averaged over independent channel realizations), and incorporate the multiple-initialization results mentioned above to demonstrate that the reported secrecy gains are robust rather than initialization-dependent. revision: yes

Circularity Check

0 steps flagged

No circularity: closed-form AN ratio derived from structured characterization; BCA is standard solver.

full rationale

The paper derives the AN-to-confidential power allocation ratio by first providing insights on spatial degrees of freedom, then characterizing the AN direction under a structured transmission design. This is an analytical step that produces a closed-form expression to simplify the joint optimization, not a fit to the secrecy metric or a self-referential definition. The BCA alternation is an algorithmic procedure for the resulting non-convex problem and does not claim to predict quantities that reduce to its own inputs by construction. No self-citation chains, uniqueness theorems imported from prior author work, or ansatzes smuggled via citation are described as load-bearing. The numerical results are presented as validation of the scheme rather than part of the derivation itself. The chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The design rests on standard wireless assumptions such as perfect channel state information at the transmitter and the validity of the structured transmission model used to obtain the closed-form ratio; no new physical entities are introduced.

free parameters (2)
  • MA positions
    Continuous variables optimized jointly with transmit parameters; their final values are outputs of the BCA procedure rather than fixed inputs.
  • Transmit power variables
    Power allocation between multicast, confidential, and AN components; optimized subject to the derived ratio.
axioms (2)
  • domain assumption The joint MA-position and transmit-variable problem is non-convex yet amenable to block coordinate ascent with acceptable convergence.
    Invoked when the abstract states that the BCA scheme is developed to solve the resulting problem.
  • domain assumption Spatial degrees of freedom in AN design can be characterized under a structured transmission model.
    Stated as the first key insight provided before deriving the closed-form ratio.

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Reference graph

Works this paper leans on

21 extracted references · 1 canonical work pages · 1 internal anchor

  1. [1]

    6G wireless networks: Vision, requirements, ar chitecture, and key technologies,

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

    2019

  2. [2]

    Age of informat ion in downlink systems: Broadcast or unicast transmission?

    Z. Tang, N. Y ang, P . Sadeghi, and X. Zhou, “Age of informat ion in downlink systems: Broadcast or unicast transmission?” IEEE Journal on Selected Areas in Communications , vol. 41, no. 7, pp. 2057–2070, Jul. 2023

    2057

  3. [3]

    Movable antenna-enhanced wireless communications: Gene ral archi- tectures and implementation methods,

    B. Ning, S. Y ang, Y . Wu, P . Wang, W. Mei, C. Y uen, and E. Bj¨ o rnson, “Movable antenna-enhanced wireless communications: Gene ral archi- tectures and implementation methods,” IEEE Wireless Commun. Mag. , vol. 32, no. 5, pp. 108–116, Oct. 2025

    2025

  4. [4]

    A tutorial on movable antennas for wi reless networks,

    L. Zhu, W. Ma, W. Mei, Y . Zeng, Q. Wu, B. Ning, Z. Xiao, X. Sha o, J. Zhang, and R. Zhang, “A tutorial on movable antennas for wi reless networks,” IEEE Commun. Surveys Tuts. Mag. , vol. 28, pp. 3002–3054, 1st Quart. 2026

    2026

  5. [5]

    Modeling and performance ana lysis for movable antenna enabled wireless communications,

    L. Zhu, W. Ma, and R. Zhang, “Modeling and performance ana lysis for movable antenna enabled wireless communications,” IEEE Trans. Wireless Commun., vol. 23, no. 6, pp. 6234–6250, Jun. 2024

    2024

  6. [6]

    Movable antenna for wireless communications: Pr ototyping and experimental results,

    Z. Dong, Z. Zhou, Z. Xiao, C. Zhang, X. Li, H. Min, Y . Zeng, S . Jin, and R. Zhang, “Movable antenna for wireless communications: Pr ototyping and experimental results,” IEEE Trans. Wireless Commun. , vol. 25, pp. 6586–6599, Mar. 2026

    2026

  7. [7]

    Multiuser commu- nications with movable-antenna base station: Joint antenn a positioning, receive combining, and power control,

    Z. Xiao, X. Pi, L. Zhu, X.-G. Xia, and R. Zhang, “Multiuser commu- nications with movable-antenna base station: Joint antenn a positioning, receive combining, and power control,” IEEE Trans. Wireless Commun., vol. 23, no. 12, pp. 19 744–19 759, Dec. 2024

    2024

  8. [8]

    Movable antenna-aided hybrid beamforming for mu lti-user communications,

    Y . Zhang, Y . Zhang, L. Zhu, S. Xiao, W. Tang, Y . C. Eldar, an d R. Zhang, “Movable antenna-aided hybrid beamforming for mu lti-user communications,” IEEE Trans. V eh. Technol., vol. 74, no. 6, pp. 9899– 9903, Jun. 2025

    2025

  9. [9]

    Pre- optimized irregular arrays versus movable antennas in multi-user MIM O systems,

    A. Irshad, A. Kosasih, V . Petrov, and E. Bj¨ ornson, “Pre- optimized irregular arrays versus movable antennas in multi-user MIM O systems,” IEEE Wireless Commun. Lett., vol. 14, no. 8, pp. 2656–2660, Aug. 2025

    2025

  10. [10]

    Secure wirel ess communication via movable-antenna array,

    G. Hu, Q. Wu, K. Xu, J. Si, and N. Al-Dhahir, “Secure wirel ess communication via movable-antenna array,” IEEE Signal Process. Lett. , vol. 31, pp. 516–520, Jan. 2024

    2024

  11. [11]

    Su m rate maximization for movable antenna enhanced multiuser cover t commu- nications,

    H. Mao, X. Pi, L. Zhu, Z. Xiao, X.-G. Xia, and R. Zhang, “Su m rate maximization for movable antenna enhanced multiuser cover t commu- nications,” IEEE Wireless Commun. Lett. , vol. 14, no. 3, pp. 611–615, Mar. 2025

    2025

  12. [12]

    Movable antenna-aided se cure full-duplex multi-user communications,

    J. Ding, Z. Zhou, and B. Jiao, “Movable antenna-aided se cure full-duplex multi-user communications,” IEEE Trans. Wireless Commun. , vol. 24, no. 3, pp. 2389–2403, Mar. 2025

    2025

  13. [13]

    Secure MIM O communication relying on movable antennas,

    J. Tang, C. Pan, Y . Zhang, H. Ren, and K. Wang, “Secure MIM O communication relying on movable antennas,” IEEE Trans. Commun. , vol. 73, no. 4, pp. 2159–2175, Apr. 2025

    2025

  14. [14]

    Movable antennas-assisted secure transmission without e avesdroppers’ instantaneous CSI,

    G. Hu, Q. Wu, D. Xu, K. Xu, J. Si, Y . Cai, and N. Al-Dhahir, “Movable antennas-assisted secure transmission without e avesdroppers’ instantaneous CSI,” IEEE Trans. Mobile Comput. , vol. 23, no. 12, pp. 14 263–14 279, Dec. 2024

    2024

  15. [15]

    Physical layer service int egration in wireless networks: Signal processing challenges,

    R. F. Schaefer and H. Boche, “Physical layer service int egration in wireless networks: Signal processing challenges,” IEEE Signal Process. Mag., vol. 31, no. 3, pp. 147–156, May 2014

    2014

  16. [16]

    Joint power allocation and passive beamforming design for IRS-assisted physical-layer service integration,

    B. Ning, Z. Chen, Z. Tian, X. Wang, C. Pan, J. Fang, and S. L i, “Joint power allocation and passive beamforming design for IRS-assisted physical-layer service integration,” IEEE Trans. Wireless Commun. , vol. 20, no. 11, pp. 7286–7301, Nov. 2021

    2021

  17. [17]

    Mov able- antenna-enhanced physical-layer service integration: Pe rformance anal- ysis and optimization,

    X. Shen, X. Wei, W. Mei, Z. Chen, J. Fang, and B. Ning, “Mov able- antenna-enhanced physical-layer service integration: Pe rformance anal- ysis and optimization,” IEEE Wireless Commun. Lett. , vol. 14, no. 9, pp. 2952–2956, Sep. 2025

    2025

  18. [18]

    A unified converg ence analysis of block successive minimization methods for nons mooth optimization,

    M. Razaviyayn, M. Hong, and Z.-Q. Luo, “A unified converg ence analysis of block successive minimization methods for nons mooth optimization,” SIAM J. Optim., vol. 23, no. 2, pp. 1126–1153, Jun. 2013

    2013

  19. [19]

    Distributed Optimization-Learning with Graph Transformers for Terahertz Cell-Free Integrated Sensing and Communication Systems

    G. Wang, Z. Tang, N. Y ang, X. Hao, and Z. Han, “Distribute d optimization-learning with graph transformers for terahe rtz cell-free integrated sensing and communication systems,” Apr. 2026. [Online]. Available: https://arxiv.org/abs/2604.09981

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  20. [20]

    Rate-splitting multiple access: Fundamentals, sur vey, and future research trends,

    Y . Mao, O. Dizdar, B. Clerckx, R. Schober, P . Popovski, a nd H. V . Poor, “Rate-splitting multiple access: Fundamentals, sur vey, and future research trends,” IEEE Commun. Surveys Tuts. Mag. , vol. 24, no. 4, pp. 2073–2126, 4th Quart. 2022

    2073

  21. [21]

    On artificial-n oise-aided transmit design for multiuser MISO systems with integrated services,

    W. Mei, Z. Chen, L. Li, J. Fang, and S. Li, “On artificial-n oise-aided transmit design for multiuser MISO systems with integrated services,” IEEE Trans. V eh. Technol., vol. 66, no. 9, pp. 8179–8195, Sep. 2017

    2017