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REVIEW 4 major objections 4 minor 31 references

Movable Antenna Aided Full-Duplex ISAC System with Self-Interference Mitigation

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Movable antennas can lift a full-duplex ISAC base station's combined rate and sensing performance by up to 64 percent over fixed-position antennas.

desk verdict A competent incremental MA-ISAC extension undone by an indexing error in the self-interference distance that undermines the headline gain. read the letter →

arxiv 2505.14830 v1 pith:AVXQWZ4C submitted 2025-05-20 cs.IT eess.SPmath.IT

Size Peng , Yin Xu , Guanli Yi , Cixiao Zhang , Dazhi He , Wenjun Zhang This is my paper · ORCID
classification cs.ITeess.SPmath.IT
keywords movableantennafull-duplexintegratedsensingandcommunicationself-interferencecancellationnear-fieldchannelalternatingoptimizationparticleswarmbeamforming
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 argues that in a mono-static integrated sensing and communication (ISAC) base station that transmits and receives at the same time, making the transmit and receive antennas movable turns self-interference into an optimizable quantity: by choosing antenna positions, the base station can physically reduce the self-interference channel strength while also steering beams for downlink, uplink, and radar sensing. The authors model a full-duplex ISAC system where the self-interference channel is a near-field function of antenna position vectors, then solve a weighted-sum maximization of downlink rate, uplink rate, and sensing mutual information. They propose an alternating-optimization algorithm with fractional programming for beamforming and power, and a particle-swarm-optimization (PSO) scheme that searches the movable region for better antenna placements. Simulations show the PSO design improves the weighted sum by up to 64.05% over a fixed-position antenna system, with larger gains in larger movable regions and at higher transmit powers. If this holds in hardware, it would make full-duplex ISAC practical without separate physical isolation hardware.

What carries the argument

The load-bearing mechanism is the position-dependent self-interference channel. The paper assumes $\mathbf{H}_{\mathrm{SI}}$ is not a fixed random matrix but a deterministic function of the antenna coordinates, given by $[\mathbf{H}_{\mathrm{SI}}]_{i,j} = \sqrt{[\eta_{\mathrm{SI}}]_{i,j}} e^{-j\frac{2\pi}{\lambda} r_{\mathrm{SI},i,j}}$, with near-field path loss $[\eta_{\mathrm{SI}}]_{i,j} = \frac{G_l}{4}\left[\left(\frac{\lambda}{2\pi r}\right)^2 - \left(\frac{\lambda}{2\pi r}\right)^4 + \left(\frac{\lambda}{2\pi r}\right)^6\right]$ and distance $r_{\mathrm{SI},i,j} = \sqrt{(x_{t,j} - x_{r,i} + d_{\mathrm{SI}})^2 + (y_{t,i} - y_{r,j})^2}$. This makes self-interference cancellation an antenna-position planning problem; moving a receive antenna by sub-wavelength distances changes both the interference path loss and the phase of the coupling. Around this mechanism, the algorithmic machinery is fractional programming (the quadratic transform of the weighted sum), closed-form KKT updates for beamformers and uplink powers, gradient ascent for antenna positions, and a particle-swarm search that integrates gradient ascent to refine local placements.

What would settle it

Measure the complex self-interference channel between a transmit and receive antenna as one antenna is moved over the simulated region at 30 GHz, compare the measured channel to (17)–(18), and then run the PSO-MA optimizer on the measured channel; if position optimization does not produce a weighted-sum gain comparable to the simulated up-to-64% improvement, the central claim is falsified.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a mono-static full-duplex ISAC system can use movable antennas to jointly optimize communication and sensing, and that the antenna-position degrees of freedom materially improve self-interference cancellation. The authors model the self-interference channel as $[\mathbf{H}_{\mathrm{SI}}]_{i,j} = \sqrt{[\eta_{\mathrm{SI}}]_{i,j}} e^{-j\frac{2\pi}{\lambda} r_{\mathrm{SI},i,j}}$, with the near-field path-loss factor $[\eta_{\mathrm{SI}}]_{i,j}$ given by the three-term law in (17) and the distance $r_{\mathrm{SI},i,j}$ between each transmit-receive pair given by (18). Because every term in the objective—downlink SINR, uplink SINR, and sensing SCNR—depends on the transmit and receive steering vectors and on $\mathbf{H}_{\mathrm{SI}}$ through the antenna positions, the optimization over positions is an optimization over the spatial channel itself. The paper develops an FP-based alternating optimization that updates beamforming, uplink powers, auxiliary variables, and antenna positions via gradient ascent, and a PSO-based algorithm that searches the whole feasible region. Numerical results show the PSO-MA scheme achieving a weighted-sum gain of 64.05% over fixed-position antennas at 40 dBm uplink power, and a 15.6% gain as the movable region extends from $2\lambda$ to $8\lambda$.

Load-bearing premise

The load-bearing premise is that the self-interference channel is exactly the near-field free-space function of antenna positions given by (17)–(18), and that the base station knows all channel parameters perfectly; if real hardware adds unmodeled mutual coupling, impedance variations, or estimation error, the predicted gains from position optimization could shrink or vanish.

Editorial extensions

If this is right

  • Deploying movable antennas in a mono-static full-duplex ISAC base station improves the weighted sum of downlink rate, uplink rate, and sensing mutual information at every simulated power level and antenna count, reaching a 64.05% gain over fixed-position antennas at high uplink power.
  • The gain grows with the size of the movable region: PSO-MA improves by 15.6% as the region grows from $2\lambda$ to $8\lambda$, while fixed antennas see no change.
  • Position optimization can itself be a primary self-interference cancellation mechanism, because the objective function contains self-interference terms that depend directly on antenna coordinates, complementing beamforming-based suppression.
  • The AO-based algorithm yields only local gains because antennas drift only about $0.015$ m total and at most $0.45\lambda$ per step, whereas PSO systematically explores the feasible region.
  • The weighted-sum framework allows a tunable trade-off between communication and sensing, and position optimization favors the sensing task more easily because the sensing channel is single-path while communication channels are multi-path.

Reading between the lines

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

  • Editorial inference: The deterministic near-field SI model in (17)–(18) excludes mutual coupling, impedance mismatch, and channel estimation error; in a real array these effects add position-dependent terms that could either help or erode the predicted SIC gains, so hardware measurement is the natural next test.
  • Editorial inference: Because the gains come largely from phase and path-loss variation over sub-wavelength moves, the scheme should transfer to fluid-antenna or reconfigurable-aperture hardware, but the benefit will depend on how finely and repeatably positions can be set.
  • Editorial inference: The same position-versus-interference coupling could be applied to bi-static or distributed ISAC, where the inter-node interference channel is also geometry-dependent, not just the intra-node self-interference.
  • Testable extension: run the optimizer with a measured HSI map as input instead of (17)–(18); if the algorithm still finds beneficial positions, the approach is robust to modeling error.
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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

4 major / 4 minor

Summary. The paper considers a full-duplex mono-static ISAC base station with movable transmit and receive antennas, serving multiple uplink and downlink users while sensing a target amid clutters. Self-interference is modeled as a near-field function of antenna positions, and the authors formulate a weighted-sum maximization of downlink/uplink rates and sensing mutual information. They propose an alternating optimization (AO) algorithm based on fractional programming, with gradient-ascent updates for antenna positions, and a particle-swarm-optimization (PSO) variant to escape poor local optima. Numerical simulations report that the PSO-based method improves the objective by up to 64.05% over a fixed-position-antenna baseline at high uplink transmit power.

Significance. If the model and results are correct, the paper offers a useful extension of movable-antenna techniques to full-duplex ISAC with self-interference mitigation, an area where the existing literature is comparatively thin. The FP/AO derivation follows a standard recipe and the integration of PSO with local gradient refinement is a reasonable practical heuristic. The paper is clearly written and the simulation study covers several operating parameters. However, the central numerical claim currently rests on an internally inconsistent self-interference distance model, and the comparison baselines are not computationally matched, so the reported gains cannot be accepted as evidence for the claimed MA advantage without correction and re-simulation.

major comments (4)
  1. The self-interference distance definition is index-inconsistent. Since H_SI is an N_T x N_R matrix (it enters as H_SI^H F in Eq. (26)), the (i,j) entry must couple transmit antenna i to receive antenna j, so the physical distance should involve (x_{t,i}, y_{t,i}) and (x_{r,j}, y_{r,j}). Eq. (18), as written, defines r_{SI,i,j} = sqrt((x_{t,j}-x_{r,i}+d_SI)^2 + (y_{t,i}-y_{r,j})^2), which uses the x-coordinate of transmit antenna j with the x-coordinate of receive antenna i and the y-coordinate of transmit antenna i with the y-coordinate of receive antenna j; no entry corresponds to a single physical transmit-receive pair. Because this distance enters the near-field path loss in Eq. (17) and the gradients in Eqs. (55)-(56), the optimized antenna positions and the simulated SIC gains are not for the system described by the model. The authors should either correct Eq. (18) to r_{SI,i,j} = sqrt((x_{t,i}-x_{r,j}+d_SI)^2 + (y_{t,i}-y_{r,j})^2) or explicitly justify the alternative indexing convention. If the simulation code already uses the corrected distance, the manuscript must be updated; if it uses Eq. (18) literally, the reported results are for a different, unphysical system.
  2. The performance comparison gives the MA schemes substantially larger computational budgets than the FPA baseline. FPA is a single run of Algorithm 1 with fixed positions, AO-MA is one run of Algorithm 2, RI-MA is 300 independent restarts, and PSO-MA is 100 particles times 50 iterations with an embedded Algorithm 2 per particle. The headline gains (e.g., 64.05% over FPA in Fig. 6) therefore conflate the flexibility of movable antennas with the added search effort. To support the claim that MA itself is beneficial, the authors should compare against an FPA baseline with the same number of random restarts or otherwise equalize the total number of beamforming/position optimization iterations, and report the resulting gain.
  3. The simulation results appear to be based on a single random channel realization. Users and clutters are described as randomly situated, and RCS coefficients and path gains are drawn from standard complex Gaussian distributions, but the figures do not show Monte Carlo averages or error bars. Without averaging over many channel realizations, the claim of an 'up to 64.05%' improvement is not statistically supported and may reflect a favorable draw. The authors should rerun the experiments over multiple independent realizations and report mean performance with confidence intervals or box plots.
  4. The convergence claims are not fully justified. For Algorithm 2, the gradient-ascent position update is said to 'ensure the non-decreasing nature' of the objective, but the step-size reduction in lines 5-8 and 12-15 of Algorithm 2 is triggered only by constraint violation, not by an objective decrease, and there is no line-search or acceptance rule that guarantees monotonic improvement. For Algorithm 3, the inequality R_g^{(i+1)} >= R_g^{(i)} in Eq. (65) holds only for the stored global best if the update rule strictly compares fitness values, but the per-particle step runs Algorithm 2, which itself has no proved monotonicity, so the statement 'expected to be non-decreasing' is not a proof. Please either provide a formal monotonicity argument with a suitable step-size rule or weaken the convergence statement accordingly.
minor comments (4)
  1. There are several typographical errors in the gradient expressions: Eq. (55) contains '∇_{x_t} α_s' where the context requires '∇_{x_t} a_s', and expressions such as 'b_c^H w_{r,k} w_{r,k}^H b_c^H' appear dimensionally inconsistent; these should be corrected.
  2. The notation for the self-interference distance is inconsistent: Table I lists 'Self-interference distance r_SI = 0.2m' while Eq. (18) uses d_SI for the separation between transmit and receive regions; the paper should use one symbol consistently.
  3. The Output line of Algorithm 2 lists F, w_r, w_r, f_UL, p_t, p_t, which appears to be a typo: it should presumably include w_s and p_r instead of the duplicated symbols.
  4. When introducing the schemes, the text states that PSO-MA randomly generates N_p sets of antenna positions, but the reported gain and the PSO parameters (N_p=100, I_p=50) should be clarified so that the reader can see the total number of objective evaluations used by each scheme, including the per-particle Algorithm 2 calls.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the MA-SI model, objective, and algorithms are self-contained, with only a non-circular internal inconsistency in Eq. (18).

full rationale

The derivation is self-contained. The objective in (31) is built directly from the channel expressions in (3), (6), (14)-(15), and (16)-(18), where antenna positions enter explicitly and no parameter is fitted to reproduce the reported gains. The AO and PSO algorithms optimize beamforming, power, and antenna positions on the same simulated channel instances, which is a standard design-evaluation procedure and does not make the 64% improvement a statistically forced prediction. The paper's self-citations, notably [20] and [27], are related-work anchors and are not used as the justification for the SI model, the FP transformations, the gradient updates, or the PSO search. The near-field SI law is attributed to the external reference [29]. There is a genuine internal-coherence issue in Eq. (18): the x-coordinate uses (x_{t,j}-x_{r,i}) while the y-coordinate pairs (y_{t,i}-y_{r,j}), so the SI matrix entry does not correspond to the distance between the i-th transmit and j-th receive antenna as required by the H_SI dimensions in Eq. (26). This is a model-consistency and reproducibility concern that could affect the physical meaning of the optimized positions and the simulated SI cancellation, but it is not circularity: the optimized SI matrix remains a function of the optimized positions and is not a restatement of the objective or of a fitted target. Because no load-bearing step reduces to its own input, the circularity score is 0.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim depends on hand-chosen weights and scenario parameters, on strong CSI and SI modeling assumptions, and on an unproven monotonicity claim for the local search. No new physical entities are introduced.

free parameters (3)
  • Objective weights (omega_c_DL, omega_c_UL, omega_s) = 0.3, 0.3, 0.4 (Table I)
    User-chosen trade-off weights; the reported 8-64% gains are relative to this particular weighting and would change for other weights.
  • PSO and GA hyperparameters (c1, c2, omega_max, omega_min, delta_t, delta_r) = not reported
    Required to reproduce Algorithms 2 and 3 but no values are given; the claimed benefit of PSO-MA depends on these settings.
  • Simulation scenario parameters (distances, D0, region size, N_p, I_p, N_RI) = see Table I and Section IV
    Simulation settings chosen by hand; gains are scenario-dependent and no averaging over random realizations is reported.
assumptions (5)
  • standard math Fractional programming quadratic transform from [30] gives a concave surrogate whose KKT points are stationary points of the original objective.
    Used in Section III-A to derive closed-form updates; standard and not proven.
  • domain assumption Perfect instantaneous knowledge of all channel parameters (angles, distances, RCS, path gains) at the BS.
    The beamforming and position updates in Section III assume exact CSI; no channel estimation or uncertainty is modeled.
  • domain assumption Near-field SI channel model (17) from [29] correctly describes the mutual coupling between transmit and receive antennas over distance r_SI.
    The entire SIC-by-positioning benefit rests on this model.
  • domain assumption Target and clutter channels are single-path, and communication channels have Lp=10 paths with random gains; these choices define the simulation but are not validated.
    The simulation results in Section IV depend on these channel structural assumptions.
  • ad hoc to paper The objective is non-decreasing in each iteration of Algorithm 2, ensuring convergence.
    Asserted in Section III-E without a line search or proof; step-size backtracking only handles constraint violations, not objective decrease.

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Cite this review

Pith. "Pith review of Movable Antenna Aided Full-Duplex ISAC System with Self-Interference Mitigation." pith.science (2026). https://pith.science/paper/AVXQWZ4C

@misc{pith2026250514830,
  author       = {Pith},
  title        = {Pith review of: Movable Antenna Aided Full-Duplex ISAC System with Self-Interference Mitigation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AVXQWZ4C}},
  note         = {Machine review of arXiv:2505.14830}
}
read the original abstract

Movable antenna (MA) has shown significant potential for improving the performance of integrated sensing and communication (ISAC) systems. In this paper, we model an MA-aided ISAC system operating in a communication full-duplex mono-static sensing framework. The self-interference channel is modeled as a function of the antenna position vectors under the near-field channel condition. We develop an optimization problem to maximize the weighted sum of downlink and uplink communication rates alongside the mutual information relevant to the sensing task. To address this highly non-convex problem, we employ the fractional programming (FP) method and propose an alternating optimization (AO)-based algorithm that jointly optimizes the beamforming, user power allocation, and antenna positions at the transceivers. Given the sensitivity of the AO-based algorithm to the initial antenna positions, a PSO-based algorithm is proposed to explore superior sub-optimal antenna positions within the feasible region. Numerical results indicate that the proposed algorithms enable the MA system to effectively leverage the antenna position flexibility for accurate beamforming in a complex ISAC scenario. This enhances the system's self-interference cancellation (SIC) capabilities and markedly improves its overall performance and reliability compared to conventional fixed-position antenna designs.

Figures

Figures reproduced from arXiv: 2505.14830 by the authors.

Figure 1
Figure 1. Illustration of the proposed ISAC system aided with MA. [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Convergence of Algorithm 2 with different number of users. [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗
Figure 3
Figure 3. Total antenna moving distance in each iteration of Algorithm 2. [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (4 more)
Figure 8
Figure 8. Figure 8: ISAC performance with different number of receive antenna. [PITH_FULL_IMAGE:figures/full_fig_p012_8.png]
Figure 7
Figure 7. Figure 7: ISAC performance with different number of transmit antenna. [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 10
Figure 10. Figure 10: Uplink and downlink communication rate with different weight. [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]
Figure 11
Figure 11. Figure 11: ISAC performance with different feasible region size. [PITH_FULL_IMAGE:figures/full_fig_p013_11.png]

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Reference graph

Works this paper leans on

31 extracted references · 18 canonical work pages

  1. [1]

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

    F. Liu, Y . Cui, C. Masouros, J. Xu, T. X. Han, Y . C. Eldar, and S. Buzzi, “Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond,”IEEE J. Sel. Areas Commun., vol. 40, no. 6, pp. 1728–1767, Jun. 2022

    2022

  2. [2]

    C. Wang, X. You, X. Gao, X. Zhu, Z. Li, C. Zhang, H. Wang, Y . Huang, Y . Chen, H. Haas, J. S. Thompson, E. G. Larsson, M. D. Renzo, W. Tong, P. Zhu, X. Shen, H. V . Poor, and L. Hanzo, “On the road to 6g: Visions, JOURNAL OF LATEX CLASS FILES, VOL. 18, NO. 9, SEPTEMBER 2020 13 Fig. 10. Uplink and downlink communication rate with different weight. Fig. 11...

    work page 2020

  3. [3]

    Communication-sensing region for Cell-Free massive MIMO ISAC systems,

    W. Mao, Y . Lu, C.-Y . Chi, B. Ai, Z. Zhong, and Z. Ding, “Communication-sensing region for Cell-Free massive MIMO ISAC systems,”IEEE Trans. Wireless Commun., vol. 23, no. 9, pp. 12 396– 12 411, Sep. 2024

    work page 2024

  4. [4]

    Power allocation for massive MIMO-ISAC systems,

    B. Liao, H. Q. Ngo, M. Matthaiou, and P. J. Smith, “Power allocation for massive MIMO-ISAC systems,”IEEE Trans. Wireless Commun., vol. 23, no. 10, pp. 14 232–14 248, Oct. 2024

    work page 2024

  5. [5]

    Secure intelligent reflecting surface-aided integrated sensing and communica- tion,

    M. Hua, Q. Wu, W. Chen, O. A. Dobre, and A. L. Swindlehurst, “Secure intelligent reflecting surface-aided integrated sensing and communica- tion,”IEEE Trans. Wireless Commun., vol. 23, no. 1, pp. 575–591, Jan. 2024

    2024

  6. [6]

    Full-duplex noma- enabled integrated sensing and communication: Joint transmit and receive beamforming optimization,

    R. Li, L. Wang, K. Chen, L. Xu, and A. Fei, “Full-duplex noma- enabled integrated sensing and communication: Joint transmit and receive beamforming optimization,”IEEE Internet Things J., vol. 11, no. 16, pp. 27 015–27 029, Aug. 2024

    work page 2024

  7. [7]

    A survey on fundamental limits of integrated sensing and communication,

    A. Liu, Z. Huang, M. Li, Y . Wan, W. Li, T. X. Han, C. Liu, R. Du, D. K. P. Tan, J. Lu, Y . Shen, F. Colone, and K. Chetty, “A survey on fundamental limits of integrated sensing and communication,”IEEE Commun. Surveys Tuts., vol. 24, no. 2, pp. 994–1034, 2nd Quart. 2022

    work page 2022

  8. [8]

    Joint radar and communication design: Applications, state-of-the-art, and the road ahead,

    F. Liu, C. Masouros, A. P. Petropulu, H. Griffiths, and L. Hanzo, “Joint radar and communication design: Applications, state-of-the-art, and the road ahead,”IEEE Trans. Commun., vol. 68, no. 6, pp. 3834–3862, Jun. 2020

    2020

Show all 31 references
  1. [9]

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

    J. A. Zhang, M. L. Rahman, K. Wu, X. Huang, Y . J. Guo, S. Chen, and J. Yuan, “Enabling joint communication and radar sensing in mobile networks—a survey,”IEEE Commun. Surveys Tuts., vol. 24, no. 1, pp. 306–345, 1st Quart. 2022

    2022

  2. [10]

    A survey of self-interference management techniques for single frequency full duplex systems,

    C. D. Nwankwo, L. Zhang, A. Quddus, M. A. Imran, and R. Tafazolli, “A survey of self-interference management techniques for single frequency full duplex systems,”IEEE Access, vol. 6, pp. 30 242–30 268, 2018

    2018

  3. [11]

    In-band full-duplex technology: Techniques and systems survey,

    K. E. Kolodziej, B. T. Perry, and J. S. Herd, “In-band full-duplex technology: Techniques and systems survey,”IEEE Trans. Microw. Theory Techn., vol. 67, no. 7, pp. 3025–3041, Jul. 2019

    2019

  4. [12]

    Robust transmit beamforming for self-interference cancellation in STAR phased array systems,

    C. Shi, W. Pan, Y . Shen, and S. Shao, “Robust transmit beamforming for self-interference cancellation in STAR phased array systems,”IEEE Signal Process. Lett., vol. 29, pp. 2622–2626, 2022

    2022

  5. [13]

    Full-duplex communication for ISAC : Joint beamforming and power optimization,

    Z. He, W. Xu, H. Shen, D. W. K. Ng, Y . C. Eldar, and X. You, “Full-duplex communication for ISAC : Joint beamforming and power optimization,”IEEE J. Sel. Areas Commun., vol. 41, no. 9, pp. 2920– 2936, Sep. 2023

    2023

  6. [14]

    Robust energy efficient beamforming design for isac full-duplex communication systems,

    R. Allu, M. Katwe, K. Singh, T. Q. Duong, and C.-P. Li, “Robust energy efficient beamforming design for isac full-duplex communication systems,”IEEE Wireless Commun. Lett., vol. 13, no. 9, pp. 2452–2456, Sep. 2024

    2024

  7. [15]

    Movable antennas for wireless commu- nication: Opportunities and challenges,

    L. Zhu, W. Ma, and R. Zhang, “Movable antennas for wireless commu- nication: Opportunities and challenges,”IEEE Commun. Mag., vol. 62, no. 6, pp. 114–120, Jun. 2024

    2024

  8. [16]

    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

    1950

  9. [17]

    Movable-antenna enhanced multiuser communication via antenna position optimization,

    L. Zhu, W. Ma, B. Ning, and R. Zhang, “Movable-antenna enhanced multiuser communication via antenna position optimization,”IEEE Trans. Wireless Commun., vol. 23, no. 7, pp. 7214–7229, Jul. 2024

    2024

  10. [18]

    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 Commun. Surveys Tuts., 2025, Early Access

    2025

  11. [19]

    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 Process. Lett., vol. 31, pp. 516–520, 2024

    2024

  12. [20]

    Sum rate maximization for movable antenna-aided downlink RSMA systems,

    C. Zhang, S. Peng, Y . Xu, Q. Wu, X. Ou, X. Guo, D. He, and W. Zhang, “Sum rate maximization for movable antenna-aided downlink RSMA systems,”arXiv preprint arXiv:2408.00413, 2024

    2024 arXiv

  13. [21]

    Fluid antenna grouping-based index modulation: Transceiver design for MIMO systems,

    X. Guo, Y . Xu, D. He, C. Zhang, W. Zhang, and Y . Wu, “Fluid antenna grouping-based index modulation: Transceiver design for MIMO systems,”arXiv preprint arXiv:2412.04877, 2024

    2024 arXiv

  14. [22]

    Modeling and performance analysis for movable antenna enabled wireless communications,

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

    2024

  15. [23]

    MIMO capacity characterization for movable antenna systems,

    W. Ma, L. Zhu, and R. Zhang, “MIMO capacity characterization for movable antenna systems,”IEEE Trans. Wireless Commun., vol. 23, no. 4, pp. 3392–3407, Apr. 2024

    2024

  16. [24]

    Multiuser communications with movable-antenna base station: Joint antenna positioning, receive combining, and power control,

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

    2024

  17. [25]

    Near-field multiuser communications aided by movable antennas,

    J. Ding, L. Zhu, Z. Zhou, B. Jiao, and R. Zhang, “Near-field multiuser communications aided by movable antennas,”IEEE Wireless Commun. Lett., vol. 14, no. 1, pp. 138–142, Jan. 2025

    2025

  18. [26]

    Flexible beamforming for movable antenna-enabled integrated sensing and com- munication,

    W. Lyu, S. Yang, Y . Xiu, Z. Zhang, C. Assi, and C. Yuen, “Flexible beamforming for movable antenna-enabled integrated sensing and com- munication,”arXiv preprint arXiv:2405.10507, 2024

    2024 arXiv

  19. [27]

    Joint antenna position and beamforming optimization with self-interference mitigation in MA-ISAC system,

    S. Peng, C. Zhang, Y . Xu, Q. Wu, X. Ou, and D. He, “Joint antenna position and beamforming optimization with self-interference mitigation in MA-ISAC system,”arXiv preprint arXiv:2411.08509, 2024

    2024 arXiv

  20. [28]

    Movable-antenna aided secure transmission for RIS-ISAC systems,

    Y . Ma, K. Liu, Y . Liu, L. Zhu, and Z. Xiao, “Movable-antenna aided secure transmission for RIS-ISAC systems,”arXiv preprint arXiv:2410.03426, 2024

    2024 arXiv

  21. [29]

    Near field propagation law & a novel fundamental limit to antenna gain versus size,

    H. Schantz, “Near field propagation law & a novel fundamental limit to antenna gain versus size,” in2005 IEEE Antennas and Propagation Society International Symposium, vol. 3A, 2005, pp. 237–240 vol. 3A

    2005

  22. [30]

    Fractional programming for communication sys- tems—part I: Power control and beamforming,

    K. Shen and W. Yu, “Fractional programming for communication sys- tems—part I: Power control and beamforming,”IEEE Trans. Signal Process., vol. 66, no. 10, pp. 2616–2630, May 2018

    2018

  23. [31]

    Intelligent reflecting surface aided MIMO broadcasting for simultaneous wireless information and power transfer,

    C. Pan, H. Ren, K. Wang, M. Elkashlan, A. Nallanathan, J. Wang, and L. Hanzo, “Intelligent reflecting surface aided MIMO broadcasting for simultaneous wireless information and power transfer,”IEEE J. Sel. Areas Commun., vol. 38, no. 8, pp. 1719–1734, Aug. 2020

    2020

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.