Robust Resource Allocation for Pinching-Antenna Systems under Imperfect CSI

George K. Karagiannidis; H. Vincent Poor; Ming Zeng; Xianbin Wang; Yuanwei Liu; Zhiguo Ding

REVIEW 3 major objections 4 minor 16 references

Robust Resource Allocation for Pinching-Antenna Systems under Imperfect CSI

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Under uncertain user locations, a pinching antenna can meet outage limits with near-optimal transmit power.

desk verdict Useful robust-formulation paper for pinching antennas, with a real but fixable correctness gap in the outage-area computation. read the letter →

arxiv 2507.12582 v1 pith:AU424PP6 submitted 2025-07-16 cs.IT eess.SPmath.IT

Ming Zeng , Xianbin Wang , Yuanwei Liu , Zhiguo Ding , George K. Karagiannidis , H. Vincent Poor This is my paper

classification cs.ITeess.SPmath.IT

keywords pinching-antennasystemsimperfectchannelstateinformationoutageprobabilitytransmitpowerminimizationparticleswarmoptimizationbisectionmethodgeometricregionuser-locationuncertainty

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

The paper establishes a robust power-allocation rule for a downlink system in which one pinching antenna slides along a dielectric waveguide and each user's true position is known only within a circular uncertainty region. The central claim is that, under a small outage threshold, the minimum transmit power for a user is fixed by a geometric condition: the area of the uncertainty circle that lies outside the antenna's projected coverage circle must equal $\epsilon_o\pi r^2$. This converts each probabilistic outage constraint into a deterministic equation $S_4(c)=\epsilon_o\pi r^2$, solved by bisection, and it lets the multi-user problem split into $K$ independent single-user power computations once the antenna position is chosen. A particle swarm optimization loop then positions the antenna, and the reported simulations show power consumption close to exhaustive search and far below a fixed-antenna baseline. The value of the result is that pinching antennas remain attractive in the realistic case where location information is imperfect.

What carries the argument

The workhorse is the projected coverage circle: Theorem 1 shows that the sphere of constant received rate centered at the pinching antenna intersects the user's uncertainty circle along an arc whose ground-plane radius is $c=\sqrt{R^2-d^2}$, centered at $(x_{\mathrm{Pin}},0,0)$. The area $S_4(c)$ of the uncovered part of the user circle is assembled from two sector areas and two congruent triangles, with Heron's formula for the triangle contribution, and its monotonicity justifies the bisection solution. This identity is what turns a probability constraint into a deterministic equation, and it makes the objective non-differentiable but cheap enough for particle swarm optimization to explore antenna positions.

What would settle it

Take a single user with uncertainty radius $r=3$ m and center distance $b=1$ m, compute the power prescribed by the bisection for a fixed $\epsilon_o$, then Monte Carlo sample actual user positions uniformly inside the uncertainty circle and count the fraction of samples whose rate falls below the target; if the empirical outage fraction meaningfully exceeds $\epsilon_o$, the geometric area reduction is not valid in that regime.

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Extended reading notes

Core claim

The specific discovery is that, for a fixed antenna position, the smallest power needed to keep user $k$ out of outage is $P_k^{\min} = (2^{\hat R_k}-1) R^2 \sigma_k^2 / \eta$, where $R$ is recovered from a pure area calculation. The paper proves that points at fixed rate from the pinching antenna project to a ground-plane circle of radius $c=\sqrt{R^2-d^2}$, and that the outage region is the part of the user's uncertainty circle outside this projected circle. Writing its area as $S_4(c)=(\pi-\beta)r^2 + 2\sqrt{s(s-b)(s-c)(s-r)}-\alpha c^2$, with $\alpha,\beta$ from the law of cosines and $s$ the semi-perimeter, the outage constraint becomes $S_4(c)=\epsilon_o\pi r^2$. Since $S_4(c)$ is decreasing in $c$, bisection on $[b,b+r]$ gives $c$, then $R=\sqrt{c^2+d^2}$, then the power directly. In the multi-user case this calculation is repeated for every user independently, and only the antenna position needs global optimization.

Load-bearing premise

The load-bearing premise is that the outage threshold is small enough for the required coverage-circle radius to lie in the interval $[b,b+r]$, so the user-circle center is inside the coverage circle and the closed-form area formula applies; the paper asserts this regime without formally characterizing how small $\epsilon_o$ must be.

Editorial extensions

If this is right

At a fixed pinching-antenna position, the $K$-user power-minimization problem decomposes into $K$ independent single-user bisection solves, so the method scales without solving a coupled program.
Total transmit power grows roughly linearly with the radius of the user-location uncertainty when the center distance $b$ dominates $r$, because the required $R^2$ behaves as $b^2+2b\delta r+\delta^2 r^2+d^2$.
The required power is nearly insensitive to the allowed outage probability over the small-$\epsilon_o$ range examined, because the feasible coverage radius only shifts within $[b,b+r]$.
The gap to a fixed antenna widens as target rates grow, since $P_k^{\min}$ scales as $(2^{\hat R_k}-1)R^2$ and the pinching antenna shrinks $R$ for each user.
Because the objective is non-differentiable, the same decomposition plus a swarm search applies to other per-user quality constraints that can be expressed as coverage-area equations.

Reading between the lines

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

For outage probabilities above about $0.5$, the required coverage radius can fall below the center distance $b$, so the bisection interval $[b,b+r]$ and the stated area formula need a separate case analysis; the paper's numerical regime deliberately stays away from this.
The same geometric reduction would carry to other position-error distributions only if the outage region is still a circle complement; for Gaussian or elliptical errors the outage probability becomes an integral over the disk intersection that would need numerical evaluation.
The common single antenna position couples all users through the choice of $x_{\mathrm{Pin}}$; allowing multiple pinching antennas or waveguides would break the per-user independence and enlarge the search space of the optimization.
A direct comparison of outage probability predicted by $S_4$ against Monte Carlo sampling over the uncertainty circle would isolate the geometric assumption from the optimization machinery, giving a clean test of the model.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is a solid, well-scoped paper on robust power allocation for pinching antennas under user-location uncertainty. The novelty is real—most prior pinching-antenna RA papers assume perfect user coordinates. The outage-constrained formulation and the geometric method for computing the outage area are new. It deserves peer review. But the paper as written has a correctness gap in the single-user power calculation that needs fixing before publication.

What the paper does well: the decomposition into independent single-user subproblems for a fixed antenna position is correct; the two-circle area calculus in Eqs. (5)-(11) is standard but accurately applied in the regime where the circles intersect in the assumed way; and the simulations show the PSO placement closely tracks exhaustive search and beats a fixed antenna by a large margin. The authors are also honest about the prior-work gap, and the citations to the pinching-antenna RA literature are relevant.

The soft spots, in order of importance.

First, the paper asserts that for small outage threshold ε_o the user-circle center lies inside the antenna circle (c≥b) and then uses [b,b+r] as the bisection interval, but it never proves the stricter condition c≥|b−r| that the formula (11) actually requires. The stress-test counterexample is valid: with r=1, b=0.1, ε_o=0.5, the root c≈0.707 is less than r−b=0.9, so the arccos arguments in (5) and (9) are outside [-1,1] and the published bisection cannot be evaluated. Since Section IV discusses ε_o up to 0.5, this isn't an edge case the authors can ignore. They need to characterize the valid regime or provide the correct alternative branch (e.g., the containment case) and adjust the bisection interval accordingly.

Second, the optimality of placing the antenna at xPin=x_k is asserted without proof. It is plausible for a uniform circular uncertainty region and free-space pathloss, but the paper does not justify it. This is a moderate issue; the multi-user PSO does not depend on it, so the main design survives.

Third, the bisection description contains a sign error. The text says that if S4(mid) is less than the target, the true c lies in the right half and we update the upper bound. Since S4 is decreasing in c, the correct update is to set the upper bound to mid. This is easy to fix, but as written it inverts the search direction.

Fourth, the PSO is a black box: no hyperparameters, no code, no sensitivity analysis. For a heuristic that claims near-optimality, this is a reproducibility gap, not a correctness one.

Who is this for? Researchers working on pinching-antenna resource allocation or robust position-aware communications. It is a serious piece of work, and the numerical claims for the default parameters (ε_o=0.01, r=3) appear to be in the valid regime. I would send it to a referee with expertise in geometric probability, and the revision should be major rather than minor because of the missing regime analysis.

Recommendation: accept for peer review with a major-revision recommendation.

Referee Report

3 major / 4 minor

Summary. The paper studies a downlink pinching-antenna system in which each user's location is known only up to a circular uncertainty region with a uniform distribution. The goal is to minimize total transmit power subject to per-user outage-probability constraints. For a single user, the authors propose fixing the antenna's x-coordinate to the user's estimated coordinate (or the waveguide boundary), then computing the minimum transmit power by solving a geometric outage-area equation S4(c)=epsilon_o*pi*r^2 via bisection. For multiple users, the power allocation for a fixed antenna position is decomposed into K independent single-user problems, and the common antenna position is optimized with particle swarm optimization. Simulations compare the proposed scheme against a fixed-antenna baseline and an exhaustive one-dimensional search, reporting near-optimal performance of the PSO-based placement.

Significance. If the technical gaps identified below are closed, the paper would provide a useful and elegant treatment of robust resource allocation for pinching-antenna systems under location uncertainty. The geometric reduction of the outage constraint to a two-circle area calculation is a clean idea, and the use of an independent exhaustive-search benchmark gives a credible check on the PSO heuristic. The numerical results suggest substantial power savings from antenna repositioning, which is the main practical message. The manuscript currently lacks a formal characterization of the regime in which the central geometric formulas apply, and the bisection update is described with a sign error; these issues are local and fixable, but they must be addressed before the claims can be accepted as stated.

major comments (3)

[Section III-A, bisection paragraph after Eq. (12)] The sector and triangle formulas used to define S4 presuppose that the two circles intersect, i.e., that c >= |b-r| and c <= b+r. The paper's small-epsilon argument establishes only that the user-circle center lies inside the antenna circle (c >= b), which is not sufficient when b < r: in that case the stricter condition c >= r-b is needed. This is not a cosmetic issue. For example, with r=1, b=0.1, and epsilon_o=0.5, the antenna circle is entirely contained in the user circle for all c <= 0.9, so the outage area is pi*(r^2-c^2); the root is c = sqrt(0.5) = 0.707, which is below r-b = 0.9. At this root the arccos arguments in Eqs. (5) and (9) are outside [-1,1], so the published S4 expression and the bisection procedure cannot be evaluated. The authors should characterize the admissible (b, r, epsilon_o) regime, enforce it in the algorithm, and either derive formulas for the non-intersecting branch or explicitly restrict the setting to parameters where the intersecting-circle condition holds. In particular, the discussion in Section IV that invokes epsilon_o <= 0.5 is not supported by the derivation.
[Section III-A, paragraph on optimal xPin] The bisection update is described with a sign error. The text states that if S4 at the midpoint is less than epsilon_o*pi*r^2, then 'the true value of c lies in the right half, and we update the upper bound.' Since S4 is a decreasing function of c, a value of S4 that is too small means that c is too large, so the true root lies to the left of the midpoint and the upper bound should be set to the midpoint. Conversely, if S4 is too large, the lower bound should be raised. As written, the described procedure would move the interval in the wrong direction and fail to converge to the root. This needs correction even though the default simulation parameters likely lie in the valid regime.
[Section III-A] The claim that the optimal single-user antenna position is xPin = x_k (or xPin = L when x_k > L) is asserted rather than proved. The argument that this minimizes the distance to the estimated user center is intuitive, but the objective is the minimum power satisfying an outage-area constraint, which depends on the full two-circle geometry through b = sqrt((xPin-x_k)^2 + y_k^2). The proof should show that the required value of c (and hence the required power) is monotonically increasing in b, or provide a direct optimality argument. This point is load-bearing because the abstract and conclusion state that the single-user optimal antenna position is derived.

minor comments (4)

[Section II-A, user-location model] The text says that for epsilon_o <= 0.5, 'the range of R satisfies b <= R <= b+r,' but the bisection variable is c = sqrt(R^2-d^2), not R. The condition should refer to c, and Section III-B does not actually contain the cited discussion; this appears to be a typo that should be corrected.
[Section II-A] The monotonicity statement 'it can be verified that S4 is a decreasing function of c' is used to justify the bisection but is not demonstrated. A brief argument based on the area of intersection of two circles as a function of their center distance would make the paper self-contained.
[Section IV] The model restricts the actual user location to the half-plane x >= 0, but the subsequent area calculations treat the uncertainty region as a full circle. If the uncertainty circle intersects the line x = 0, the outage area is a circular-segment calculation rather than the two-full-circle formula used here. The paper should state that the parameters are chosen so that this boundary effect is negligible, or handle it explicitly.
[Abstract] The sentence 'recent research have illustrated' contains a subject-verb agreement error; it should be 'recent research has illustrated.'

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the outage-area geometry and bisection power computation are derived from first principles, and the PSO placement is validated against an exhaustive-search baseline.

full rationale

The paper's central derivation chain is self-contained. The single-user outage area S4 is obtained from geometric identities (law of cosines, Heron's formula) in Eqs. (5)-(11), and the minimum transmit power follows by solving S4 = epsilon_o times pi times r^2 for c via bisection and then inserting R = sqrt(c^2+d^2) into the free-space rate expression (14). No fitted parameter is re-labelled as a prediction: r, b, epsilon_o, and the rate target are inputs, and the output P_min is a deterministic function of those inputs with no data-fitting step. The extension to K users is a genuine decomposition (independent per-user power constraints given xPin), and the PSO search over xPin is compared against an independent one-dimensional exhaustive search, so the near-optimality claim is benchmarked rather than assumed. The only mild self-reference is that the numerical setup instantiates the same LoS/TDMA model used in the derivation, which does not by itself validate the model; this is normal practice and not circular. A separate correctness gap exists: Sec. III asserts "due to the small value of the outage probability threshold epsilon_o (e.g., 0.05), the center of the user circle must lie within the antenna circle," supporting the bisection interval [b, b+r], but it never proves the stricter intersecting-circles condition required for Eqs. (5) and (9) to be well-defined (e.g., c >= |b-r| when b < r). Sec. IV similarly claims "for the considered range epsilon_o <= 0.5, the range of R satisfies b <= R <= b+r, as discussed in Section III-B," yet Section III-B does not prove this. This is an unproven-regime robustness issue, not a reduction of a result to its inputs, and therefore does not raise the circularity score.

Assumptions & free parameters 1 free parameters · 6 assumptions · 0 invented entities

The derivation introduces no fitted constants and no new physical entities. It rests on the free-space LoS model and the uniform circular location-uncertainty model inherited from prior pinching-antenna work, plus an ad hoc small-epsilon geometric regime and an empirical PSO convergence assumption.

free parameters (1)

PSO hyperparameters (swarm size, iterations, inertia weight, cognitive and social coefficients)
Not reported in the paper; the PSO implementation is deferred to standard MATLAB. The near-optimality claim depends on these hand-chosen or default settings, which are not controlled across the study.

assumptions (6)

domain assumption Actual user locations are uniformly distributed within circular uncertainty regions centered at the estimated coordinates.
Section II-A states this distribution; it is the basis for equating outage probability with the area fraction of the circle. If the distribution differs, all outage calculations change.
domain assumption The channel consists of a single LoS component with free-space path loss proportional to eta/distance^2, with no small-scale fading or blockage.
Eq. (1), inherited from Refs. [7], [8], [14]. The entire power expression and the sphere-based geometry rely on this idealized model.
domain assumption Users are served in TDMA with equal time allocation and share one common pinching antenna position on a single waveguide.
Section II-A, following Refs. [7], [8]. This makes per-user power independent once the antenna position is fixed.
ad hoc to paper For the small outage thresholds considered, the antenna's ground-projected circle contains the user-circle center (c >= b), so the two-circle segment formula and the bisection interval [b, b+r] apply.
Section III-A and Fig. 2. This condition is asserted from the small value of epsilon_o but never formally proven or given a parameter range.
domain assumption Actual user x-coordinates are non-negative (same side of the waveguide), so clipping xPin to [0, L] covers all relevant antenna positions.
Section II-A imposes xhat_k >= 0; used implicitly in the xPin=x_k clipping rule.
ad hoc to paper PSO with default MATLAB settings converges to a near-global optimum of the non-convex, non-differentiable antenna-position problem.
Section III-B and Figs. 3-5. This is an empirical claim, not proven; results depend on unstated hyperparameters.

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Cite this review

Pith. "Pith review of Robust Resource Allocation for Pinching-Antenna Systems under Imperfect CSI." pith.science (2026). https://pith.science/paper/AU424PP6

@misc{pith2026250712582,
  author       = {Pith},
  title        = {Pith review of: Robust Resource Allocation for Pinching-Antenna Systems under Imperfect CSI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AU424PP6}},
  note         = {Machine review of arXiv:2507.12582}
}

read the original abstract

Pinching-antenna technology has lately showcased its promising capability for reconfiguring wireless propagation environments, especially in high-frequency communication systems like millimeter-wave and terahertz bands. By dynamically placing the antenna over a dielectric waveguide, line-of-sight (LoS) connections can be made to significantly improve system performance. Although recent research have illustrated the advantages of pinching-antenna-assisted designs, they mainly presuppose complete knowledge of user locations -- an impractical assumption in real-world systems. To address this issue, the robust resource allocation in a multi-user pinching antenna downlink system with uncertain user positions is investigated, aiming to minimize total transmit power while satisfying individual outage probability constraints. First, we address the single-user case, deriving the optimal pinching antenna position and obtaining the corresponding power allocation using a bisection method combined with geometric analysis. We then extend this solution to the multi-user case. In this case, we optimize the pinching antenna position using a particle swarm optimization (PSO) algorithm to handle the resulting non-convex and non-differentiable optimization problem. Simulation results demonstrate that the proposed scheme outperforms conventional fixed-antenna systems and validate the effectiveness of the PSO-based antenna placement strategy under location uncertainty.

Figures

Figures reproduced from arXiv: 2507.12582 by the authors.

**Figure 2.** Illustration of a) the intersection between the user circle and pinching antenna sphere; and b) the intersection between the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Total power consumption versus target rate at the users. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 4.** Figure 4: Total power consumption versus the radius of the [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

**Figure 5.** Figure 5: Total power consumption versus the outage probability [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]

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Reference graph

Works this paper leans on

16 extracted references · 7 canonical work pages

[1]

Pinching antenna using a dielectric waveguide as an antenna,

A. Fukudaet al., “Pinching antenna using a dielectric waveguide as an antenna,”NTT DOCOMO Technical Journal, vol. 23, no. 3, pp. 5–12, 2022

work page 2022
[2]

Pinching antennas: Principles, applications and challenges,

Z. Yang, N. Wang, Y . Sun, Z. Ding, R. Schober, G. K. Karagiannidis, V . W. S. Wong, and O. A. Dobre, “Pinching antennas: Principles, applications and challenges,” 2025. [Online]. Available: https://arxiv.org/abs/2501.10753

arXiv 2025
[3]

Pinching-antenna systems (PASS): Architecture designs, opportunities, and outlook,

Y . Liu, Z. Wang, X. Mu, C. Ouyang, X. Xu, and Z. Ding, “Pinching-antenna systems (PASS): Architecture designs, opportunities, and outlook,” 2025. [Online]. Available: https://arxiv.org/abs/2501.18409

arXiv 2025
[4]

Resource allocation for pinching-antenna systems: State-of-the-art, key techniques and open issues,

M. Zeng, J. Wang, O. A. Dobre, Z. Ding, G. K. Karagiannidis, R. Schober, and H. V . Poor, “Resource allocation for pinching-antenna systems: State-of-the-art, key techniques and open issues,” 2025. [Online]. Available: https://arxiv.org/abs/2506.06156

arXiv 2025
[5]

Sum rate maximization for wireless powered pinching-antenna systems (PASS),

Y . Liet al., “Sum rate maximization for wireless powered pinching-antenna systems (PASS),”IEEE Commun. Lett., 2025, submitted

work page 2025
[6]

Sum rate maximization for NOMA-assisted uplink pinching-antenna systems,

M. Zeng, J. Wang, X. Li, G. Wang, O. A. Dobre, and Z. Ding, “Sum rate maximization for NOMA-assisted uplink pinching-antenna systems,” 2025. [Online]. Available: https://arxiv.org/abs/2505.00549

arXiv 2025
[7]

Flexible-antenna systems: A pinching- antenna perspective,

Z. Ding, R. Schober, and H. V . Poor, “Flexible-antenna systems: A pinching- antenna perspective,”IEEE Trans. Commun., pp. 1–1, 2025

work page 2025
[8]

A Low-Complexity Placement Design of Pinching-Antenna Systems

X. Xie, F. Fang, Z. Ding, and X. Wang, “A low-complexity placement design of pinching-antenna systems,” 2025. [Online]. Available: https://arxiv.org/abs/2502.14250

work page Pith review arXiv 2025

Show all 16 references

[9]

Antenna activation for NOMA assisted pinching-antenna systems,

K. Wang, Z. Ding, and R. Schober, “Antenna activation for NOMA assisted pinching-antenna systems,”IEEE Wirel. Commun. Lett., pp. 1–1, 2025

2025
[10]

Sum-rate maximization for pinching antenna-assisted NOMA systems with multiple dielectric waveguides,

S. Hu, R. Zhao, Y . Liao, D. W. K. Ng, and J. Yuan, “Sum-rate maximization for pinching antenna-assisted NOMA systems with multiple dielectric waveguides,” 2025. [Online]. Available: https://arxiv.org/abs/2503.10060

2025
[11]

Energy- efficient resource allocation for NOMA-assisted uplink pinching-antenna systems,

M. Zeng, X. Li, J. Wang, G. Huang, O. A. Dobre, and Z. Ding, “Energy- efficient resource allocation for NOMA-assisted uplink pinching-antenna systems,” 2025. [Online]. Available: https://arxiv.org/abs/2505.07555

2025 arXiv
[12]

Energy-efficient design for downlink pinching-antenna systems with QoS guarantee,

M. Zeng, J. Wang, G. Zhou, F. Fang, and X. Wang, “Energy-efficient design for downlink pinching-antenna systems with QoS guarantee,” 2025. [Online]. Available: https://arxiv.org/abs/2505.14904

2025 arXiv
[13]

Graph neural network enabled pinching antennas,

X. Xie, Y . Lu, and Z. Ding, “Graph neural network enabled pinching antennas,” 2025. [Online]. Available: https://arxiv.org/abs/2502.05447

2025 arXiv
[14]

Minimum data rate maximization for uplink pinching- antenna systems,

S. A. Tegoset al., “Minimum data rate maximization for uplink pinching- antenna systems,”IEEE Wirel. Commun. Lett., p. 1–1, 2025, early access

2025
[15]

Power minimization for noma-assisted pinching antenna systems with multiple waveguides,

Y . Fuet al., “Power minimization for noma-assisted pinching antenna systems with multiple waveguides,” 2025. [Online]. Available: https://arxiv.org/abs/2503.20336

2025 arXiv
[16]

Modeling and beamforming optimiza- tion for pinching-antenna systems,

Z. Wanget al., “Modeling and beamforming optimiza- tion for pinching-antenna systems,” 2025. [Online]. Available: https://arxiv.org/abs/2502.05917

2025 arXiv

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Pinching antenna using a dielectric waveguide as an antenna,

A. Fukudaet al., “Pinching antenna using a dielectric waveguide as an antenna,”NTT DOCOMO Technical Journal, vol. 23, no. 3, pp. 5–12, 2022

work page 2022

[2] [2]

Pinching antennas: Principles, applications and challenges,

Z. Yang, N. Wang, Y . Sun, Z. Ding, R. Schober, G. K. Karagiannidis, V . W. S. Wong, and O. A. Dobre, “Pinching antennas: Principles, applications and challenges,” 2025. [Online]. Available: https://arxiv.org/abs/2501.10753

arXiv 2025

[3] [3]

Pinching-antenna systems (PASS): Architecture designs, opportunities, and outlook,

Y . Liu, Z. Wang, X. Mu, C. Ouyang, X. Xu, and Z. Ding, “Pinching-antenna systems (PASS): Architecture designs, opportunities, and outlook,” 2025. [Online]. Available: https://arxiv.org/abs/2501.18409

arXiv 2025

[4] [4]

Resource allocation for pinching-antenna systems: State-of-the-art, key techniques and open issues,

M. Zeng, J. Wang, O. A. Dobre, Z. Ding, G. K. Karagiannidis, R. Schober, and H. V . Poor, “Resource allocation for pinching-antenna systems: State-of-the-art, key techniques and open issues,” 2025. [Online]. Available: https://arxiv.org/abs/2506.06156

arXiv 2025

[5] [5]

Sum rate maximization for wireless powered pinching-antenna systems (PASS),

Y . Liet al., “Sum rate maximization for wireless powered pinching-antenna systems (PASS),”IEEE Commun. Lett., 2025, submitted

work page 2025

[6] [6]

Sum rate maximization for NOMA-assisted uplink pinching-antenna systems,

M. Zeng, J. Wang, X. Li, G. Wang, O. A. Dobre, and Z. Ding, “Sum rate maximization for NOMA-assisted uplink pinching-antenna systems,” 2025. [Online]. Available: https://arxiv.org/abs/2505.00549

arXiv 2025

[7] [7]

Flexible-antenna systems: A pinching- antenna perspective,

Z. Ding, R. Schober, and H. V . Poor, “Flexible-antenna systems: A pinching- antenna perspective,”IEEE Trans. Commun., pp. 1–1, 2025

work page 2025

[8] [8]

A Low-Complexity Placement Design of Pinching-Antenna Systems

X. Xie, F. Fang, Z. Ding, and X. Wang, “A low-complexity placement design of pinching-antenna systems,” 2025. [Online]. Available: https://arxiv.org/abs/2502.14250

work page Pith review arXiv 2025