arxiv: 2604.24985 · v1 · submitted 2026-04-27 · 📡 eess.SP

Recognition: unknown

Energy Efficiency Maximization for Discrete Activation based NOMA-assisted Pinching-Antenna Systems

Yishi Zhang , Aditya Powari , Kaidi Wang , Yaru Fu , Daniel K. C. So

Authors on Pith no claims yet

Pith reviewed 2026-05-08 01:44 UTC · model grok-4.3

classification 📡 eess.SP

keywords energy efficiencypinching-antenna systemsNOMApower allocationantenna activationdiscrete optimization

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The pith

Joint optimization of pinching antenna activation and power allocation maximizes energy efficiency in NOMA systems.

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

This paper examines energy efficiency maximization in a downlink NOMA-assisted pinching-antenna system where antennas activate in discrete on-off states. It jointly optimizes which antennas to activate and how to allocate transmit power to users while respecting quality-of-service and total power limits. The solution uses a two-layer iterative algorithm with matching-based selection on the outside and closed-form power allocation on the inside. If the approach holds, it demonstrates that including activation power costs produces materially higher efficiency than fixed-antenna baselines and reduces the need for exhaustive search. The work also shows that omitting activation power leads to designs that overestimate achievable efficiency.

Core claim

The paper claims that a two-layer iterative algorithm, with matching-based pinching-antenna selection in the outer layer and closed-form optimal power allocation in the inner layer, solves the mixed-integer nonlinear program for energy efficiency maximization under quality-of-service and transmit power constraints, yielding substantial gains over fixed-antenna and benchmark schemes while approaching exhaustive-search performance at lower complexity.

What carries the argument

Two-layer iterative algorithm that performs matching-based PA selection in the outer layer and computes closed-form power allocation in the inner layer.

If this is right

The algorithm delivers substantial energy efficiency gains over conventional fixed-antenna systems and the considered benchmark schemes.
It approaches the exhaustive-search upper bound while using significantly lower computational complexity.
The method exhibits fast convergence across the tested scenarios.
Accounting for pinching-antenna activation power is essential to avoid overestimating achievable energy efficiency.

Where Pith is reading between the lines

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

If activation power depends on hardware imperfections, the closed-form power solution would require an additional estimation step before each iteration.
The same two-layer structure could be tested in multi-cell or uplink settings to check whether the efficiency advantage persists at network scale.
Running the algorithm with measured activation power values from real pinching-antenna prototypes would provide a direct check on the numerical gains reported.

Load-bearing premise

The power consumed by activating a pinching antenna is a fixed, known constant that does not change with position or channel conditions.

What would settle it

Measure energy efficiency in a hardware testbed where pinching-antenna activation power is allowed to vary with temperature, position, or device tolerances; if the reported gains over fixed antennas disappear under these conditions, the closed-form inner solution and overall claims would not hold.

Figures

Figures reproduced from arXiv: 2604.24985 by Aditya Powari, Daniel K. C. So, Kaidi Wang, Yaru Fu, Yishi Zhang.

**Figure 1.** Figure 1: System model for downlink NOMA-assisted PASS. view at source ↗

**Figure 2.** Figure 2: EE performance for the proposed algorithm and the different bench view at source ↗

**Figure 3.** Figure 3: EE performance versus Pt under different Pact values and PA deployment densities with K = 4. proposed algorithm also achieves higher EE than the “Nearest” scheme, confirming the benefit of the matching design and the importance of activated PAs selection optimization. Moreover, it is also clear that the minimum power strategy performs poorly in EE as it does not balance between rate and power consumption view at source ↗

read the original abstract

Pinching-antenna systems (PASS) have recently attracted significant attention as a promising architecture for flexible and reconfigurable wireless communications. Despite notable advancements, research on energy efficiency (EE) maximization for PASS is limited as existing studies mainly focus on transmit power minimization or utilizing a simple power consumption model. This paper evaluates the impact of pinching antenna (PA) activation power on EE maximization in a downlink NOMA-assisted PASS by jointly optimizing PA activation and user power allocation under quality-of-service and transmit power constraints. To tackle the resulting mixed-integer nonlinear programming problem, we develop a two-layer iterative algorithm, where the outer layer performs matching-based PA selection and the inner layer computes a closed-form optimal power allocation solution. Numerical results demonstrate that the proposed solution achieves substantial EE gains over conventional fixed antennas systems and the considered benchmark schemes, approaches the exhaustive-search upper bound with significantly reduced complexity, while exhibiting fast convergence. It also demonstrates the significance of accounting for PA activation power in EE maximization problem.

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.

Desk Editor's Note private letter to a colleague

The paper gives a workable two-layer algorithm for EE in NOMA-PASS that approaches exhaustive search, but the gains rest on treating activation power as a fixed constant.

read the letter

The main thing here is that the authors built a two-layer algorithm for energy efficiency maximization in a downlink NOMA setup with discrete pinching-antenna activation. The outer layer uses matching to choose which antennas to turn on, and the inner layer delivers a closed-form power allocation under QoS and total power limits. Their simulations indicate it gets close to the exhaustive-search bound, converges quickly, and delivers clear EE gains over fixed-antenna baselines and other schemes, while showing that leaving activation power out of the model changes the picture.

Referee Report

1 major / 0 minor

Summary. The manuscript proposes a two-layer iterative algorithm to maximize energy efficiency in a downlink NOMA-assisted pinching-antenna system. The outer layer employs matching-based selection of discrete pinching-antenna activations, while the inner layer derives a closed-form power-allocation solution under QoS and total transmit-power constraints; the total power consumption model explicitly includes a PA activation power term. Numerical results are reported to show substantial EE gains versus fixed-antenna baselines and other benchmarks, near-optimality relative to exhaustive search at reduced complexity, rapid convergence, and the importance of including activation power.

Significance. If the modeling assumptions are valid, the work supplies an efficient, near-optimal method for EE optimization in an emerging reconfigurable-antenna architecture and quantifies the effect of activation power on the objective, which is a practically relevant modeling detail often omitted in prior PASS studies.

major comments (1)

[Power consumption model and inner-layer derivation] The closed-form inner-layer power allocation and the reported EE gains rest on modeling PA activation power as a fixed, position- and channel-independent constant. If activation power varies with PA location, hardware state, or instantaneous channel conditions, both the closed-form expression and the numerical superiority claims over benchmarks and the exhaustive-search bound cease to hold. This modeling choice is load-bearing for the central claims.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comment below and will incorporate clarifications in the revised manuscript.

read point-by-point responses

Referee: [Power consumption model and inner-layer derivation] The closed-form inner-layer power allocation and the reported EE gains rest on modeling PA activation power as a fixed, position- and channel-independent constant. If activation power varies with PA location, hardware state, or instantaneous channel conditions, both the closed-form expression and the numerical superiority claims over benchmarks and the exhaustive-search bound cease to hold. This modeling choice is load-bearing for the central claims.

Authors: We thank the referee for this observation. The system model in Section II explicitly defines the PA activation power as a fixed constant P_act per activated antenna, independent of position and instantaneous channel (see the total power consumption in Eq. (3)). This modeling choice enables the closed-form inner-layer solution for power allocation under the NOMA rate and total power constraints. All numerical results, including the EE gains and comparisons to exhaustive search, are obtained under this model. We agree that the closed-form derivation and associated claims are specific to the constant-activation-power case; if activation power varied with location or channel state, the problem would become more complex and the current closed-form would no longer apply. In the revised manuscript we will add an explicit statement of this assumption together with a short discussion of its implications and potential extensions in Section II. revision: yes

Circularity Check

0 steps flagged

No circularity: standard two-layer optimization algorithm with independent closed-form solution

full rationale

The paper formulates EE maximization as a mixed-integer nonlinear program and solves it via an outer matching-based PA selection layer combined with an inner closed-form power allocation derived from standard convex optimization under fixed activation power and QoS constraints. No step reduces by construction to a quantity defined by the authors' own fitted parameters, self-citations, or ansatz smuggled from prior work; the closed-form inner solution follows directly from the Lagrangian and KKT conditions applied to the given power consumption model. Numerical validation against exhaustive search and benchmarks is external and does not rely on self-referential definitions. The assumption that activation power is a known constant is a modeling choice, not a circular reduction in the derivation chain.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard domain assumptions about wireless propagation and power-consumption models rather than new free parameters or invented entities.

axioms (2)

domain assumption Pinching-antenna activation power is a fixed, position-independent constant
Invoked when formulating the total power consumption in the EE objective.
domain assumption Standard downlink NOMA channel and interference models hold
Used to express achievable rates inside the optimization problem.

pith-pipeline@v0.9.0 · 5482 in / 1432 out tokens · 75714 ms · 2026-05-08T01:44:11.839980+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

17 extracted references · 3 canonical work pages · 1 internal anchor

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