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Pinching-Antenna Systems (PASS) Aided Over-the-air Computation

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arxiv 2505.07559 v1 pith:7L7RCXH2 submitted 2025-05-12 cs.IT eess.SPmath.IT

classification cs.ITeess.SPmath.IT
keywords aircompantennapositioncomputationdesignhighlymisalignmentsover-the-air
verification ladder T0 review T1 audit T2 compute T3 formal
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Over-the-air computation (AirComp) enables fast data aggregation for edge intelligence applications. However the performance of AirComp can be severely degraded by channel misalignments. Pinching antenna systems (PASS) have recently emerged as a promising solution for physically reshaping favorable wireless channels to reduce misalignments and thus AirComp errors, via low-cost, fully passive, and highly reconfigurable antenna deployment. Motivated by these benefits, we propose a novel PASS-aided AirComp system that introduces new design degrees of freedom through flexible pinching antenna (PA) placement. To improve performance, we consider a mean squared error (MSE) minimization problem by jointly optimizing the PA position, transmit power, and decoding vector. To solve this highly non-convex problem, we propose an alternating optimization based framework with Gauss-Seidel based PA position updates. Simulation results show that our proposed joint PA position and communication design significantly outperforms various benchmark schemes in AirComp accuracy.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Analytical Optimization for Antenna Placement in Pinching-Antenna Systems

    cs.IT 2025-07 conditional novelty 4.0 of 10

    For fairness-based OMA, the optimal pinching-antenna location is the mean of users' x-coordinates and does not depend on their distance from the waveguide; for NOMA it moves exponentially toward the user nearest the w...

  2. A Gradient Meta-Learning Joint Optimization for Beamforming and Antenna Position in Pinching-Antenna Systems

    cs.IR 2025-06 conditional novelty 4.0 of 10

    A gradient meta-learning algorithm with two unrolled neural networks jointly optimizes beamforming and pinching-antenna positions, reporting 5.6 bits/s/Hz weighted sum rate and a 32.7% gain over alternating optimizati...

  3. MIMO Pinching-Antenna-Aided SWIPT

    cs.IT 2025-06 conditional novelty 4.0 of 10

    A joint beamforming and pinching-antenna position optimization for MIMO SWIPT is proposed, using WMMSE inner iterations and grid-search position updates.

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