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

TL;DR

This paper tackles AirComp performance degradation caused by channel misalignment by introducing Pinching Antenna Systems (PASS) to physically reshape channels through flexible PA placement. It formulates a PASS-aided AirComp system with $M$ waveguides and $N$ PAs per waveguide, and optimizes PA positions $V$, transmit power $P$, and decoding vector $w$ to minimize the MSE $\min_{w,P,V} \| w^H G(V) P - 1^T \|^2 + N\sigma^2 \|w\|^2$ using an alternating optimization (AO) approach. The optimization alternates among closed-form or convex updates for $w$, a KKT-based solution for $P$, and a Gauss-Seidel grid-search for $V$, yielding a monotone nonincreasing objective and convergence. Numerical results show substantial MSE improvements over benchmarks (Fixed PA, Conventional MIMO, Discrete PASS, PGD-based) as waveguide length, number of PAs, or user count increases, highlighting PASS as a low-cost, passive, and scalable tool for robust, high-precision AirComp in edge networks.

Abstract

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.

Figures (2)

• Figure 1: Schematic of PASS-aided AirComp.
• Figure 2: (a) Convergence performance of our proposed design. (b) MSE w.r.t. the waveguide length. (c) MSE w.r.t. the number of PAs. (d) MSE w.r.t. the number of users.