Deep Learning Optimization of Two-State Pinching Antennas Systems

TL;DR

This paper develops neural network-based methods to optimize the activation of two-state pinching antennas in waveguides, aiming to maximize communication rates while accounting for real-world uncertainties.

Contribution

It introduces a data-driven approach using neural networks to solve a complex combinatorial optimization problem for antenna activation, including robustness to user location uncertainty.

Findings

Neural network models effectively optimize antenna activation policies.

Proposed methods outperform baseline approaches in simulation.

Models demonstrate robustness under uncertain user locations.

Abstract

The evolution of wireless communication systems requires flexible, energy-efficient, and cost-effective antenna technologies. Pinching antennas (PAs), which can dynamically control electromagnetic wave propagation through binary activation states, have recently emerged as a promising candidate. In this work, we investigate the problem of optimally selecting a subset of fixed-position PAs to activate in a waveguide, when the aim is to maximize the communication rate at a user terminal. Due to the complex interplay between antenna activation, waveguide-induced phase shifts, and power division, this problem is formulated as a combinatorial fractional 0-1 quadratic program. To efficiently solve this challenging problem, we use neural network architectures of varying complexity to learn activation policies directly from data, leveraging spatial features and signal structure. Furthermore, we incorporate user location uncertainty into our training and evaluation pipeline to simulate realistic deployment conditions. Simulation results demonstrate the effectiveness and robustness of the proposed models.