Self-Supervised graph attention–based antenna activation for pinching antenna systems under uncertainty

Abstract

The Pinching Antenna Systems (PAS) technology functions as a fully operational flexible antenna platform which supports future wireless network applications through its ability to switch radiation points along a waveguide using low-power electronic control. The system enables PAS adaptive beamforming through the activation of its individual pinching elements which results in less mechanical movement needed to create different beam patterns while saving energy and simplifying system design and increasing operational range compared to traditional array systems and adaptive intelligent surfaces. The process of identifying which antennas need to operate for optimal communication results in a NP-hard Quadratic Fractional 0–1 optimization challenge which requires extensive computational power making it impossible to use in active system operations. The current solutions to the problem depend on two main approaches which either use complete solvers or need supervised learning systems that depend on costly labeled training data which restricts their ability to expand and change. To develop a self-supervised framework which proposed Graph Attention Lagrangian-Reinforced Network (GALR-Net) to tackle these difficulties. The Graph-based model of PAS represents its antennas as graph nodes which use attention-based message passing to establish spatial relationships between them. The system uses a Straight-Through Estimator which makes it possible to use binary activation for differentiation while the physics-aware Lagrangian loss function maximizes the reachable rate without requiring actual ground-truth data. The results from our simulations show that GALR-Net achieves better performance than MLP and GNN baseline systems and all traditional methods. The proposed method achieves SNR optimality at 93–95% while enhancing antenna gain by 3.2 dB and achieving a 12–15% increase in radiation efficiency and a 40% decrease in beamforming errors and an approximate 22% improvement in spectral efficiency. The framework demonstrates its ability to handle arrays containing 1000 antennas while it maintains high performance standards when users experience localization errors between ±1 and 2 meters. The results show that physics-informed self-supervised learning enables scalable and operational solutions for real-time PAS optimization in upcoming 6G wireless networks.