Learning Beamforming for Pinching Antenna System-Enabled ISAC in Low-Altitude Wireless Networks

TL;DR

This paper introduces a joint learning framework using graph neural networks to optimize antenna positioning and beamforming in low-altitude UAV networks, significantly improving sensing and communication performance with real-time capability.

Contribution

It proposes a novel GNN-based approach for joint optimization of antenna positions and beamforming in ISAC systems, outperforming traditional algorithms in efficiency and effectiveness.

Findings

GNN achieves comparable or better sensing performance than AO algorithm.

Proposed method enables real-time deployment with lower complexity.

Enhanced sensing and communication performance in UAV networks.

Abstract

This work investigates the joint learning of pinching antenna (PA) positions and transmit beamforming for PA-aided integrated sensing and communication (ISAC) in the low-altitude wireless networks. By freely deploying antenna positions along waveguides, the pinching antenna system effectively mitigates the impact of path loss and thus enhances the capacities of sensing and communicating unmanned aerial vehicles (UAVs) that fly over a large range. We first model the problem of maximizing the sensing performance of multiple targets while satisfying the communication performance requirements of multiple users, where both the targets and users are UAVs. For mitigating in-waveguide attenuation and improving sensing performance, the segmented waveguide-enabled pinching antenna (SWAN) system is adopted. Furthermore, an alternative optimization (AO) algorithm for SWAN-based ISAC (SWISAC-AO) is developed, where the optimal structure of the transmit beamforming solution is derived. A graph neural network (GNN), termed SWISAC-GNN, is then proposed to jointly learn PA positions and transmit beamforming, with its alternative update procedure inspired by the SWISAC-AO algorithm. Numerical results show that the GNN achieves sensing performance comparable to or better than the AO algorithm while better satisfying communication requirements. Moreover, the SWISAC-GNN is with much lower implementation complexity, enabling real-time deployment.