# Graph Neural Network Enabled Pinching Antennas

arXiv: https://arxiv.org/abs/2502.05447 · HTML: https://mlsanity.com/paper/2502.05447/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [RIS-Assisted Downlink Pinching-Antenna Systems: GNN-Enabled Optimization Approaches](https://mlsanity.com/paper/2511.20305/) (2025)
- [Deep Learning Optimization of Two-State Pinching Antennas Systems](https://mlsanity.com/paper/2507.06222/) (2025)
- [Pinching Antennas Meet AI in Next-Generation Wireless Networks](https://mlsanity.com/paper/2511.07442/) (2025)
- [Spectral- and Energy-efficient Multi-BS Multi-RIS Pinching-antenna Systems: A GNN-based Approach](https://mlsanity.com/paper/2605.01307/) (2026)

## Abstract (arXiv)

The pinching-antenna system is a novel flexible-antenna technology, which has the capabilities not only to combat large-scale path loss, but also to reconfigure the antenna array in a flexible manner. The key idea of pinching antennas is to apply small dielectric particles on a waveguide of arbitrary length, so that they can be positioned close to users to avoid significant large-scale path loss. This paper investigates the graph neural network (GNN) enabled transmit design for the joint optimization of antenna placement and power allocation in pinching-antenna systems. We formulate the downlink communication system equipped with pinching antennas as a bipartite graph, and propose a graph attention network (GAT) based model, termed bipartite GAT (BGAT), to solve an energy efficiency (EE) maximization problem. With the tailored readout processes, the BGAT guarantees a feasible solution, which also facilitates the unsupervised training. Numerical results demonstrate the effectiveness of pinching antennas in enhancing the system EE as well as the proposed BGAT in terms of optimality, scalability and computational efficiency.