# Spectral- and Energy-efficient Multi-BS Multi-RIS Pinching-antenna Systems: A GNN-based Approach

arXiv: https://arxiv.org/abs/2605.01307 · HTML: https://mlsanity.com/paper/2605.01307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 38 of its 44 references that are in the map.
- First posted on arXiv 2 May 2026.

## Builds on (most cited first, 30 of 38)

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- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead](https://mlsanity.com/paper/2004.09352/) (2020)
- [Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial](https://mlsanity.com/paper/2007.02759/) (2020)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
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- [Multicell MIMO Communications Relying on Intelligent Reflecting Surface](https://mlsanity.com/paper/1907.10864/) (2019)
- [Flexible-Antenna Systems: A Pinching-Antenna Perspective](https://mlsanity.com/paper/2412.02376/) (2024)
- [Movable Antennas for Wireless Communication: Opportunities and Challenges](https://mlsanity.com/paper/2306.02331/) (2023)
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- [Modeling and Performance Analysis for Movable Antenna Enabled Wireless Communications](https://mlsanity.com/paper/2210.05325/) (2022)
- [DeepTx: Deep Learning Beamforming with Channel Prediction](https://mlsanity.com/paper/2202.07998/) (2022)
- [Movable-Antenna Enhanced Multiuser Communication via Antenna Position Optimization](https://mlsanity.com/paper/2302.06978/) (2023)
- [A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs](https://mlsanity.com/paper/2407.03449/) (2024)
- [Graph Neural Network Enabled Pinching Antennas](https://mlsanity.com/paper/2502.05447/) (2025)
- [Intelligent Reflecting Surface Assisted Secrecy Communication: Is Artificial Noise Helpful or Not?](https://mlsanity.com/paper/1907.12839/) (2019)
- [Movable-Antenna Position Optimization: A Graph-based Approach](https://mlsanity.com/paper/2403.16886/) (2024)
- [GNN-Enabled Optimization of Placement and Transmission Design for UAV Communications](https://mlsanity.com/paper/2410.02277/) (2024)
- [Movable Antennas Meet Intelligent Reflecting Surface: Friends or Foes?](https://mlsanity.com/paper/2501.15880/) (2025)
- [Downlink Beamforming with Pinching-Antenna Assisted MIMO Systems](https://mlsanity.com/paper/2502.01590/) (2025)

## Abstract (arXiv)

This paper investigates coordinated downlink transmission in a multi-base station (multi-BS) multi-reconfigurable intelligent surface (multi-RIS)-assisted pinching-antenna (PA) system, where each user equipment (UE) is associated with a single BS and each BS is equipped with movable PAs deployed on parallel waveguides. We formulate sum rate (SR) and energy efficiency (EE) maximization problems by jointly optimizing PA placement, RIS phase shifts, transmit beamforming, and BS-UE association under constraints of inter-PA spacing, power budget, and unit-modulus phase shift. To address the resulting highly coupled mixed-variable problem, we propose a three-stage graph neural network (GNN) that integrates heterogeneous and homogeneous graph representations and is trained end-to-end in an unsupervised manner. Extensive numerical results demonstrate that the proposed three-stage GNN consistently outperforms representative system and learning baselines, generalizes well to unseen numbers of UEs, RISs, and BSs, and maintains millisecond-level inference time. Besides, the results validate the effectiveness of the proposed design from both system and architectural perspectives. Moreover, PAs are shown to enhance SR and EE, and the performance gain is enlarged with increasing number of PAs.