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

2026-05-02

AI Summary

This work addresses the coupled optimization of antenna placement, reconfigurable intelligent surface (RIS) phase shifts, beamforming, and user association in multi-base-station, multi-RIS-assisted holographic MIMO systems. To tackle this high-dimensional mixed-variable problem, the authors propose an end-to-end unsupervised three-stage graph neural network (GNN) architecture that uniquely integrates heterogeneous and homogeneous graph representations for joint optimization. The proposed method significantly enhances both spectral and energy efficiency under practical hardware and power constraints, achieves millisecond-level inference latency, and demonstrates strong generalization across varying network scales. Experimental results show that it outperforms existing model-based and learning-based baselines in terms of overall system performance.

Abstract

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.

Problem

• multi-BS
• multi-RIS
• pinching-antenna
• sum rate
• energy efficiency

Innovation

• Graph Neural Network (GNN)
• Reconfigurable Intelligent Surface (RIS)
• Pinching Antenna (PA)
• Energy Efficiency
• Multi-BS Coordination