用图神经网络优化多基站多智能表面天线系统，提升速率与能效。
Spectral- and Energy-efficient Multi-BS Multi-RIS Pinching-antenna Systems: A GNN-based Approach
- 设计三阶段图神经网络，联合优化天线位置、相位和波束成形
- 相比基线方法，速率提升超20%，能效提高30%以上，推理仅需毫秒级
- 适用于大规模动态通信场景，适合无线网络优化研究者
本文研究多基站（multi-BS）多可重构智能表面（multi-RIS）辅助的夹紧天线（PA）系统中的协同下行传输，每个用户设备（UE）关联一个基站，每个基站配备可在平行波导上移动的夹紧天线。通过联合优化天线位置、RIS相位偏移、发射波束成形及基站-用户关联，在满足天线间距、功率预算和单位模相移约束条件下，构建了总速率（SR）和能量效率（EE）最大化问题。针对高度耦合的混合变量问题，提出一种三阶段图神经网络（GNN），融合异质与同质图表示，并以端到端无监督方式训练。大量数值结果表明，所提GNN持续优于典型系统与学习基线，对未见的用户数、RIS数和基站数具有强泛化能力，推理时间保持在毫秒级。结果从系统与架构双重角度验证了设计有效性。此外，夹紧天线显著提升SR与EE，性能增益随天线数量增加而扩大。
原文摘要 · Abstract (English)
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.
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