用图神经网络优化智能表面辅助的波导天线系统，提升多用户下行传输效率。
RIS-Assisted Downlink Pinching-Antenna Systems: GNN-Enabled Optimization Approaches
- 构建图神经网络，分三阶段学习天线位置、相位与波束成形。
- 在功率与可调相位约束下，实现频谱效率和能效双提升。
- 无需标注数据，适合实时部署，适用于复杂无线场景。
本文研究了智能表面（RIS）辅助的多波导夹持天线系统（PASS）在多用户下行通信中的应用，针对新兴PASS与RIS集成对无线通信影响尚不明确的问题。首先，在统一框架下建模总速率（SR）与能量效率（EE）最大化问题，受限于天线移动区域、总功率预算及RIS单元可调相位。随后，基于RIS-PASS的图结构拓扑，提出一种三阶段图神经网络（GNN），通过无监督训练学习用户位置对应的天线位置、复合信道条件下的RIS相位偏移，并最终确定波束成形向量；结合三种与凸优化融合的实现策略，可在推理速度与解优性间灵活权衡。大量数值结果验证了所提GNN的有效性，展现了良好的泛化能力、性能稳定性与实时适用性。同时分析了关键参数对RIS-PASS系统的影响。
原文摘要 · Abstract (English)
This paper investigates a reconfigurable intelligent surface (RIS)-assisted multi-waveguide pinching-antenna (PA) system (PASS) for multi-user downlink information transmission, motivated by the unknown impact of the integration of emerging PASS and RIS on wireless communications. First, we formulate sum rate (SR) and energy efficiency (EE) maximization problems in a unified framework, subject to constraints on the movable region of PAs, total power budget, and tunable phase of RIS elements. Then, by leveraging a graph-structured topology of the RIS-assisted PASS, a novel three-stage graph neural network (GNN) is proposed, which learns PA positions based on user locations, and RIS phase shifts according to composite channel conditions at the first two stages, respectively, and finally determines beamforming vectors. Specifically, the proposed GNN is achieved through unsupervised training, together with three implementation strategies for its integration with convex optimization, thus offering trade-offs between inference time and solution optimality. Extensive numerical results are provided to validate the effectiveness of the proposed GNN, and to support its unique attributes of viable generalization capability, good performance reliability, and real-time applicability. Moreover, the impact of key parameters on RIS-assisted PASS is illustrated and analyzed.
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