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.

翻译：本文研究了一种可重构智能表面（RIS）辅助的多波导夹持天线（PA）系统（PASS），用于多用户下行链路信息传输，其动机在于新兴PASS与RIS集成对无线通信的未知影响。首先，我们在统一框架下构建了和速率（SR）与能效（EE）最大化问题，约束条件包括PA可移动区域、总功率预算以及RIS单元的可调相位。随后，利用RIS辅助PASS的图结构拓扑，提出了一种新颖的三阶段图神经网络（GNN），该网络在前两个阶段分别基于用户位置学习PA位置，并根据复合信道条件学习RIS相移，最终确定波束赋形向量。具体而言，所提出的GNN通过无监督训练实现，并结合了三种与凸优化集成的实施策略，从而在推理时间与解的最优性之间提供权衡。大量数值结果验证了所提GNN的有效性，并支持其具备可行的泛化能力、良好的性能可靠性和实时适用性等独特属性。此外，本文还阐述并分析了关键参数对RIS辅助PASS的影响。