# RIS-Assisted Downlink Pinching-Antenna Systems: GNN-Enabled Optimization Approaches

arXiv: https://arxiv.org/abs/2511.20305 · HTML: https://mlsanity.com/paper/2511.20305/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 42 references that are in the map.
- First posted on arXiv 25 Nov 2025.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [Deep Learning in Mobile and Wireless Networking: A Survey](https://mlsanity.com/paper/1803.04311/) (2018)
- [Optimal Wireless Resource Allocation with Random Edge Graph Neural Networks](https://mlsanity.com/paper/1909.01865/) (2019)
- [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)
- [Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning](https://mlsanity.com/paper/2002.10072/) (2020)
- [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)
- [Learning to Reflect and to Beamform for Intelligent Reflecting Surface with Implicit Channel Estimation](https://mlsanity.com/paper/2009.14404/) (2020)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
- [Deep Reinforcement Learning Based Mode Selection and Resource Management for Green Fog Radio Access Networks](https://mlsanity.com/paper/1809.05629/) (2018)
- [Robust and Secure Wireless Communications via Intelligent Reflecting Surfaces](https://mlsanity.com/paper/1912.01497/) (2019)
- [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)
- [GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks](https://mlsanity.com/paper/2311.03659/) (2023)
- [Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation](https://mlsanity.com/paper/2404.11858/) (2024)
- [Agentic Graph Neural Networks for Wireless Communications and Networking Towards Edge General Intelligence: A Survey](https://mlsanity.com/paper/2508.08620/) (2025)
- [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)
- [GNN-Enabled Optimization of Placement and Transmission Design for UAV Communications](https://mlsanity.com/paper/2410.02277/) (2024)
- [Pinching-Antenna Systems (PASS)-enabled Secure Wireless Communications](https://mlsanity.com/paper/2504.13670/) (2025)
- [Pinching-Antenna Systems (PASS): A Tutorial](https://mlsanity.com/paper/2508.07572/) (2025)
- [Graph Neural Network Enabled Fluid Antenna Systems: A Two-Stage Approach](https://mlsanity.com/paper/2502.03922/) (2025)
- [Beam Training for Pinching-Antenna Systems (PASS)](https://mlsanity.com/paper/2502.05921/) (2025)
- [Pinching Antenna Systems versus Reconfigurable Intelligent Surfaces in mmWave](https://mlsanity.com/paper/2506.05102/) (2025)

## Built on by (most cited first, 1 of 1)

- [Spectral- and Energy-efficient Multi-BS Multi-RIS Pinching-antenna Systems: A GNN-based Approach](https://mlsanity.com/paper/2605.01307/) (2026)

## Abstract (arXiv)

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.