# GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising

arXiv: https://arxiv.org/abs/2511.06663 · HTML: https://mlsanity.com/paper/2511.06663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 40 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 20 of 20)

- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020)
- [MIMO Channel Estimation using Score-Based Generative Models](https://mlsanity.com/paper/2204.07122/) (2022)
- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Generative Semantic Communication: Diffusion Models Beyond Bit Recovery](https://mlsanity.com/paper/2306.04321/) (2023)
- [Edge Representation Learning with Hypergraphs](https://mlsanity.com/paper/2106.15845/) (2021)
- [Graph Embedding based Wireless Link Scheduling with Few Training Samples](https://mlsanity.com/paper/1906.02871/) (2019)
- [Co-embedding of Nodes and Edges with Graph Neural Networks](https://mlsanity.com/paper/2010.13242/) (2020)
- [Deep-Learning-based Millimeter-Wave Massive MIMO for Hybrid Precoding](https://mlsanity.com/paper/1901.06537/) (2019)
- [Hybrid Precoding for Multi-User Millimeter Wave Massive MIMO Systems: A Deep Learning Approach](https://mlsanity.com/paper/1911.04239/) (2019)
- [Multidimensional Graph Neural Networks for Wireless Communications](https://mlsanity.com/paper/2212.11531/) (2022)
- [GNN-Based Beamforming for Sum-Rate Maximization in MU-MISO Networks](https://mlsanity.com/paper/2311.03659/) (2023)
- [Agentic Graph Neural Networks for Wireless Communications and Networking Towards Edge General Intelligence: A Survey](https://mlsanity.com/paper/2508.08620/) (2025)
- [GBLinks: GNN-Based Beam Selection and Link Activation for Ultra-dense D2D mmWave Networks](https://mlsanity.com/paper/2107.02412/) (2021)
- [A Robust Deep Learning-Based Beamforming Design for RIS-assisted Multiuser MISO Communications with Practical Constraints](https://mlsanity.com/paper/2111.06555/) (2021)
- [Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems](https://mlsanity.com/paper/2312.05786/) (2023)
- [Joint User Scheduling and Beamforming Design for Multiuser MISO Downlink Systems](https://mlsanity.com/paper/2112.01738/) (2021)
- [A Data and Model-Driven Deep Learning Approach to Robust Downlink Beamforming Optimization](https://mlsanity.com/paper/2406.03098/) (2024)

## Abstract (arXiv)

Accurate Channel State Information (CSI) is critical for Hybrid Beamforming (HBF) tasks. However, obtaining high-resolution CSI remains challenging in practical wireless communication systems. To address this issue, we propose to utilize Graph Neural Networks (GNNs) and score-based generative models to enable robust HBF under imperfect CSI conditions. Firstly, we develop the Hybrid Message Graph Attention Network (HMGAT) which updates both node and edge features through node-level and edge-level message passing. Secondly, we design a Bidirectional Encoder Representations from Transformers (BERT)-based Noise Conditional Score Network (NCSN) to learn the distribution of high-resolution CSI, facilitating CSI generation and data augmentation to further improve HMGAT's performance. Finally, we present a Denoising Score Network (DSN) framework and its instantiation, termed DeBERT, which can denoise imperfect CSI under arbitrary channel error levels, thereby facilitating robust HBF. Experiments on DeepMIMO urban datasets demonstrate the proposed models' superior generalization, scalability, and robustness across various HBF tasks with perfect and imperfect CSI.