用图神经网络和得分模型，让无线通信在信号不全时也能精准波束成形。
GNN-Enabled Robust Hybrid Beamforming with Score-Based CSI Generation and Denoising
- 用图神经网络融合节点与边信息，提升信道状态估计精度。
- 通过得分模型生成高分辨率信道数据，增强模型泛化能力。
- 可处理任意误差水平的噪声信道，适合实际部署场景。
精确的信道状态信息（CSI）对混合波束成形（HBF）至关重要，但在实际无线通信系统中获取高分辨率CSI仍具挑战。为此，我们提出利用图神经网络（GNN）和基于得分的生成模型，在不完美CSI条件下实现鲁棒的HBF。首先，设计了混合消息图注意力网络（HMGAT），通过节点级与边级消息传递同时更新节点与边特征。其次，构建基于BERT的噪声条件得分网络（NCSN），学习高分辨率CSI分布，用于信道生成与数据增强，进一步提升HMGAT性能。最后，提出去噪得分网络（DSN）框架及其具体实现DeBERT，可在任意信道误差水平下对不完整信道进行去噪，从而支持鲁棒的HBF。在DeepMIMO城市数据集上的实验表明，所提模型在多种HBF任务中均展现出优异的泛化性、可扩展性与鲁棒性，无论在理想或非理想CSI条件下均表现优越。
原文摘要 · Abstract (English)
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.
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