用图神经网络优化太赫兹通信中的混合波束成形，提升效率并抗波束倾斜。
Graph Neural Network Based Hybrid Beamforming Design in Wideband Terahertz MIMO-OFDM Systems
- 用图神经网络建模模拟与数字波束成形矩阵，降低计算开销。
- 在宽频太赫兹MIMO-OFDM系统中逼近全数字波束成形性能。
- 实时适应性强，对波束倾斜不敏感，适合6G高频场景。
6G无线技术预计将采用更高更宽的频段，依赖高度定向的波束成形。然而，大规模多输入多输出（MIMO）系统中巨大的带宽导致波束倾斜效应不可忽视。传统方法如为每个天线添加真时延线（TTD）因阵列规模庞大而成本高昂。本文提出一种针对OFDM多载波结构的信号处理新方案，通过创新应用图神经网络（GNN）优化混合波束成形。通过引入两类图节点分别表示模拟和数字波束成形矩阵，该方法显著降低计算与内存负担，同时实现接近全数字波束成形的高谱效率。GNN的运行时间和内存需求仅为传统方法的极小部分，支持混合波束成形的实时调整。此外，所提GNN对波束倾斜具有强鲁棒性，在更高载波频率下系统带宽增加时仍保持几乎恒定的谱效率。
原文摘要 · Abstract (English)
6G wireless technology is projected to adopt higher and wider frequency bands, enabled by highly directional beamforming. However, the vast bandwidths available also make the impact of beam squint in massive multiple input and multiple output (MIMO) systems non-negligible. Traditional approaches such as adding a true-time-delay line (TTD) on each antenna are costly due to the massive antenna arrays required. This paper puts forth a signal processing alternative, specifically adapted to the multicarrier structure of OFDM systems, through an innovative application of Graph Neural Networks (GNNs) to optimize hybrid beamforming. By integrating two types of graph nodes to represent the analog and the digital beamforming matrices efficiently, our approach not only reduces the computational and memory burdens but also achieves high spectral efficiency performance, approaching that of all digital beamforming. The GNN runtime and memory requirement are at a fraction of the processing time and resource consumption of traditional signal processing methods, hence enabling real-time adaptation of hybrid beamforming. Furthermore, the proposed GNN exhibits strong resiliency to beam squinting, achieving almost constant spectral efficiency even as the system bandwidth increases at higher carrier frequencies.
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