用于多载波宽带混合波束成形优化的高效图神经网络
Efficient Graph Neural Networks for Multicarrier Wideband Hybrid Beamforming Optimization
浏览论文内容
中文总结 AI 辅助
本文提出用图神经网络优化多载波宽带混合波束成形，通过三种结构设计应对波束斜视，性能优于传统方法和现有ML方案，且对不完美CSI鲁棒，可泛化至多用户场景。
中文摘要 AI 辅助
6G无线技术预计将采用更高和更宽的频段，利用高方向性波束成形。然而，巨大的带宽放大了波束斜视的影响。传统解决方案，如为每根天线添加真时延滤波器，由于所需的硬件规模而成本过高。本文提出了一种信号处理替代方案，使用图神经网络（GNNs）来优化多载波宽带系统中的混合波束成形。利用二分图表示多个子载波间共享的模拟波束成形器，我们开发了三种具有不同数字波束成形器表示的GNN结构：（i）在子载波节点上，（ii）在边上，或（iii）集成传统的奇异值分解解决方案。通过设计高效的消息传递机制，这些结构提供了不同GNN设计对通信系统性能和计算复杂度影响的见解。广泛的分析和消融研究表明，我们提出的GNN结构优于传统优化方法和现有的基于机器学习的解决方案。此外，所提出的GNN对波束斜视表现出强大的弹性，并且对不完美的CSI具有比甚至全数字波束成形和所有现有混合设计更好的鲁棒性。这些GNN还可以扩展到多用户场景，并展现出优秀的泛化能力，使得训练好的模型无需重新训练即可适应多样的多载波和多用户设置。
英文摘要
6G wireless technology is poised to adopt higher and wider frequency bands, leveraging highly directional beamforming. However, the vast bandwidths amplify the impact of beam squinting. Traditional solutions, such as adding a true-time-delay filter to each antenna, are cost-prohibitive due to the required hardware scale. This paper proposes a signal processing alternative using Graph Neural Networks (GNNs) to optimize hybrid beamforming in multicarrier wideband systems. Using a bipartite graph to represent a shared analog beamformer among multiple subcarriers, we develop three GNN structures with distinct digital beamformer representations (i) at the subcarrier nodes, (ii) at the edges, or (iii) integrating traditional singular-value decomposition solutions. By designing an efficient message-passing mechanism, these structures offer insights into the impact of different GNN designs on communication system performance and computational complexity. Extensive analysis and ablation studies show that our proposed GNN structures outperform traditional optimization methods and existing ML-based solutions. Furthermore, the proposed GNNs exhibit strong resiliency to beam squinting and better robustness against imperfect CSI than even fully digital beamforming and all existing hybrid designs. These GNNs can also be extended to multi-user scenarios and demonstrate excellent generalization capabilities, allowing trained models to adapt to diverse multicarrier and multi-user settings without retraining.
发表机构
- Tufts University（塔夫茨大学）
机构由 AI 辅助整理，请以论文原文为准。
