English(EN) Efficient Graph Neural Networks for Multicarrier Wideband Hybrid Beamforming Optimization

图神经网络优化6G波束成形

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

研究人员开发了新颖的图神经网络（GNN）来优化6G无线系统的混合波束成形，解决了宽带频率下的波束倾斜等挑战。所提出的GNN结构在二分图模型中以各种方式表示，与传统和现有的机器学习方法相比，提供了更高的性能和更低的计算复杂度。这些GNN在不完美的信道状态信息下表现出鲁棒性，并且无需重新训练即可泛化到多用户场景。 AI

影响这项研究可能通过利用先进的人工智能技术进行信号处理，从而实现更高效、更鲁棒的无线通信系统。

排序理由该集群包含一篇详细介绍无线通信优化新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Beier Li, Mai Vu · 2026-10-02 04:00

面向多载波宽带混合波束成形优化的高效图神经网络

arXiv:2609.09708v2 Announce Type: replace-cross Abstract: 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 addin…

本文如何被排名

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面向多载波宽带混合波束成形优化的高效图神经网络

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