English(EN) Deep Learning-Based Tri-Hybrid Multi-User MIMO Precoding: The Blessing of EM-Reconfigurable Antennas

深度学习框架优化三混合MIMO预编码，提升无线效率

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-08 04:00

研究人员开发了一个名为Tri-PNet的新型深度学习框架，用于优化无线通信系统中的多用户MIMO预编码。该框架将电磁（EM）可重构天线与传统的混合模拟数字预编码相结合，创建了一个“三混合”系统，显著提高了频谱效率。Tri-PNet利用了结合了卷积神经网络和Transformer的Conformer架构，以联合学习电磁、模拟和数字预编码策略。与现有方法相比，该系统表现出优越的性能，以显著降低的计算复杂度逼近最优解，并在信道信息不完美的情况下仍保持鲁棒性。 AI

影响这项研究通过先进的深度学习技术优化信号传输，有望带来更高效的无线通信系统。

排序理由详细介绍用于信号处理的新深度学习模型的学术论文。[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) · Kaijun Feng, Jiaxin He, Hongrui Yu, Zhen Gao, Anwen Liao, Ziwei Wan, Zhaocheng Wang · 2026-10-08 04:00

基于深度学习的三混合多用户MIMO预编码：电磁可重构天线的福音

arXiv:2609.39167v1 Announce Type: cross Abstract: Electromagnetic (EM)-reconfigurable antennas provide multiple candidate radiation patterns per element, thereby introducing an additional EM-domain degree of freedom. Integrating radiation-pattern reconfigurability, realized as EM…

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基于深度学习的三混合多用户MIMO预编码：电磁可重构天线的福音

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