English(EN) Achieving Robust Channel Estimation Neural Networks on Doppler effects by Designed Training Data

新方法增强了无线信道估计神经网络的鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

研究人员开发了一种方法来提高用于无线通信系统信道估计的神经网络的鲁棒性。所提出的方法侧重于生成具有特定设计标准的合成训练数据，以确保这些网络能够有效地泛化到多样化和时变信道，特别是那些具有高多普勒频移的信道。这种离线训练方法旨在消除对先验信道信息或在线训练的需求，从而即使在以前未知的无线环境中也能进行精确的信道估计。 AI

影响提高了关键无线通信任务中AI模型的可靠性，可能支持更鲁棒和高效的系统。

排序理由学术论文，详细介绍了改进信号处理中神经网络泛化能力的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Dianxin Luan, John Thompson · 2026-11-11 05:00

通过设计训练数据实现多普勒效应下的鲁棒信道估计神经网络

arXiv:2611.14267v1 Announce Type: cross Abstract: Channel estimation is crucial in wireless communication systems. Many existing works train and test their models using the same or similar channel. However, practical channels are infinitely diverse and time-variant while data-dri…

本文如何被排名

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通过设计训练数据实现多普勒效应下的鲁棒信道估计神经网络

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