带岭回归去噪的注意力辅助最小均方误差：如何在噪声信道样本下进行训练
Attention-Aided MMSE with Ridge Denoising: How to Train under Noisy Channel Samples
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中文总结 AI 辅助
针对噪声信道样本下A-MMSE训练的结构偏差问题，提出岭正则化目标与基于特征值裁剪的协方差估计方法，在COST 2100信道上Ridge-A-MMSE性能优于N2N基线，高SNR下接近干净CSI训练的网络。
中文摘要 AI 辅助
深度神经网络信道估计器通常使用在实际正交频分复用（OFDM）系统中不可用的干净信道状态信息（CSI）进行训练。在基于导频的OFDM中，朴素的噪声目标训练存在结构偏差，因为导频输入和噪声全网格目标共享相同的噪声实现，会驱使估计器趋向于恒等复制。为解决注意力辅助最小均方误差（A-MMSE）的这一问题，我们提出一种岭正则化目标，直接惩罚生成的滤波器。在简化的固定滤波器模型中，该惩罚会诱导标量收缩，并在明确的惩罚值处恢复标量MMSE增益。我们还通过对噪声经验二阶矩矩阵进行特征值裁剪来估计信道协方差，从而构造替代训练目标，无需干净CSI标签。在COST 2100信道上，所提出的Ridge-A-MMSE始终优于本文考虑的Noise2Noise（N2N）基线，且岭正则化与协方差收缩的组合在高信噪比（SNR）下接近使用干净CSI标签训练的同一网络。
英文摘要
Deep neural channel estimators are typically trained with clean channel state information (CSI), which is unavailable in practical orthogonal frequency-division multiplexing (OFDM) systems. In pilot-based OFDM, naive noisy-target training is structurally biased because the pilot input and noisy full-grid target share the same noise realization, driving the estimator toward identity copying. To address this for Attention-aided MMSE (A-MMSE), we propose a ridge-regularized objective that penalizes the generated filter directly. In a stylized fixed-filter model, this penalty induces scalar shrinkage and recovers the scalar MMSE gain at an explicit penalty value. We further construct surrogate training targets by estimating the channel covariance via eigenvalue clipping of the noisy empirical second-moment matrix, without requiring clean CSI labels. On COST 2100 channels, the proposed Ridge-A-MMSE consistently outperforms the Noise2Noise (N2N) baselines considered in this paper, and the combination of ridge regularization and covariance shrinkage approaches the same network trained with clean CSI labels at high signal-to-noise ratios (SNRs).
