This work utilizes a variational autoencoder for channel estimation and evaluates it on real-world measurements. The estimator is trained solely on noisy channel observations and parameterizes an approximation to the mean squared error-optimal estimator by learning observation-dependent conditional first and second moments. The proposed estimator significantly outperforms related state-of-the-art estimators on real-world measurements. We investigate the effect of pre-training with synthetic data and find that the proposed estimator exhibits comparable results to the related estimators if trained on synthetic data and evaluated on the measurement data. Furthermore, pre-training on synthetic data also helps to reduce the required measurement training dataset size.

翻译：本研究运用变分自编码器进行信道估计，并在实测数据上开展评估。该估计器仅基于含噪信道观测进行训练，通过学习依赖观测数据的条件一阶矩与二阶矩，参数化出逼近均方误差最优估计器的近似形式。在实测数据上，所提估计器显著优于当前最优的相关估计器。我们探究了基于合成数据预训练的效果，发现若在合成数据上训练并在实测数据上评估，所提估计器与相关估计器表现相当。此外，合成数据预训练还有助于减少所需实测训练数据集的规模。