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：本研究利用变分自编码器进行信道估计，并在真实世界测量数据上对其进行评估。该估计器仅基于含噪信道观测进行训练，通过学习观测相关的条件一阶和二阶矩，参数化对均方误差最优估计器的近似。在真实世界测量中，所提估计器显著优于相关最先进的估计器。我们探究了使用合成数据预训练的影响，发现若在合成数据上训练并在测量数据上评估，所提估计器与相关估计器性能相当。此外，合成数据预训练还有助于减少所需的测量训练数据集规模。