We propose to utilize a variational autoencoder (VAE) for data-driven channel estimation. The underlying true and unknown channel distribution is modeled by the VAE as a conditional Gaussian distribution in a novel way, parameterized by the respective first and second order conditional moments. As a result, it can be observed that the linear minimum mean square error (LMMSE) estimator in its variant conditioned on the latent sample of the VAE approximates an optimal MSE estimator. Furthermore, we argue how a VAE-based channel estimator can approximate the MMSE channel estimator. We propose three variants of VAE estimators that differ in the data used during training and estimation. First, we show that given perfectly known channel state information at the input of the VAE during estimation, which is impractical, we obtain an estimator that can serve as a benchmark result for an estimation scenario. We then propose practically feasible approaches, where perfectly known channel state information is only necessary in the training phase or is not needed at all. Simulation results on 3GPP and QuaDRiGa channel data attest a small performance loss of the practical approaches and the superiority of our VAE approaches in comparison to other related channel estimation methods.

翻译：我们建议使用一个变式自动读数仪(VAE)来进行数据驱动的频道估计。VAE将基础真实和未知的频道分布模拟成一个有条件的Gaussian分布新颖模式,以各自的第一和第二顺序有条件的时段为参数。因此,我们可以看到,以VAE潜质样本为条件的线性最低平均平方差(LMMSE)变式估计器(LMMSE)在变量中显示,以VAE潜在样本为条件的线性最低平均平方差(LMMSE)估计器(VAE)接近一个最佳 MSE 估计仪。此外,我们提出VAE 频道估计仪如何能接近MMSE 频道估计器。我们提出了三种不同的VAE 估计器在培训和估计期间使用的数据中不同,我们提出了三种不同的变式。首先,我们表明,鉴于在估算期间对VAE 最低均分数计算法输入的频道信息是十分不切实际的,我们得到了一个可用作估计假设情景的基准结果。我们然后提出切实可行的方法,在培训阶段只需要或根本不需要频道资料,或根本不需要。在3GPPPP和VADRA系统相关方法中模拟模拟了我们的数据压低级评估方法的模拟结果。