Channel estimation in quantized systems is challenging, particularly in
low-resolution systems. In this work, we propose to leverage a Gaussian mixture
model (GMM) as generative prior, capturing the channel distribution of the
propagation environment, to enhance a classical estimation technique based on
the expectation-maximization (EM) algorithm for one-bit quantization. Thereby,
a maximum a posteriori (MAP) estimate of the most responsible mixture component
is inferred for a quantized received signal, which is subsequently utilized in
the EM algorithm as side information. Numerical results demonstrate the
significant performance improvement of our proposed approach over both a
simplistic Gaussian prior and current state-of-the-art channel estimators.
Furthermore, the proposed estimation framework exhibits adaptability to higher
resolution systems and alternative generative priors.

通过利用高斯混合模型作为一位量化信号传导环境的通道分布的生成先验，我们提出了一种改进基于期望最大化算法的经典估计技术的方法，借此提高量化系统中通道估计的性能表现。数值结果表明，我们提出的方法相较于简单的高斯先验和当前最先进的通道估计器，具有显著的性能改进。此外，所提出的估计框架还能够适应更高分辨率系统和其他生成先验。