  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.
