  As future wireless systems trend towards higher carrier frequencies and large
antenna arrays, receivers with one-bit analog-to-digital converters (ADCs) are
being explored owing to their reduced power consumption. However, the
combination of large antenna arrays and one-bit ADCs makes channel estimation
challenging. In this paper, we formulate channel estimation from a limited
number of one-bit quantized pilot measurements as an inverse problem and
reconstruct the channel by optimizing the input vector of a pre-trained deep
generative model with the objective of maximizing a novel correlation-based
loss function. We observe that deep generative priors adapted to the underlying
channel model significantly outperform Bernoulli-Gaussian Approximate Message
Passing (BG-GAMP), while a single generative model that uses a conditional
input to distinguish between Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS)
channel realizations outperforms BG-GAMP on LOS channels and achieves
comparable performance on NLOS channels in terms of the normalized channel
reconstruction error.
