  As the array dimension of massive MIMO systems increases to unprecedented
levels, two problems occur. First, the spatial stationarity assumption along
the antenna elements is no longer valid. Second, the large array size results
in an unacceptably high power consumption if high-resolution analog-to-digital
converters are used. To address these two challenges, we consider a Bussgang
linear minimum mean square error (BLMMSE)-based channel estimator for large
scale massive MIMO systems with one-bit quantizers and a spatially
non-stationary channel. Whereas other works usually assume that the channel
covariance is known at the base station, we consider a plug-in BLMMSE estimator
that uses an estimate of the channel covariance and rigorously analyze the
distortion produced by using an estimated, rather than the true, covariance. To
cope with the spatial non-stationarity, we introduce dithering into the
quantized signals and provide a theoretical error analysis. In addition, we
propose an angular domain fitting procedure which is based on solving an
instance of non-negative least squares. For the multi-user data transmission
phase, we further propose a BLMMSE-based receiver to handle one-bit quantized
data signals. Our numerical results show that the performance of the proposed
BLMMSE channel estimator is very close to the oracle-aided scheme with ideal
knowledge of the channel covariance matrix. The BLMMSE receiver outperforms the
conventional maximum-ratio-combining and zero-forcing receivers in terms of the
resulting ergodic sum rate.
