  Massive multiple-input multiple-output (MIMO) can improve the overall system
performance significantly. Massive MIMO systems, however, may require a large
number of radio frequency (RF) chains that could cause high cost and power
consumption issues. One of promising approaches to resolve these issues is
using low-resolution analog-to-digital converters (ADCs) at base stations.
Channel estimation becomes a difficult task by using low-resolution ADCs
though. This paper addresses the channel estimation problem for massive MIMO
systems using one-bit ADCs when the channels are spatially and temporally
correlated. Based on the Bussgang decomposition, which reformulates a
non-linear one-bit quantization to a statistically equivalent linear operator,
the Kalman filter is used to estimate the spatially and temporally correlated
channel by assuming the quantized noise follows a Gaussian distribution.
Numerical results show that the proposed technique can improve the channel
estimation quality significantly by properly exploiting the spatial and
temporal correlations of channels.
