  Massive multiple-input multiple-output (MIMO) communications using
low-resolution analog-to-digital converters (ADCs) is a promising technology
for providing high spectral and energy efficiency with affordable hardware cost
and power consumption. However, the use of low-resolution ADCs requires special
signal processing methods for channel estimation and data detection since the
resulting system is severely non-linear. This paper proposes joint channel
estimation and data detection methods for massive MIMO systems with
low-resolution ADCs based on the variational Bayes (VB) inference framework. We
first derive matched-filter quantized VB (MF-QVB) and linear minimum
mean-squared error quantized VB (LMMSE-QVB) detection methods assuming the
channel state information (CSI) is available. Then we extend these methods to
the joint channel estimation and data detection (JED) problem and propose two
methods we refer to as MF-QVB-JED and LMMSE-QVB-JED. Unlike conventional
VB-based detection methods that assume knowledge of the second-order statistics
of the additive noise, we propose to float the noise variance/covariance matrix
as an unknown random variable that is used to account for both the noise and
the residual inter-user interference. We also present practical aspects of the
QVB framework to improve its implementation stability. Finally, we show via
numerical results that the proposed VB-based methods provide robust performance
and also significantly outperform existing methods.
