  The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical
solution for reducing cost and power consumption for massive
Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity
of low-resolution ADCs causes significant distortions in the received signals
and makes the channel estimation and data detection tasks much more
challenging. In this paper, we show how \textit{Support Vector Machine}
(\textit{SVM}), a well-known supervised-learning technique in machine learning,
can be exploited to provide efficient and robust channel estimation and data
detection in massive MIMO systems with one-bit ADCs. First, the problem of
channel estimation for uncorrelated channels is formulated as a conventional
SVM problem. The objective function of this SVM problem is then modified for
estimating spatially correlated channels. Next, a two-stage detection algorithm
is proposed where SVM is further exploited in the first stage. The performance
of the proposed data detection method is very close to that of
Maximum-Likelihood (ML) data detection when the channel is perfectly known. We
also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD)
method, which makes use of both the to-be-decoded data vectors and the pilot
data vectors to improve the estimation and detection performance. Finally, an
extension of the proposed methods to OFDM systems with frequency-selective
fading channels is presented. Simulation results show that the proposed methods
are efficient and robust, and also outperform existing ones.
