  In this paper, we propose a learning-based detection framework for uplink
massive multiple-input and multiple-output (MIMO) systems with one-bit
analog-to-digital converters. The learning-based detection only requires
counting the occurrences of the quantized outputs of -1 and +1 for estimating a
likelihood probability at each antenna. Accordingly, the key advantage of this
approach is to perform maximum likelihood detection without explicit channel
estimation which has been one of the primary challenges of one-bit quantized
systems. However, due to the quasi-deterministic reception in the high
signal-to-noise ratio (SNR) regime, one-bit observations in the high SNR regime
are biased to either +1 or -1, and thus, the learning requires excessive
training to estimate the small likelihood probabilities. To address this
drawback, we propose a dither-and-learning technique to estimate likelihood
functions from dithered signals. First, we add a dithering signal to
artificially decrease the SNR and then infer the likelihood function from the
quantized dithered signals by using an SNR estimate derived from a deep neural
network-based estimator which is trained offline. We extend our technique by
developing an adaptive dither-and-learning method that updates the dithering
power according to the patterns observed in the quantized dithered signals. The
proposed framework is also applied to channel-coded MIMO systems by computing a
bit-wise and user-wise log-likelihood ratio from the refined likelihood
probabilities. Simulation results validate the performance of the proposed
methods in both uncoded and coded systems.
