  In this paper, we investigate learning-based maximum likelihood (ML)
detection for uplink massive multiple-input and multiple-output (MIMO) systems
with one-bit analog-to-digital converters (ADCs). To overcome the significant
dependency of learning-based detection on the training length, we propose two
one-bit ML detection methods: a biased-learning method and a
dithering-and-learning method. The biased-learning method keeps likelihood
functions with zero probability from wiping out the obtained information
through learning, thereby providing more robust detection performance.
Extending the biased method to a system with knowledge of the received
signal-to-noise ratio, the dithering-and-learning method estimates more
likelihood functions by adding dithering noise to the quantizer input. The
proposed methods are further improved by adopting the post likelihood function
update, which exploits correctly decoded data symbols as training pilot
symbols. The proposed methods avoid the need for channel estimation. Simulation
results validate the detection performance of the proposed methods in symbol
error rate.
