  This paper considers a multiple-input-multiple-output (MIMO) system with
low-resolution analog-to-digital converters (ADCs). In this system, we propose
a novel communication framework that is inspired by supervised learning. The
key idea of the proposed framework is to learn the non-linear input-output
system, formed by the concatenation of a wireless channel and a quantization
function used at the ADCs, for data detection. In this framework, a
conventional channel estimation process is replaced by a system learning
process, in which the conditional probability mass functions (PMFs) of the
nonlinear system are empirically learned by sending the repetitions of all
possible data signals as pilot signals. Then the subsequent data detection
process is performed based on the empirical conditional PMFs obtained during
the system learning. To reduce both the training overhead and the detection
complexity, we also develop a supervised-learning-aided
successive-interference-cancellation method. In this method, a data signal
vector is divided into two subvectors with reduced dimensions. Then these two
subvectors are successively detected based on the conditional PMFs that are
learned using artificial noise signals and an estimated channel. For the case
of one-bit ADCs, we derive an analytical expression for vector-error-rate of
the proposed framework under perfect channel knowledge at the receiver.
Simulations demonstrate the detection error reduction of the proposed framework
compared to conventional detection techniques that are based on channel
estimation.
