  We consider a multihop distributed uplink reception system in which $K$ users
transmit independent messages to one data center of $N_{\rm r} \geq K$ receive
antennas, with the aid of multihop intermediate relays. In particular, each
antenna of the data center is equipped with one-bit analog-to-digital converts
(ADCs) for the sake of power-efficiency. In this system, it is extremely
challenging to develop a low-complexity detector due to the non-linearity of an
end-to-end channel transfer function (created by relays' operations and one-bit
ADCs). Furthermore, there is no efficient way to estimate such complex function
with a limited number of training data. Motivated by this, we propose a
supervised-learning (SL) detector by introducing a novel Bernoulli-like model
in which training data is directly used to design a detector rather than
estimating a channel transfer function. It is shown that the proposed SL
detector outperforms the existing SL detectors based on Gaussian model for
one-bit quantized (binary observation) systems. Furthermore, we significantly
reduce the complexity of the proposed SL detector using the fast kNN algorithm.
Simulation results demonstrate that the proposed SL detector can yield an
attractive performance with a significantly lower complexity.
