  In this paper, an efficient massive multiple-input multiple-output (MIMO)
detector is proposed by employing a deep neural network (DNN). Specifically, we
first unfold an existing iterative detection algorithm into the DNN structure,
such that the detection task can be implemented by deep learning (DL) approach.
We then introduce two auxiliary parameters at each layer to better cancel
multiuser interference (MUI). The first parameter is to generate the residual
error vector while the second one is to adjust the relationship among previous
layers. We further design the training procedure to optimize the auxiliary
parameters with pre-processed inputs. The so derived MIMO detector falls into
the category of model-driven DL. The simulation results show that the proposed
MIMO detector can achieve preferable detection performance compared to the
existing detectors for massive MIMO systems.
