  Expectation Propagation (EP)-based Multiple-Input Multiple-Output (MIMO)
detector is regarded as a state-of-the-art MIMO detector because of its
exceptional performance. However, we find that the EP MIMO detector cannot
guarantee to achieve the optimal performance due to the empirical parameter
selection, including initial variance and damping factors. According to the
influence of the moment matching and parameter selection for the performance of
the EP MIMO detector, we propose a modified EP MIMO detector (MEPD). In order
to obtain the optimal initial variance and damping factors, we adopt a deep
learning scheme, in which we unfold the iterative processing of MEPD to
establish MEPNet for parameters training. The simulation results show that MEPD
with off-line trained parameters outperforms the original one in various MIMO
scenarios. Besides, the proposed MEPD with deep learning parameters selection
is more robust than EPD in practical scenarios.
