  In this paper, we consider the use of deep neural networks in the context of
Multiple-Input-Multiple-Output (MIMO) detection. We give a brief introduction
to deep learning and propose a modern neural network architecture suitable for
this detection task. First, we consider the case in which the MIMO channel is
constant, and we learn a detector for a specific system. Next, we consider the
harder case in which the parameters are known yet changing and a single
detector must be learned for all multiple varying channels. We demonstrate the
performance of our deep MIMO detector using numerical simulations in comparison
to competing methods including approximate message passing and semidefinite
relaxation. The results show that deep networks can achieve state of the art
accuracy with significantly lower complexity while providing robustness against
ill conditioned channels and mis-specified noise variance.
