  In this paper, we propose a model-driven deep learning network for
multiple-input multiple-output (MIMO) detection. The structure of the network
is specially designed by unfolding the iterative algorithm. Some trainable
parameters are optimized through deep learning techniques to improve the
detection performance. Since the number of trainable variables of the network
is equal to that of the layers, the network can be easily trained within a very
short time. Furthermore, the network can handle time-varying channel with only
a single training. Numerical results show that the proposed approach can
improve the performance of the iterative algorithm significantly under Rayleigh
and correlated MIMO channels.
