  In a K-best detector for multiple-input-multiple-output(MIMO) systems, the
value of K needs to be sufficiently large to achieve near-maximum-likelihood
(ML) performance. By treating K as a variable that can be adjusted according to
a fitting function of some learnable coefficients, an intelligent MIMO
detection network based on deep neural networks (DNN) is proposed to reduce
complexity of the detection algorithm with little performance degradation. In
particular, the proposed intelligent detection algorithm uses meta learning to
learn the coefficients of the fitting function for K to circumvent the problem
of learning K directly. The idea of network fusion is used to combine the
learning results of the meta learning component networks. Simulation results
show that the proposed scheme achieves near-ML detection performance while its
complexity is close to that of linear detectors. Besides, it also exhibits
strong ability of fast training.
