  In this paper, we propose to dynamically select a MIMO detector using neural
network for each resource element (RE) in the transport block of 5G NR/LTE
communication system. The objective is to minimize the computational complexity
of MIMO detection while keeping the transport block error rate (BLER) close to
the BLER when dimension-reduced maximum-likelihood (DR-ML) detection is used. A
detector selection problem is formulated to achieve this objective. However,
since the problem is high dimensional and NP-hard, we first decompose the
problem into smaller problems and train a multi-layer perceptron (MLP) network
to obtain the solution. The MLP network is trained to select a low-complexity,
yet reliable, detector using instantaneous channel condition in the RE. We
first propose a method to generate a labeled dataset to select a low-complexity
detector. Then, the MLP is trained twice using quasi-Newton method to select a
reliable detector for each RE. The performance of online detector selection is
evaluated in 5G NR link level simulator in terms of BLER and the complexity is
quantified in terms of the number of Euclidean distance (ED) computations and
the number of real additions and multiplication. Results show that the
computational complexity in the MIMO detector can be reduced by ~10X using the
proposed method.
