MIMO Detector Selection for Multiple High-Order Modulations with Unified Neural Network

We propose a unified multi-layer perceptron (MLP) network to select an appropriate multiple-input, multiple-output (MIMO) detector for high-order quadrature amplitude modulations (QAMs) such as 64-, 256-, and 1024-QAM. The network is trained to select a low-complexity detector dynamically for 64-, 256-, and 1024-QAM from a set of candidate detectors, while simultaneously maintaining a block error rate (BLER) close to the highest complexity candidate detector. We train the network on a combined data set collected under various environments such as multiple modulation orders, channel profiles, and signal-to-noise ratios. For offline training, a selective back-propagation method is proposed wherein samples of specific $M$-QAM are used to update modulation specific weights between the hidden layer and the output layer of the MLP network. The common weights between the input layer and the hidden layer are updated using all the samples in the data set. Thus, a single unified network can be deployed for multiple modulation orders together. The performance is evaluated with different channel profiles including channels for which the network was not trained. Simulation results show that the proposed algorithm maintains the BLER close to that of the most complex candidate detector even under un-trained channel profiles. The proposed algorithm also reduces the computational complexity of the MIMO detection block up to 10×.