SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection

In this paper, we propose a multiple-input multiple-output receiver with deep learning. To obtain the high performance of the maximum likelihood receiver while maintaining the low computational complexity of the linear receiver, we apply a signal-to-noise ratio-based receiver-type decision method. Specifically, the criterion and threshold, which are based on the ratio between the symbol error probabilities of both receivers, to decide the receiver type are presented. For this, we analyze the signal-to-noise ratio gain of the zero-forcing receiver and exploit it as an input feature of the DNN. The simulation results demonstrate that the proposed receiver achieves a symbol error rate performance nearly identical to that of the maximum likelihood receiver with low computational complexity.