Low-Complexity MIMO Receiver Based on Deep Learning

This paper proposes a low-complexity multiple-input multiple-output receiver based on deep learning. The proposed receiver defines a threshold utilizing the symbol error probabilities and the deep neural network decides the receiver type to detect the transmitted signal vector. Simulation results show that the proposed receiver can obtain almost the same symbol error rate performance as a conventional maximum likelihood receiver.