Abstract

Multiple-input multiple-output (MIMO) detectors have been a key technology in communication systems. In this paper, a new MIMO detector is designed by combining the adaptive learning rate (ALR) with the convolutional neural network (CNN) and successfully implementing it in a mode division multiplexing (MDM) optical transmission system. The results show that the training and test accuracy of the signal in the system we proposed reaches 100%. What is more, we used the ALR-CNN to compare the performance with conventional detection algorithms. The results confirm that our DLNN exceeds the conventional MIMO detectors in performance and is able to achieve the ideal QPSK BER level. The minimum difference in the SNR is about 9.5 dB at a BER of the ${{10}^{- 3}}$ order.

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