Symbol detection based on temporal convolutional network in optical communications

Abstract

Deep learning (DL) is one of the fastest developing areas in artificial intelligence, it has been recently gained studies and application in computer vision, automatic driving, automatic speech recognition, and communication. This paper uses the DL method to design a symbol detection algorithm in receiver for optical communication systems. The proposed DL based method is implemented by a non-causal temporal convolutional network (ncTCN), which is a convolutional neural network and appropriate for sequence processing. Meanwhile, we adopt three methods to realize the training process for multiple signal-to-noise ratios of the AWGN channel. Furthermore, we apply two nonlinear activation functions for the noise robustness to the proposed ncTCN. Without losing generality, we apply the ncTCN-based receiver to the 16-ary quadrature amplitude modulation optical communication system in the simulation experiment. According to the experiment results, the proposed method can obtain some bit error rate performance gain compared to some conventional receivers.