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Recently, deep learning methods have shown significant improvements incommunication systems. In this paper, we study the equalization problem overthe nonlinear channel using neural networks. The joint equalizer and decoderbased on neural networks are proposed to realize blind equalization anddecoding process without the knowledge of channel state information (CSI).Different from previous methods, we use two neural networks instead of one.First, convolutional neural network (CNN) is used to adaptively recover thetransmitted signal from channel impairment and nonlinear distortions. Then thedeep neural network decoder (NND) decodes the detected signal from CNNequalizer. Under various channel conditions, the experiment results demonstratethat the proposed CNN equalizer achieves better performance than othersolutions based on machine learning methods. The proposed model reduces about$2/3$ of the parameters compared to state-of-the-art counterparts. Besides, ourmodel can be easily applied to long sequence with $\mathcal{O}(n)$ complexity.

Joint Neural Network Equalizer and Decoder