  Recently, deep learning methods have shown significant improvements in
communication systems. In this paper, we study the equalization problem over
the nonlinear channel using neural networks. The joint equalizer and decoder
based on neural networks are proposed to realize blind equalization and
decoding 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 the
transmitted signal from channel impairment and nonlinear distortions. Then the
deep neural network decoder (NND) decodes the detected signal from CNN
equalizer. Under various channel conditions, the experiment results demonstrate
that the proposed CNN equalizer achieves better performance than other
solutions based on machine learning methods. The proposed model reduces about
$2/3$ of the parameters compared to state-of-the-art counterparts. Besides, our
model can be easily applied to long sequence with $\mathcal{O}(n)$ complexity.
