  Recently, deep learning has been exploited in many fields with revolutionary
breakthroughs. In the light of this, deep learning-assisted communication
systems have also attracted much attention in recent years and have potential
to break down the conventional design rule for communication systems. In this
work, we propose two kinds of neural network-based equalizers to exploit
different characteristics between convolutional neural networks and recurrent
neural networks. The equalizer in conventional block-based design may destroy
the code structure and degrade the capacity of coding gain for decoder. On the
contrary, our proposed approach not only eliminates channel fading, but also
exploits the code structure with utilization of coding gain in advance, which
can effectively increase the overall utilization of coding gain with more than
1.5 dB gain.
