Neural Network-based Equalizer by Utilizing Coding Gain in Advance
This work addresses performance degradation in communication systems for users by improving coding gain, though it appears incremental as it builds on existing deep learning methods.
The paper tackles the problem of channel equalization in communication systems by proposing neural network-based equalizers that preserve code structure, resulting in over 1.5 dB gain in coding gain utilization.
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.