Deep Learning Methods for Channel Decoding A Brief Tutorial

Abstract

The recent development of deep learning methods demonstrates a new insight to optimize the decoding of linear codes. In this paper, we survey the typical neural network decoding methods, including data-driven and model-driven schemes. We investigate the design principle, algorithm mechanism, parameter assignment, and training process of these neural decoders for high-density parity check (HDPC), low-density paritycheck (LDPC), and polar codes. Finally, we summarize the advantages of neural network decoding and point out some research directions in the future.

Kai Niu

Professor

Jincheng Dai

Supervisor

Kailin Tan

Ph.D Student

My research include semantic communications, source and channel cod- ing, and machine learning.