High-performance Decoder for Convolutional Code with Deep Neural Network

TL;DR

This paper proposes a deep neural network-based decoder for convolutional codes that improves decoding performance and is suitable for high-bit-rate applications by leveraging window decoding and an integrated decoding approach.

Contribution

It introduces a novel neural network decoder for long convolutional codes using window decoding and an integrated approach, enhancing performance over traditional methods.

Findings

Viterbi decoder improved by approximately 2 dB at a bit error rate of 5

Neural network accuracy can reach level 8 or higher

Decoders can operate in parallel for high-bit-rate applications

Abstract

The use of deep neural network for decoding error control code will encounter two problems, namely, the high-precision requirements of the error control code and the complexity of the neural network due to the long code. In this paper, a deep neural network decoder is proposed to solve the decoding problem of long code by using the nature of convolutional code window decoding. A deep neural network decoder is utilized as a weak classifier, and an integrated decoder is proposed to improve the decoding performance greatly. The Viterbi decoder is improved by approximately 2 db at a bit error rate of level 5. Both decoder methods proposed in this paper can be decoded in parallel and are suitable for high-bit-rate applications. This study reveals that the accuracy of neural networks can reach level 8 more.