# Deep Learning Methods for Improved Decoding of Linear Codes

## TL;DR

This paper explores how deep learning techniques can enhance the decoding of linear codes, improving performance and reducing complexity of traditional algorithms like belief propagation and min-sum.

## Contribution

It introduces neural network-based modifications to standard decoders, including recurrent architectures and parameter tying, achieving better decoding accuracy with fewer parameters.

## Key findings

- Deep learning improves belief propagation decoding performance.
- Recurrent neural decoders match results with fewer parameters.
- Neural decoders enhance BCH code decoding efficiency.

## Abstract

The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.