# RNN Decoding of Linear Block Codes

arXiv: https://arxiv.org/abs/1702.07560 · HTML: https://mlsanity.com/paper/1702.07560/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2017.

## Built on by (most cited first, 3 of 3)

- [Channel model for end-to-end learning of communications systems: A survey](https://mlsanity.com/paper/2204.03944/) (2022)
- [A recipe of training neural network-based LDPC decoders](https://mlsanity.com/paper/2205.00481/) (2022)
- [Neural Window Decoder for SC-LDPC Codes](https://mlsanity.com/paper/2411.19092/) (2024)

## Abstract (arXiv)

Designing a practical, low complexity, close to optimal, channel decoder for powerful algebraic codes with short to moderate block length is an open research problem. Recently it has been shown that a feed-forward neural network architecture can improve on standard belief propagation decoding, despite the large example space. In this paper we introduce a recurrent neural network architecture for decoding linear block codes. Our method shows comparable bit error rate results compared to the feed-forward neural network with significantly less parameters. We also demonstrate improved performance over belief propagation on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the RNN decoder can be used to improve the performance or alternatively reduce the computational complexity of the mRRD algorithm for low complexity, close to optimal, decoding of short BCH codes.