# RNN-Conv-Decoder

**Repository Path**: zhoub86/RNN-Conv-Decoder

## Basic Information

- **Project Name**: RNN-Conv-Decoder
- **Description**: Accompanying code of paper "On Recurrent Neural Networks for Sequence-based Processing in Communications" by Daniel Tandler, Sebastian Dörner, Sebastian Cammerer, Stephan ten Brink.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-18
- **Last Updated**: 2021-03-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# On Recurrent Neural Networks for Sequence-based Processing in Communications
## In this notebook we show how to build a decoder for convolutional codes based on recurrent neural networks
Accompanying code of paper ["On Recurrent Neural Networks for Sequence-based Processing in Communications" by Daniel Tandler, Sebastian Dörner, Sebastian Cammerer, Stephan ten Brink](https://arxiv.org/abs/1905.09983)

If you find this code helpful please cite this work using the following bibtex entry:

```tex
@article{RNN-Conv-Decoding-Tandler2019,
  author    = {Daniel Tandler and
               Sebastian D{\"{o}}rner and
               Sebastian Cammerer and
               Stephan ten Brink},
  title     = {On Recurrent Neural Networks for Sequence-based Processing in Communications},
  journal   = {CoRR},
  year      = {2019},
  url       = {http://arxiv.org/abs/1905.09983},
}
```


## Installation/Setup

An example of the used code is given in the Jupyter Notebook (.ipynb file), the coding.py file is just for arbitrary code generation and not required to run the notebook.

You can directly run the notebook with code and short explanations in google colab:

[Run this Notebook in Google Colaboratory: Link to colab.google.com](https://colab.research.google.com/github/sdnr/RNN-Conv-Decoder/blob/master/RNN-based%20Decoder%20for%20Convolutional%20Codes.ipynb)