@inproceedings{56ebe356badf44c682d763402711d56e,
title = "Learning to decode linear codes using deep learning",
abstract = "A novel deep learning method for improving the belief propagation algorithm is proposed. The method generalizes the standard belief propagation algorithm by assigning weights to the edges of the Tanner graph. These edges are then trained using deep learning techniques. A well-known property of the belief propagation algorithm is the independence of the performance on the transmitted codeword. A crucial property of our new method is that our decoder preserved this property. Furthermore, this property allows us to learn only a single codeword instead of exponential number of codewords. Improvements over the belief propagation algorithm are demonstrated for various high density parity check codes.",
author = "Eliya Nachmani and Yair Be'Ery and David Burshtein",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016 ; Conference date: 27-09-2016 Through 30-09-2016",
year = "2017",
month = feb,
day = "10",
doi = "10.1109/ALLERTON.2016.7852251",
language = "English",
series = "54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "341--346",
booktitle = "54th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2016",
address = "United States",
}