Name
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 iterat... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JSTSP.2017.2788405 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | DocType | Volume |
Decoding,Signal processing algorithms,Neural networks,Belief propagation,Parity check codes,Machine learning,Standards | Journal | 12 |
Issue | ISSN | Citations |
1 | 1932-4553 | 50 |
PageRank | References | Authors |
2.25 | 24 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eliya Nachmani | 1 | 132 | 12.32 |
| Elad Marciano | 2 | 50 | 2.25 |
| Loren Lugosch | 3 | 50 | 2.25 |
| Warren J. Gross | 4 | 1106 | 113.38 |
| David Burshtein | 5 | 198 | 17.93 |
| Y. Be'ery | 6 | 284 | 37.76 |