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About Learning To Decode Linear Codes Using Deep Learning: July 2016
This document summarizes a research paper that proposes using deep learning techniques to improve belief propagation decoding of linear codes. Specifically, it trains weights assigned to the edges of a Tanner graph that represents a linear code. This allows the decoder to generalize belief propagation while preserving the key property of independence from the transmitted codeword. This means only a single codeword needs to be used to train the decoder parameters, overcoming limitations of prior approaches. Results show improvements over belief propagation decoding for several high density parity check codes.
Detailed Information
| Author: | ['Laxman Jagarlamudi'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 7 |
| Language: | English |
| Format: | |
| Price: | FREE |
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