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Conference papers

Blind Neural Belief Propagation Decoder for Linear Block Codes

Abstract : Neural belief propagation decoders were recently introduced by Nachmani et al. as a way to improve the decoding performance of belief propagation iterative algorithm for short to medium length linear block codes. The main idea behind these decoders is to represent belief propagation as a neural network, enabling adaptive weighting of the decoding process. In the present paper an efficient recurrent neural network architecture, based on gating and weights sharing mechanisms, is proposed to perform blind neural belief propagation decoding without prior knowledge of the coding scheme used by the encoder. The proposed architecture is able to learn to decode BCH (15,11) and BCH (15,7) codes and significantly improves the decoding performance over a standard belief propagation algorithm. A particular emphasis is given to the interpretability and complexity of the proposed model to ensure scalability to larger codes.
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Contributor : Hadi Ghauch Connect in order to contact the contributor
Submitted on : Thursday, July 1, 2021 - 2:20:16 PM
Last modification on : Tuesday, October 19, 2021 - 11:15:03 AM
Long-term archiving on: : Saturday, October 2, 2021 - 6:53:24 PM


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  • HAL Id : hal-03275838, version 1


Guillaume Larue, Louis-Adrien Dufrene, Quentin Lampin, Paul Chollet, Hadi Ghauch, et al.. Blind Neural Belief Propagation Decoder for Linear Block Codes. EnCNC 2021, Jun 2021, porto, Portugal. ⟨hal-03275838⟩



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