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Published July 28, 2021 | Version v1
Conference paper Open

Blind Neural Belief Propagation Decoder for Linear Block Codes

  • 1. Orange Labs & Institut Polytechnique de Paris
  • 2. Orange Labs
  • 3. Institut Polytechnique de Paris

Description

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 at least at the level of performance of a standard belief propagation algorithm and even to outperform it in the case of BCH (15,11) code thanks to NBP approach. A particular emphasis is given to the interpretability and complexity of the proposed model to ensure scalability to larger codes.

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Additional details

Funding

Hexa-X – A flagship for B5G/6G vision and intelligent fabric of technology enablers connecting human, physical, and digital worlds 101015956
European Commission