Neural Decoding with Optimization of Node Activations
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Abstract (arXiv)
The problem of maximum likelihood decoding with a neural decoder for error-correcting code is considered. It is shown that the neural decoder can be improved with two novel loss terms on the node's activations. The first loss term imposes a sparse constraint on the node's activations. Whereas, the second loss term tried to mimic the node's activations from a teacher decoder which has better performance. The proposed method has the same run time complexity and model size as the neural Belief Propagation decoder, while improving the decoding performance by up to 1.1dB on BCH codes.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 14 of its 31 references that are in the map. First posted on arXiv 1 Jun 2022.
Builds on
14 of 31 references.
References · 14
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- 2016Learning to Decode Linear Codes Using Deep Learningcited by 61
- 2022Error Correction Code Transformercited by 47
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2 more
- 2020Ensemble Wrapper Subsampling for Deep Modulation Classificationcited by 4
- 2018Learning from the Syndromecited by 3
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 1.
4 papers.
- 2023Boosting Learning for LDPC Codes to Improve the Error-Floor Performancecited by 10
- 2023Generalization Bounds for Neural Belief Propagation Decoderscited by 4
- 2024Boosted Neural Decoders: Achieving Extreme Reliability of LDPC Codes for 6G Networkscited by 3
- 2022A Scalable Graph Neural Network Decoder for Short Block Codescited by 0
- 2024Boosted Neural Decoders: Achieving Extreme Reliability of LDPC Codes for 6G Networks22 May 2024
- 2023Boosting Learning for LDPC Codes to Improve the Error-Floor Performance11 Oct 2023
- 2023Generalization Bounds for Neural Belief Propagation Decoders17 May 2023
- 2022A Scalable Graph Neural Network Decoder for Short Block Codes13 Nov 2022