mlsanityData to 14 Oct 2026

Neural Decoding with Optimization of Node Activations

arXiv 2206.00786 ↗First posted 1 Jun 2022cs.IT cs.AI cs.LGcited by 4

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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

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2018Learning to Detectcited by 63
  3. 2016Learning to Decode Linear Codes Using Deep Learningcited by 61
  4. 2022Error Correction Code Transformercited by 47
  5. 2018Deepcode: Feedback Codes via Deep Learningcited by 42
  6. 2019Hyper-Graph-Network Decoders for Block Codescited by 42
  7. 2017Neural Offset Min-Sum Decodingcited by 24
  8. 2019Fast Deep Learning for Automatic Modulation Classificationcited by 21
  9. 2021Cyclically Equivariant Neural Decoders for Cyclic Codescited by 12
  10. 2019Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoderscited by 11
  11. 2020Pruning Neural Belief Propagation Decoderscited by 10
  12. 2019Active Deep Decoding of Linear Codescited by 8
2 more
  1. 2020Ensemble Wrapper Subsampling for Deep Modulation Classificationcited by 4
  2. 2018Learning from the Syndromecited by 3

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Citing papers per year: 2022: 1; 2023: 2; 2024: 1.

4 papers.