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Doing More With Less: Towards More Data-Efficient Syndrome-Based Neural Decoders

Ahmad Ismail, Raphaël Le Bidan, Elsa Dupraz, Charbel Abdel-Nour

TL;DR

This work shows that data quality and training data design are critical for syndrome-based neural decoders (SBND) to approach maximum-likelihood decoding (MLD) on short codes. By using fixed, well-constructed datasets and targeting MLD-like error patterns rather than true channel errors, SBNDs such as GRU-based and ECCT models achieve near-MLD performance with far fewer training samples. The authors introduce several dataset-design heuristics, including optimizing the training distribution and balancing syndrome coverage, and demonstrate substantial data-efficiency gains across BCH codes of moderate length. The results have practical implications for deploying data-efficient neural decoders and extend to model-based decoders, suggesting that data-centric training can close much of the gap to MLD without prohibitive data requirements.

Abstract

While significant research efforts have been directed toward developing more capable neural decoding architectures, comparatively little attention has been paid to the quality of training data. In this study, we address the challenge of constructing effective training datasets to maximize the potential of existing syndrome-based neural decoder architectures. We emphasize the advantages of using fixed datasets over generating training data dynamically and explore the problem of selecting appropriate training targets within this framework. Furthermore,we propose several heuristics for selecting training samples and present experimental evidence demonstrating that, with carefully curated datasets, it is possible to train neural decoders to achieve superior performance while requiring fewer training examples.

Doing More With Less: Towards More Data-Efficient Syndrome-Based Neural Decoders

TL;DR

This work shows that data quality and training data design are critical for syndrome-based neural decoders (SBND) to approach maximum-likelihood decoding (MLD) on short codes. By using fixed, well-constructed datasets and targeting MLD-like error patterns rather than true channel errors, SBNDs such as GRU-based and ECCT models achieve near-MLD performance with far fewer training samples. The authors introduce several dataset-design heuristics, including optimizing the training distribution and balancing syndrome coverage, and demonstrate substantial data-efficiency gains across BCH codes of moderate length. The results have practical implications for deploying data-efficient neural decoders and extend to model-based decoders, suggesting that data-centric training can close much of the gap to MLD without prohibitive data requirements.

Abstract

While significant research efforts have been directed toward developing more capable neural decoding architectures, comparatively little attention has been paid to the quality of training data. In this study, we address the challenge of constructing effective training datasets to maximize the potential of existing syndrome-based neural decoder architectures. We emphasize the advantages of using fixed datasets over generating training data dynamically and explore the problem of selecting appropriate training targets within this framework. Furthermore,we propose several heuristics for selecting training samples and present experimental evidence demonstrating that, with carefully curated datasets, it is possible to train neural decoders to achieve superior performance while requiring fewer training examples.

Paper Structure

This paper contains 29 sections, 4 equations, 8 figures.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Transmission model
  4. Optimal decoder
  5. Syndrome-based deep learning decoders
  6. SBND models implementation, training, and testing
  7. Model input
  8. Core DNN implementation
  9. Model output
  10. SBND model training
  11. SBND model evaluation
  12. The Training With On-Demand Data Paradigm
  13. What does training with on-demand data mean?
  14. Pros and cons of this approach
  15. Improving Training With The Help Of Well-Designed Datasets
  16. ...and 14 more sections

Figures (8)

  • Figure 1: Transmission system model.
  • Figure 2: General architecture of a syndrome-based neural decoder.
  • Figure 3: Frame error rate as a function of the number of training samples for different SBND models and training strategies on the $(31,21,5)$ BCH code, at $E_b/N_0=3$ dB (legend: EP=Error Pattern, ML=Maximum-Likelihood).
  • Figure 4: Frame error rate as a function of $E_b/N_0$ for different SBND models and training strategies on the $(31,21,5)$ BCH code.
  • Figure 5: Frame error rate as a function of $E_b/N_0$ for a GRU$(5,3)$ model trained to decode the $(31,21)$ code using different datasets of 4M samples.
  • ...and 3 more figures