Decoding Quadratic Residue Codes Using Deep Neural Networks

Ming Wang, Yong Li, Rui Liu, Huihui Wu, Youqiang Hu, Francis C.M. Lau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)


In this paper, a low-complexity decoder based on a neural network is proposed to decode binary quadratic residue (QR) codes. The proposed decoder is based on the neural min-sum algorithm and the modified random redundant decoder (mRRD) algorithm. This new method has the same asymptotic time complexity as the min-sum algorithm, which is much lower than the difference on syndromes (DS) algorithm. Simulation results show that the proposed algorithm achieves a gain of more than 0.4 dB when compared to the DS algorithm. Furthermore, a simplified approach based on trapping sets is applied to reduce the complexity of the mRRD. This simplification leads to a slight loss in error performance and a reduction in implementation complexity.

Original languageEnglish
Article number2717
Pages (from-to)1-15
JournalElectronics (Switzerland)
Issue number17
Publication statusPublished - Sept 2022


  • deep learning
  • iterative decoding
  • quadratic residue codes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Hardware and Architecture
  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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