Neural layered min-sum decoders for cyclic codes

Ming Wang, Yong Li, Jianqing Liu, Taolin Guo, Huihui Wu, Francis C.M. Lau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)


This paper proposes a low-complexity neural network decoder based on the layered min-sum algorithm to decode cyclic codes. By generalizing the layered min-sum algorithm to its neural network counterpart, the number of network weights decreases while retaining a good error correction performance. The Bose–Chaudhuri–Hocquenghem (BCH) codes, quadratic residue (QR) codes, and punctured Reed–Muller (RM) codes are selected as three exemplary binary cyclic codes. Simulation results show that the proposed neural decoder achieves superior performance with less computational complexity compared with the state-of-the-art neural network decoder. Further, a neural decoder incorporating the modified random redundant decoding (mRRD) algorithm is investigated to approach the performance of maximum-likelihood decoding for some short codes.

Original languageEnglish
Article number102194
JournalPhysical Communication
Publication statusPublished - Dec 2023


  • Channel codes
  • Layered min-sum algorithm
  • Modified random redundant decoding
  • Neural network decoders

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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