Neural layered min-sum decoding for protograph LDPC codes

Dexin Zhang, Jincheng Dai, Kailin Tan, Kai Niu, Mingzhe Chen, H. Vincent Poor, Shuguang Cui

Research output: Contribution to journalConference articlepeer-review


In this paper, layered min-sum (MS) iterative decoding is formulated as a customized neural network following the sequential scheduling of check node (CN) updates. By virtue of the lifting structure of protograph low-density parity-check (LDPC) codes, identical network parameters are shared among all derived edges originating from the same edge in the protograph, which makes the number of learnable parameters manageable. The proposed neural layered MS decoder can support arbitrary codelengths consequently. Moreover, an iteration-wise greedy training method is proposed to tune the parameters such that it avoids the vanishing gradient problem and accelerates the decoding convergence.

Original languageEnglish (US)
Pages (from-to)4845-4849
Number of pages5
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 2021
Externally publishedYes
Event2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada
Duration: Jun 6 2021Jun 11 2021

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


  • Layered decoding
  • Min-sum (MS)
  • Neural network
  • Protograph LDPC codes


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