Neural layered min-sum decoding for protograph LDPC codes

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

doi:10.1109/ICASSP39728.2021.9414543

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 journal › Conference article › peer-review

Abstract

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 language	English (US)
Pages (from-to)	4845-4849
Number of pages	5
Journal	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2021-June
DOIs	https://doi.org/10.1109/ICASSP39728.2021.9414543
State	Published - 2021
Externally published	Yes
Event	2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 - Virtual, Toronto, Canada Duration: Jun 6 2021 → Jun 11 2021

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Electrical and Electronic Engineering

Keywords

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

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10.1109/ICASSP39728.2021.9414543

Cite this

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title = "Neural layered min-sum decoding for protograph LDPC codes",

abstract = "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.",

keywords = "Layered decoding, Min-sum (MS), Neural network, Protograph LDPC codes",

author = "Dexin Zhang and Jincheng Dai and Kailin Tan and Kai Niu and Mingzhe Chen and Poor, {H. Vincent} and Shuguang Cui",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021 ; Conference date: 06-06-2021 Through 11-06-2021",

year = "2021",

doi = "10.1109/ICASSP39728.2021.9414543",

language = "English (US)",

volume = "2021-June",

pages = "4845--4849",

journal = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

issn = "1520-6149",

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TY - JOUR

T1 - Neural layered min-sum decoding for protograph LDPC codes

AU - Zhang, Dexin

AU - Dai, Jincheng

AU - Tan, Kailin

AU - Niu, Kai

AU - Chen, Mingzhe

AU - Poor, H. Vincent

AU - Cui, Shuguang

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Layered decoding

KW - Min-sum (MS)

KW - Neural network

KW - Protograph LDPC codes

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DO - 10.1109/ICASSP39728.2021.9414543

M3 - Conference article

AN - SCOPUS:85115094002

SN - 1520-6149

VL - 2021-June

SP - 4845

EP - 4849

JO - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

JF - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

T2 - 2021 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2021

Y2 - 6 June 2021 through 11 June 2021

ER -

Neural layered min-sum decoding for protograph LDPC codes

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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