Adaptive deactivation and zero-forcing scheme for low-complexity LDPC decoders

Taehyun Kim, Jonghyun Baik, Myeongwoo Lee, Jun Heo*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    Abstract

    A modified message propagation algorithm is proposed for a low-complexity decoder of low-density parity-check (LDPC) codes, which controls the information propagated from variable and check nodes.The proposed threshold-based node deactivation for variable nodes and zero-forcing scheme for check nodes remarkably reduce decoding complexity required for similar error performance. In the proposed scheme, different thresholds, which are determined from the base matrix of the LDPC codes, are applied for each type of variable node. In addition, thresholds for deactivating variable nodes are increased while the decoding process is operated for a reduction in decoding complexity without early error floor, which is a drawback of the conventional threshold-based deactivation scheme. Simulation results show that the proposed scheme enables normalized min-sum decoders to decode successfully with less complexity than the conventional threshold-based deactivating scheme.

    Original languageEnglish
    Article number153
    JournalEurasip Journal on Wireless Communications and Networking
    Volume2017
    Issue number1
    DOIs
    Publication statusPublished - 2017 Dec 1

    Bibliographical note

    Funding Information:
    This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2015-0-00385) supervised by the IITP (Institute for Information and communications Technology Promotion).

    Publisher Copyright:
    © 2017, The Author(s).

    Keywords

    • Complexity reduction
    • Low-density parity-check codes
    • Normalized min-sum algorithm
    • Protograph-based extrinsic transfer chart

    ASJC Scopus subject areas

    • Signal Processing
    • Computer Science Applications
    • Computer Networks and Communications

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