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    A Sub-Graph Expansion-Contraction Method for Error Floor Computation

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    A Sub-Graph Expansion-Contraction ...
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    Author
    Raveendran, Nithin
    Declercq, David
    Vasic, Bane
    Affiliation
    Univ Arizona, Dept Elect & Comp Engn
    Issue Date
    2020-04-20
    
    Metadata
    Show full item record
    Publisher
    IEEE
    Citation
    N. Raveendran, D. Declercq and B. Vasić, "A Sub-Graph Expansion-Contraction Method for Error Floor Computation," in IEEE Transactions on Communications, vol. 68, no. 7, pp. 3984-3995, July 2020, doi: 10.1109/TCOMM.2020.2988676
    Journal
    IEEE Transactions on Communications
    Rights
    Copyright © 2020 IEEE.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    In this paper, we present a computationally efficient method for estimating error floors of low-density parity-check (LDPC) codes over the binary symmetric channel (BSC) without any prior knowledge of its trapping sets (TSs). Given the Tanner graph G of a code, and the decoding algorithm D , the method starts from a list of short cycles in G , and expands each cycle by including its sufficiently large neighborhood in G . Variable nodes of the expanded sub-graphs GEXP are then corrupted exhaustively by all possible error patterns, and decoded by D operating on GEXP . Union of support of the error patterns for which D fails on each GEXP defines a subset of variable nodes that is a TS. The knowledge of the minimal error patterns and their strengths in each TSs is used to compute an estimation of the frame error rate. This estimation represents the contribution of error events localized on TSs, and therefore serves as an accurate estimation of the error floor performance of D at low BSC cross-over probabilities. We also discuss trade-offs between accuracy and computational complexity. Our analysis shows that in some cases the proposed method provides a million-fold improvement in computational complexity over standard Monte-Carlo simulation.
    ISSN
    0090-6778
    DOI
    10.1109/tcomm.2020.2988676
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1109/tcomm.2020.2988676
    Scopus Count
    Collections
    UA Faculty Publications

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