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Using linear programming (LP) decoding based on alternating direction method of multipliers (ADMM) for low-density parity-check (LDPC) codes shows lower complexity than the original LP decoding. However, the development of the ADMM-LP decoding algorithm could still be limited by the computational complexity of Euclidean projections onto parity check polytope. In this paper, we proposed a bisection method iterative algorithm (BMIA) for projection onto parity check polytope avoiding sorting operation and the complexity is linear. In addition, the convergence of the proposed algorithm is more than three times as fast as the existing algorithm, which can even be 10 times in the case of high input dimension.
Yan LIN
Central China Normal University
Qiaoqiao XIA
Central China Normal University
Wenwu HE
Wuhan University
Qinglin ZHANG
Central China Normal University
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Yan LIN, Qiaoqiao XIA, Wenwu HE, Qinglin ZHANG, "A Fast Iterative Check Polytope Projection Algorithm for ADMM Decoding of LDPC Codes by Bisection Method" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 10, pp. 1406-1410, October 2019, doi: 10.1587/transfun.E102.A.1406.
Abstract: Using linear programming (LP) decoding based on alternating direction method of multipliers (ADMM) for low-density parity-check (LDPC) codes shows lower complexity than the original LP decoding. However, the development of the ADMM-LP decoding algorithm could still be limited by the computational complexity of Euclidean projections onto parity check polytope. In this paper, we proposed a bisection method iterative algorithm (BMIA) for projection onto parity check polytope avoiding sorting operation and the complexity is linear. In addition, the convergence of the proposed algorithm is more than three times as fast as the existing algorithm, which can even be 10 times in the case of high input dimension.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.1406/_p
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@ARTICLE{e102-a_10_1406,
author={Yan LIN, Qiaoqiao XIA, Wenwu HE, Qinglin ZHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Fast Iterative Check Polytope Projection Algorithm for ADMM Decoding of LDPC Codes by Bisection Method},
year={2019},
volume={E102-A},
number={10},
pages={1406-1410},
abstract={Using linear programming (LP) decoding based on alternating direction method of multipliers (ADMM) for low-density parity-check (LDPC) codes shows lower complexity than the original LP decoding. However, the development of the ADMM-LP decoding algorithm could still be limited by the computational complexity of Euclidean projections onto parity check polytope. In this paper, we proposed a bisection method iterative algorithm (BMIA) for projection onto parity check polytope avoiding sorting operation and the complexity is linear. In addition, the convergence of the proposed algorithm is more than three times as fast as the existing algorithm, which can even be 10 times in the case of high input dimension.},
keywords={},
doi={10.1587/transfun.E102.A.1406},
ISSN={1745-1337},
month={October},}
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TY - JOUR
TI - A Fast Iterative Check Polytope Projection Algorithm for ADMM Decoding of LDPC Codes by Bisection Method
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1406
EP - 1410
AU - Yan LIN
AU - Qiaoqiao XIA
AU - Wenwu HE
AU - Qinglin ZHANG
PY - 2019
DO - 10.1587/transfun.E102.A.1406
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2019
AB - Using linear programming (LP) decoding based on alternating direction method of multipliers (ADMM) for low-density parity-check (LDPC) codes shows lower complexity than the original LP decoding. However, the development of the ADMM-LP decoding algorithm could still be limited by the computational complexity of Euclidean projections onto parity check polytope. In this paper, we proposed a bisection method iterative algorithm (BMIA) for projection onto parity check polytope avoiding sorting operation and the complexity is linear. In addition, the convergence of the proposed algorithm is more than three times as fast as the existing algorithm, which can even be 10 times in the case of high input dimension.
ER -