A Fast Approximate Check Polytope Projection Algorithm for ADMM Decoding of LDPC Codes

Simplifying the Euclidean projection onto check polytope is an efficient way to reduce the computational complexity of alternating direction method of multipliers (ADMM) decoding algorithm for low-density parity-check (LDPC) codes. Existing algorithms for check polytope projection require sorting op...

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Bibliographic Details
Published in:IEEE communications letters Vol. 23; no. 9; pp. 1520 - 1523
Main Authors: Xia, Qiaoqiao, Lin, Yan, Tang, Shidi, Zhang, Qinglin
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Alternating direction method of multipliers (ADMM)
Approximation algorithms
Binary system
check polytope projection
Classification
Codes
Complexity
Complexity theory
Computer simulation
Decoding
Error correcting codes
Iterative decoding
Iterative methods
line segment projection algorithm
low-density parity-check(LDPC) codes
Projection
Projection algorithms
Search algorithms
Sorting
Sorting algorithms
ISSN:1089-7798, 1558-2558
Online Access:Get full text
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Summary:Simplifying the Euclidean projection onto check polytope is an efficient way to reduce the computational complexity of alternating direction method of multipliers (ADMM) decoding algorithm for low-density parity-check (LDPC) codes. Existing algorithms for check polytope projection require sorting operation or iterative operation, which happens to be the most complex part of the projection. In this letter, a novel and fast projection algorithm is proposed without sorting and iterative operations. In the proposed algorithm, line segment projection replaces check polytope projection to approach approximate Euclidean projection at low computational complexity. Simulation results show that the proposed algorithm can substantially reduce the projection time while maintaining the frame error rate (FER) performance. In particular, the proposed algorithm can save the average projection time by 43% compared with cut search algorithm (CSA) when the dimension of the input vector is 20.
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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2019.2926085