Uniformly reweighted APP decoder for memory efficient decoding of LDPC Codes

Velimir Ilic; Elsa Dupraz; David Declercq; Bane V Vasic

doi:10.1109/ALLERTON.2014.7028595

Uniformly reweighted APP decoder for memory efficient decoding of LDPC Codes

Velimir Ilic, Elsa Dupraz, David Declercq, Bane V Vasic

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

In this paper we propose a uniformly reweighted a posteriori probability (APP) decoder. The APP decoder is well-known to be suboptimal compared to the BP decoder. Here, we derive the APP decoder as an algorithm of approximate Bayesian inference on the LDPC code graph and introduce a correction parameter to overcome the suboptimaly of the APP decoder. We optimize numerically the correction parameter and show that it improves the BER performance of the APP decoder compared to its non-corrected version. In addition, the original APP decoder requires memory that is linear in the number of edges in the code graph. Here, we propose a memory efficient implementation of the algorithm that requires memory that is linear only in the codeword length.

Original language	English (US)
Title of host publication	2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1228-1232
Number of pages	5
ISBN (Print)	9781479980093
DOIs	https://doi.org/10.1109/ALLERTON.2014.7028595
State	Published - Jan 30 2014
Event	2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014 - Monticello, United States Duration: Sep 30 2014 → Oct 3 2014

Other

Other	2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014
Country	United States
City	Monticello
Period	9/30/14 → 10/3/14

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications

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Uniformly reweighted APP decoder for memory efficient decoding of LDPC Codes. / Ilic, Velimir; Dupraz, Elsa; Declercq, David; Vasic, Bane V.

2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1228-1232 7028595.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ilic, V, Dupraz, E, Declercq, D & Vasic, BV 2014, Uniformly reweighted APP decoder for memory efficient decoding of LDPC Codes. in 2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014., 7028595, Institute of Electrical and Electronics Engineers Inc., pp. 1228-1232, 2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014, Monticello, United States, 9/30/14. https://doi.org/10.1109/ALLERTON.2014.7028595

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title = "Uniformly reweighted APP decoder for memory efficient decoding of LDPC Codes",

abstract = "In this paper we propose a uniformly reweighted a posteriori probability (APP) decoder. The APP decoder is well-known to be suboptimal compared to the BP decoder. Here, we derive the APP decoder as an algorithm of approximate Bayesian inference on the LDPC code graph and introduce a correction parameter to overcome the suboptimaly of the APP decoder. We optimize numerically the correction parameter and show that it improves the BER performance of the APP decoder compared to its non-corrected version. In addition, the original APP decoder requires memory that is linear in the number of edges in the code graph. Here, we propose a memory efficient implementation of the algorithm that requires memory that is linear only in the codeword length.",

author = "Velimir Ilic and Elsa Dupraz and David Declercq and Vasic, {Bane V}",

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N2 - In this paper we propose a uniformly reweighted a posteriori probability (APP) decoder. The APP decoder is well-known to be suboptimal compared to the BP decoder. Here, we derive the APP decoder as an algorithm of approximate Bayesian inference on the LDPC code graph and introduce a correction parameter to overcome the suboptimaly of the APP decoder. We optimize numerically the correction parameter and show that it improves the BER performance of the APP decoder compared to its non-corrected version. In addition, the original APP decoder requires memory that is linear in the number of edges in the code graph. Here, we propose a memory efficient implementation of the algorithm that requires memory that is linear only in the codeword length.

AB - In this paper we propose a uniformly reweighted a posteriori probability (APP) decoder. The APP decoder is well-known to be suboptimal compared to the BP decoder. Here, we derive the APP decoder as an algorithm of approximate Bayesian inference on the LDPC code graph and introduce a correction parameter to overcome the suboptimaly of the APP decoder. We optimize numerically the correction parameter and show that it improves the BER performance of the APP decoder compared to its non-corrected version. In addition, the original APP decoder requires memory that is linear in the number of edges in the code graph. Here, we propose a memory efficient implementation of the algorithm that requires memory that is linear only in the codeword length.

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Uniformly reweighted APP decoder for memory efficient decoding of LDPC Codes

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