IEICE TRANSACTIONS on Fundamentals
An Enhanced HDPC-EVA Decoder Based on ADMM
Summary :
Linear programming (LP) decoding based on the alternating direction method of multipliers (ADMM) has proved to be effective for low-density parity-check (LDPC) codes. However, for high-density parity-check (HDPC) codes, the ADMM-LP decoder encounters two problems, namely a high-density check matrix in HDPC codes and a great number of pseudocodewords in HDPC codes' fundamental polytope. The former problem makes the check polytope projection extremely complex, and the latter one leads to poor frame error rates (FER) performance. To address these issues, we introduce the even vertex algorithm (EVA) into the ADMM-LP decoding algorithm for HDPC codes, named as HDPC-EVA. HDPC-EVA can reduce the complexity of the projection process and improve the FER performance. We further enhance the proposed decoder by the automorphism groups of codes, creating diversity in the parity-check matrix. The simulation results show that the proposed decoder is capable of cutting down the average decoding time for each iteration by 30%-60%, as well as achieving near maximum likelihood (ML) performance on some BCH codes.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E104-A No.10 pp.1425-1429
- Publication Date
- 2021/10/01
- Publicized
- 2021/04/02
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2020EAL2116
- Type of Manuscript
- LETTER
- Category
- Coding Theory
Authors
Yujin ZHENG
Central China Normal University
Yan LIN
Central China Normal University
Zhuo ZHANG
Shanghai Aerospace Electronic Technology Institute
Qinglin ZHANG
Central China Normal University
Qiaoqiao XIA
Central China Normal University
Keyword
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Yujin ZHENG, Yan LIN, Zhuo ZHANG, Qinglin ZHANG, Qiaoqiao XIA, "An Enhanced HDPC-EVA Decoder Based on ADMM" in IEICE TRANSACTIONS on Fundamentals,
vol. E104-A, no. 10, pp. 1425-1429, October 2021, doi: 10.1587/transfun.2020EAL2116.
Abstract: Linear programming (LP) decoding based on the alternating direction method of multipliers (ADMM) has proved to be effective for low-density parity-check (LDPC) codes. However, for high-density parity-check (HDPC) codes, the ADMM-LP decoder encounters two problems, namely a high-density check matrix in HDPC codes and a great number of pseudocodewords in HDPC codes' fundamental polytope. The former problem makes the check polytope projection extremely complex, and the latter one leads to poor frame error rates (FER) performance. To address these issues, we introduce the even vertex algorithm (EVA) into the ADMM-LP decoding algorithm for HDPC codes, named as HDPC-EVA. HDPC-EVA can reduce the complexity of the projection process and improve the FER performance. We further enhance the proposed decoder by the automorphism groups of codes, creating diversity in the parity-check matrix. The simulation results show that the proposed decoder is capable of cutting down the average decoding time for each iteration by 30%-60%, as well as achieving near maximum likelihood (ML) performance on some BCH codes.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2020EAL2116/_p
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@ARTICLE{e104-a_10_1425,
author={Yujin ZHENG, Yan LIN, Zhuo ZHANG, Qinglin ZHANG, Qiaoqiao XIA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={An Enhanced HDPC-EVA Decoder Based on ADMM},
year={2021},
volume={E104-A},
number={10},
pages={1425-1429},
abstract={Linear programming (LP) decoding based on the alternating direction method of multipliers (ADMM) has proved to be effective for low-density parity-check (LDPC) codes. However, for high-density parity-check (HDPC) codes, the ADMM-LP decoder encounters two problems, namely a high-density check matrix in HDPC codes and a great number of pseudocodewords in HDPC codes' fundamental polytope. The former problem makes the check polytope projection extremely complex, and the latter one leads to poor frame error rates (FER) performance. To address these issues, we introduce the even vertex algorithm (EVA) into the ADMM-LP decoding algorithm for HDPC codes, named as HDPC-EVA. HDPC-EVA can reduce the complexity of the projection process and improve the FER performance. We further enhance the proposed decoder by the automorphism groups of codes, creating diversity in the parity-check matrix. The simulation results show that the proposed decoder is capable of cutting down the average decoding time for each iteration by 30%-60%, as well as achieving near maximum likelihood (ML) performance on some BCH codes.},
keywords={},
doi={10.1587/transfun.2020EAL2116},
ISSN={1745-1337},
month={October},}
Copy
TY - JOUR
TI - An Enhanced HDPC-EVA Decoder Based on ADMM
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1425
EP - 1429
AU - Yujin ZHENG
AU - Yan LIN
AU - Zhuo ZHANG
AU - Qinglin ZHANG
AU - Qiaoqiao XIA
PY - 2021
DO - 10.1587/transfun.2020EAL2116
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E104-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2021
AB - Linear programming (LP) decoding based on the alternating direction method of multipliers (ADMM) has proved to be effective for low-density parity-check (LDPC) codes. However, for high-density parity-check (HDPC) codes, the ADMM-LP decoder encounters two problems, namely a high-density check matrix in HDPC codes and a great number of pseudocodewords in HDPC codes' fundamental polytope. The former problem makes the check polytope projection extremely complex, and the latter one leads to poor frame error rates (FER) performance. To address these issues, we introduce the even vertex algorithm (EVA) into the ADMM-LP decoding algorithm for HDPC codes, named as HDPC-EVA. HDPC-EVA can reduce the complexity of the projection process and improve the FER performance. We further enhance the proposed decoder by the automorphism groups of codes, creating diversity in the parity-check matrix. The simulation results show that the proposed decoder is capable of cutting down the average decoding time for each iteration by 30%-60%, as well as achieving near maximum likelihood (ML) performance on some BCH codes.
ER -
English
