The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
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Propomos a decodificação de produto de soma (SP) com reconhecimento de hardware para códigos de verificação de paridade de baixa densidade. Para simplificar uma implementação usando uma representação numérica de ponto fixo, transformamos a decodificação SP no domínio do logaritmo para aquela no domínio de decisão. Uma aproximação polinomial é proposta para implementar de forma eficiente uma regra de atualização da decodificação SP proposta. Simulações numéricas mostram que a decodificação SP aproximada atinge quase o mesmo desempenho que a decodificação SP exata quando um grau apropriado na aproximação polinomial é usado, que melhora as propriedades de convergência de SP e decodificação de soma mínima normalizada na alta relação sinal-para- regime de relação de ruído e que é robusto contra erros de quantização.
Mizuki YAMADA
Toyohashi University of Technology
Keigo TAKEUCHI
Toyohashi University of Technology
Kiyoyuki KOIKE
Tokyo College
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Mizuki YAMADA, Keigo TAKEUCHI, Kiyoyuki KOIKE, "Hardware-Aware Sum-Product Decoding in the Decision Domain" in IEICE TRANSACTIONS on Fundamentals,
vol. E102-A, no. 12, pp. 1980-1987, December 2019, doi: 10.1587/transfun.E102.A.1980.
Abstract: We propose hardware-aware sum-product (SP) decoding for low-density parity-check codes. To simplify an implementation using a fixed-point number representation, we transform SP decoding in the logarithm domain to that in the decision domain. A polynomial approximation is proposed to implement an update rule of the proposed SP decoding efficiently. Numerical simulations show that the approximate SP decoding achieves almost the same performance as the exact SP decoding when an appropriate degree in the polynomial approximation is used, that it improves the convergence properties of SP and normalized min-sum decoding in the high signal-to-noise ratio regime, and that it is robust against quantization errors.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.1980/_p
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@ARTICLE{e102-a_12_1980,
author={Mizuki YAMADA, Keigo TAKEUCHI, Kiyoyuki KOIKE, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Hardware-Aware Sum-Product Decoding in the Decision Domain},
year={2019},
volume={E102-A},
number={12},
pages={1980-1987},
abstract={We propose hardware-aware sum-product (SP) decoding for low-density parity-check codes. To simplify an implementation using a fixed-point number representation, we transform SP decoding in the logarithm domain to that in the decision domain. A polynomial approximation is proposed to implement an update rule of the proposed SP decoding efficiently. Numerical simulations show that the approximate SP decoding achieves almost the same performance as the exact SP decoding when an appropriate degree in the polynomial approximation is used, that it improves the convergence properties of SP and normalized min-sum decoding in the high signal-to-noise ratio regime, and that it is robust against quantization errors.},
keywords={},
doi={10.1587/transfun.E102.A.1980},
ISSN={1745-1337},
month={December},}
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TY - JOUR
TI - Hardware-Aware Sum-Product Decoding in the Decision Domain
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1980
EP - 1987
AU - Mizuki YAMADA
AU - Keigo TAKEUCHI
AU - Kiyoyuki KOIKE
PY - 2019
DO - 10.1587/transfun.E102.A.1980
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2019
AB - We propose hardware-aware sum-product (SP) decoding for low-density parity-check codes. To simplify an implementation using a fixed-point number representation, we transform SP decoding in the logarithm domain to that in the decision domain. A polynomial approximation is proposed to implement an update rule of the proposed SP decoding efficiently. Numerical simulations show that the approximate SP decoding achieves almost the same performance as the exact SP decoding when an appropriate degree in the polynomial approximation is used, that it improves the convergence properties of SP and normalized min-sum decoding in the high signal-to-noise ratio regime, and that it is robust against quantization errors.
ER -