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Reed-Solomon Coding Algorithms Based on Reed-Muller Transform for Any Number of Parities

Based on the Reed-Muller (RM) transform, this paper proposes a Reed-Solomon (RS) encoding/erasure decoding algorithm for any number of parities. Specifically, we first generalize the previous RM-based syndrome calculation, which allows only up to seven parities, to support any number of parities. Th...

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Published in:IEEE transactions on computers 2023-09, Vol.72 (9), p.2677-2688
Main Authors: Yu, Leilei, Lin, Sian-Jheng, Hou, Hanxu, Li, Zhengrui
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description Based on the Reed-Muller (RM) transform, this paper proposes a Reed-Solomon (RS) encoding/erasure decoding algorithm for any number of parities. Specifically, we first generalize the previous RM-based syndrome calculation, which allows only up to seven parities, to support any number of parities. Then we propose a general encoding/erasure decoding algorithm. The proposed encoding algorithm eliminates the operations in solving linear equations, and this improves the computational efficiency of existing RM-based RS algorithms. In terms of erasure decoding, this paper employs the generalized RM-based syndrome calculation and lower-upper (LU) decomposition to accelerate the computational efficiency. Analysis shows that the proposed encoding/erasure decoding algorithm approaches the complexity of \lfloor \lg T \rfloor + 1 ⌊lgT⌋+1 XORs per data bit with N N increasing, where T T and N N denote the number of parities and codeword length respectively. To highlight the advantage of the proposed RM-based algorithms, the implementations with Single Instruction Multiple Data (SIMD) technology are provided. Simulation results show that the proposed algorithms are competitive, as compared with other cutting-edge implementations.
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Specifically, we first generalize the previous RM-based syndrome calculation, which allows only up to seven parities, to support any number of parities. Then we propose a general encoding/erasure decoding algorithm. The proposed encoding algorithm eliminates the operations in solving linear equations, and this improves the computational efficiency of existing RM-based RS algorithms. In terms of erasure decoding, this paper employs the generalized RM-based syndrome calculation and lower-upper (LU) decomposition to accelerate the computational efficiency. Analysis shows that the proposed encoding/erasure decoding algorithm approaches the complexity of <inline-formula><tex-math notation="LaTeX">\lfloor \lg T \rfloor + 1</tex-math> <mml:math><mml:mrow><mml:mo>⌊</mml:mo><mml:mo form="prefix">lg</mml:mo><mml:mi>T</mml:mi><mml:mo>⌋</mml:mo><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="yu-ieq1-3262922.gif"/> </inline-formula> XORs per data bit with <inline-formula><tex-math notation="LaTeX">N</tex-math> <mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq2-3262922.gif"/> </inline-formula> increasing, where <inline-formula><tex-math notation="LaTeX">T</tex-math> <mml:math><mml:mi>T</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq3-3262922.gif"/> </inline-formula> and <inline-formula><tex-math notation="LaTeX">N</tex-math> <mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq4-3262922.gif"/> </inline-formula> denote the number of parities and codeword length respectively. To highlight the advantage of the proposed RM-based algorithms, the implementations with Single Instruction Multiple Data (SIMD) technology are provided. Simulation results show that the proposed algorithms are competitive, as compared with other cutting-edge implementations.]]></description><identifier>ISSN: 0018-9340</identifier><identifier>EISSN: 1557-9956</identifier><identifier>DOI: 10.1109/TC.2023.3262922</identifier><identifier>CODEN: ITCOB4</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Codes ; Coding ; computational complexity ; Computational efficiency ; Computing time ; Decoding ; Encoding ; Galois fields ; Linear equations ; Mathematical analysis ; Mathematical models ; Matrix decomposition ; Reed-Muller transform ; Reed-Solomon code ; storage erasure codes ; Transforms ; Vandermonde matrix</subject><ispartof>IEEE transactions on computers, 2023-09, Vol.72 (9), p.2677-2688</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Specifically, we first generalize the previous RM-based syndrome calculation, which allows only up to seven parities, to support any number of parities. Then we propose a general encoding/erasure decoding algorithm. The proposed encoding algorithm eliminates the operations in solving linear equations, and this improves the computational efficiency of existing RM-based RS algorithms. In terms of erasure decoding, this paper employs the generalized RM-based syndrome calculation and lower-upper (LU) decomposition to accelerate the computational efficiency. Analysis shows that the proposed encoding/erasure decoding algorithm approaches the complexity of <inline-formula><tex-math notation="LaTeX">\lfloor \lg T \rfloor + 1</tex-math> <mml:math><mml:mrow><mml:mo>⌊</mml:mo><mml:mo form="prefix">lg</mml:mo><mml:mi>T</mml:mi><mml:mo>⌋</mml:mo><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="yu-ieq1-3262922.gif"/> </inline-formula> XORs per data bit with <inline-formula><tex-math notation="LaTeX">N</tex-math> <mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq2-3262922.gif"/> </inline-formula> increasing, where <inline-formula><tex-math notation="LaTeX">T</tex-math> <mml:math><mml:mi>T</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq3-3262922.gif"/> </inline-formula> and <inline-formula><tex-math notation="LaTeX">N</tex-math> <mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq4-3262922.gif"/> </inline-formula> denote the number of parities and codeword length respectively. To highlight the advantage of the proposed RM-based algorithms, the implementations with Single Instruction Multiple Data (SIMD) technology are provided. 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Specifically, we first generalize the previous RM-based syndrome calculation, which allows only up to seven parities, to support any number of parities. Then we propose a general encoding/erasure decoding algorithm. The proposed encoding algorithm eliminates the operations in solving linear equations, and this improves the computational efficiency of existing RM-based RS algorithms. In terms of erasure decoding, this paper employs the generalized RM-based syndrome calculation and lower-upper (LU) decomposition to accelerate the computational efficiency. Analysis shows that the proposed encoding/erasure decoding algorithm approaches the complexity of <inline-formula><tex-math notation="LaTeX">\lfloor \lg T \rfloor + 1</tex-math> <mml:math><mml:mrow><mml:mo>⌊</mml:mo><mml:mo form="prefix">lg</mml:mo><mml:mi>T</mml:mi><mml:mo>⌋</mml:mo><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="yu-ieq1-3262922.gif"/> </inline-formula> XORs per data bit with <inline-formula><tex-math notation="LaTeX">N</tex-math> <mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq2-3262922.gif"/> </inline-formula> increasing, where <inline-formula><tex-math notation="LaTeX">T</tex-math> <mml:math><mml:mi>T</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq3-3262922.gif"/> </inline-formula> and <inline-formula><tex-math notation="LaTeX">N</tex-math> <mml:math><mml:mi>N</mml:mi></mml:math><inline-graphic xlink:href="yu-ieq4-3262922.gif"/> </inline-formula> denote the number of parities and codeword length respectively. To highlight the advantage of the proposed RM-based algorithms, the implementations with Single Instruction Multiple Data (SIMD) technology are provided. Simulation results show that the proposed algorithms are competitive, as compared with other cutting-edge implementations.]]></abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TC.2023.3262922</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0003-3528-5483</orcidid><orcidid>https://orcid.org/0000-0002-6309-2876</orcidid><orcidid>https://orcid.org/0000-0001-7328-9341</orcidid><orcidid>https://orcid.org/0000-0001-8861-2535</orcidid></addata></record>
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subjects Algorithms
Codes
Coding
computational complexity
Computational efficiency
Computing time
Decoding
Encoding
Galois fields
Linear equations
Mathematical analysis
Mathematical models
Matrix decomposition
Reed-Muller transform
Reed-Solomon code
storage erasure codes
Transforms
Vandermonde matrix
title Reed-Solomon Coding Algorithms Based on Reed-Muller Transform for Any Number of Parities
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