Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

IET Communications

Decoding of the (24, 12, 8) extended Golay code up to four errors

Tsung-Chieh Lin, Trieu‐Kien Truong, Wen-Ku Su, P.-Y. Shih, G. Dubney

Published Feb 4, 2009• 11 citations
Publisher

Abstract

A new decoder is proposed to decode the (24, 12, 8) binary extended Golay code up to four errors. It consists of the conventional hard decoder for correcting up to three errors, the detection algorithm for four errors and the soft decoding for four errors. For a weight-4 error in a received 24-bit word, Method 1 or 2 is developed to determine all six possible error patterns. The emblematic probability value of each error pattern is then defined as the product of four individual bit-error probabilities corresponding to the locations of the four errors. The most likely one among these six error patterns is obtained by choosing the maximum of the emblematic probability values of all possible error patterns. Finally, simulation results of this decoder in additive white Gaussian noise show that at least 93% and 99% of weight-4 error patterns that occur are corrected if the two Eb/N0 ratios are greater than 2 and 5 dB, respectively. Consequently, the proposed method can achieve a better percentage of successful decoding for four errors at variable signal-to-noise ratios than Lu et al.'s algorithm in software. However, the speed of the method is slower than Lu et al.'s algorithm.

DOI
10.1049/iet-com:20080037

Related papers

On soft-decoding of the (24, 12, 8) extended Golay code up to six errors

Tsung‐Ching Lin, Pei-Yu Shih, Wen-Ku Su +1 more

In this paper, a new soft-decision decoder of the (24, 12, 8) binary extended Golay code up to six errors is proposed. First, by using the error pattern obtained from hard decoder, the method of determining possible error patterns is developed. The emblematic probability value of each error pattern is then defined as the product of the individual bit-error probabilities corresponding to the locations of the possible error patterns. The most likely one among these error patterns is obtained by choosing the maximum of the emblematic probability values of possible error patterns. Finally, simulation results in additive white Gaussian noise (AWGN) show that this decoder reduce the decoding complexity although it performs a slight loss of coding gain than the modified Chase's II algorithm proposed by Hackett.

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)•Sep 1, 2009•0 citations

On soft-decoding of the (24, 12, 8) Golay code

Wen-Ku Su, Pei-Yu Shih, Tsung‐Ching Lin +2 more

In this paper, an efficient soft-decision decoder of the (24, 12, 8) binary Golay code for the four errors, composed of two stages: a conventional hard decoder up to three errors and the soft decoding for four errors, is proposed. All probable patterns of occurred weight-4 error, which are always decoded to the same weight-3 error pattern, are determined from the lookup table of weight-7 codewords. And the most possible one will be obtained by estimating the emblematic probability values of all probable patterns. The simulation result of this decoder in additive white Gaussian noise (AWGN) shows that at least 92% and 99% of weight-4 error patterns occurred are corrected if a bit-energy to noise-spectral-density ratios (Eb/N0) are greater than 2 dB and 5 dB, respectively.

IEEE Region 10 Conference (TENCON)•Oct 1, 2007•4 citations

Soft decoding of the (23, 12, 7) Golay-code up to five errors

Y. Li, Lin Wang, T. K. Truong

A new decoder is proposed to decode the (23, 12, 7) binary Golay-code up to five errors. It is based on the algorithm that can correct up to four errors for the (24, 12, 8) extended Golay-code proposed by Lin et al., thereby achieving the soft decoding in the real sense for the Golay-code. For a weight-2 or weight-3 error pattern decoded by the hard decoder for correcting up to three errors, one can find the corresponding 21 weight-4 or weight-5 error patterns and choose the one with the maximum emblematic probability value, which is defined as the product of individual bit-error probabilities corresponding to the non-zero locations of the error pattern as the ultimate choice. Finally, simulation results of this decoder over additive white Gaussian noise (AWGN) channels show that the proposed method provides 0.9 dB coding gain than that of Lin et al.'s algorithm at bit-error rate of 10−5.

IET Communications•Oct 14, 2011•1 citation

Soft-decoding of the (23, 12, 7) Binary Golay Code

Wen-Ku Su, Pei-Yu Shih, Tsung‐Ching Lin +1 more

Abstract—Since the (23, 12, 7) binary Golay code is a perfect code, a weight-4 error occurred is always decoded as a weight-3 error pattern by a hard decoding method. In this paper, an efficient soft-decision decoder of the (23, 12, 7) binary Golay code up to the four errors is proposed. All probable patterns of occurred weight-4 error, which are always decoded to the same weight-3 error pattern, are determined from the look-up table of weight-7 codewords. And the most possible error pattern of weight-4 or weight-3 will be obtained by estimating the emblematic probability values of all probable patterns. The simulation result of this decoder in additive white Gaussian noise (AWGN) shows that at least 93 % and 99 % of weight-4 error patterns occurred are corrected if a bit-energy to noise-spectral-density ratios (E b/N 0) are greater than 3 dB and 6 dB, respectively, and at least 96 % of weight-3 error patterns occurred are corrected for any dB. I.

International MultiConference of Engineers and Computer Scientists•Mar 1, 2008•1 citation

Decoding the extended binary Golay code by an iterative invert-one-bit scheme

Wen-Ku Su, Pei-Yu Shih, Tsung‐Ching Lin +1 more

In this paper, a new decoding algorithm of the extended binary Golay code is proposed. Based on the proposition developed in this paper, all probable patterns of occurred weight-4 error, which are always decoded to the same weight-3 error pattern, are determined from an iterative invert-one-bit scheme instead of the look-up table of weight-7 codewords. And the most possible one will be obtained by estimating the emblematic probability values of all probable patterns. The simulation result of this decoder in AWGN shows that more than 99% of four errors are corrected if Eb/N0 is greater than 5 dB.

IEEE International Conference on Communication Technology (ICCT)•Nov 1, 2008•3 citations

QGEC : Quantum Golay Code Error Correction

Hideo Mukai, Hoshitaro Ohnishi

Quantum computers have the possibility of a much reduced calculation load compared with classical computers in specific problems. Quantum error correction (QEC) is vital for handling qubits, which are vulnerable to external noise. In QEC, actual errors are predicted from the results of syndrome measurements by stabilizer generators, in place of making direct measurements of the data qubits. Here, we propose Quantum Golay code Error Correction (QGEC), a QEC method using Golay code, which is an efficient coding method in classical information theory. We investigated our method's ability in decoding calculations with the Transformer. We evaluated the accuracy of the decoder in a code space defined by the generative polynomials with three different weights sets and three noise models with different correlations of bit-flip error and phase-flip error. Furthermore, under a noise model following a discrete uniform distribution, we compared the decoding performance of Transformer decoders with identical architectures trained respectively on Golay and toric codes. The results showed that the noise model with the smaller correlation gave better accuracy, while the weights of the generative polynomials had little effect on the accuracy of the decoder. In addition, they showed that Golay code requiring 23 data qubits and having a code distance of 7 achieved higher decoding accuracy than toric code which requiring 50 data qubits and having a code distance of 5. This suggests that implementing quantum error correction using a Transformer may enable the Golay code to realize fault-tolerant quantum computation more efficiently.

cs.LGCross-listedarXiv:2512.11307•Dec 15, 2025•0 citations
A new decoder is proposed to decode the (24, 12, 8) binary extended Golay code up to four errors. It consists of the conventional hard decoder for correcting up to three errors, the detection algorithm for four errors and the soft decoding for four errors. For a weight-4 error in a received 24-bit word, Method 1 or 2 is developed to determine all six possible error patterns. The emblematic probability value of each error pattern is then defined as the product of four individual bit-error probabilities corresponding to the locations of the four errors. The most likely one among these six error patterns is obtained by choosing the maximum of the emblematic probability values of all possible error patterns. Finally, simulation results of this decoder in additive white Gaussian noise show that at least 93% and 99% of weight-4 error patterns that occur are corrected if the two Eb/N0 ratios are greater than 2 and 5 dB, respectively. Consequently, the proposed method can achieve a better percentage of successful decoding for four errors at variable signal-to-noise ratios than Lu et al.'s algorithm in software. However, the speed of the method is slower than Lu et al.'s algorithm.
In this paper, a new soft-decision decoder of the (24, 12, 8) binary extended Golay code up to six errors is proposed. First, by using the error pattern obtained from hard decoder, the method of determining possible error patterns is developed. The emblematic probability value of each error pattern is then defined as the product of the individual bit-error probabilities corresponding to the locations of the possible error patterns. The most likely one among these error patterns is obtained by choosing the maximum of the emblematic probability values of possible error patterns. Finally, simulation results in additive white Gaussian noise (AWGN) show that this decoder reduce the decoding complexity although it performs a slight loss of coding gain than the modified Chase's II algorithm proposed by Hackett.
In this paper, an efficient soft-decision decoder of the (24, 12, 8) binary Golay code for the four errors, composed of two stages: a conventional hard decoder up to three errors and the soft decoding for four errors, is proposed. All probable patterns of occurred weight-4 error, which are always decoded to the same weight-3 error pattern, are determined from the lookup table of weight-7 codewords. And the most possible one will be obtained by estimating the emblematic probability values of all probable patterns. The simulation result of this decoder in additive white Gaussian noise (AWGN) shows that at least 92% and 99% of weight-4 error patterns occurred are corrected if a bit-energy to noise-spectral-density ratios (Eb/N0) are greater than 2 dB and 5 dB, respectively.
A new decoder is proposed to decode the (23, 12, 7) binary Golay-code up to five errors. It is based on the algorithm that can correct up to four errors for the (24, 12, 8) extended Golay-code proposed by Lin et al., thereby achieving the soft decoding in the real sense for the Golay-code. For a weight-2 or weight-3 error pattern decoded by the hard decoder for correcting up to three errors, one can find the corresponding 21 weight-4 or weight-5 error patterns and choose the one with the maximum emblematic probability value, which is defined as the product of individual bit-error probabilities corresponding to the non-zero locations of the error pattern as the ultimate choice. Finally, simulation results of this decoder over additive white Gaussian noise (AWGN) channels show that the proposed method provides 0.9 dB coding gain than that of Lin et al.'s algorithm at bit-error rate of 10−5.
Abstract—Since the (23, 12, 7) binary Golay code is a perfect code, a weight-4 error occurred is always decoded as a weight-3 error pattern by a hard decoding method. In this paper, an efficient soft-decision decoder of the (23, 12, 7) binary Golay code up to the four errors is proposed. All probable patterns of occurred weight-4 error, which are always decoded to the same weight-3 error pattern, are determined from the look-up table of weight-7 codewords. And the most possible error pattern of weight-4 or weight-3 will be obtained by estimating the emblematic probability values of all probable patterns. The simulation result of this decoder in additive white Gaussian noise (AWGN) shows that at least 93 % and 99 % of weight-4 error patterns occurred are corrected if a bit-energy to noise-spectral-density ratios (E b/N 0) are greater than 3 dB and 6 dB, respectively, and at least 96 % of weight-3 error patterns occurred are corrected for any dB. I.
In this paper, a new decoding algorithm of the extended binary Golay code is proposed. Based on the proposition developed in this paper, all probable patterns of occurred weight-4 error, which are always decoded to the same weight-3 error pattern, are determined from an iterative invert-one-bit scheme instead of the look-up table of weight-7 codewords. And the most possible one will be obtained by estimating the emblematic probability values of all probable patterns. The simulation result of this decoder in AWGN shows that more than 99% of four errors are corrected if Eb/N0 is greater than 5 dB.
Quantum computers have the possibility of a much reduced calculation load compared with classical computers in specific problems. Quantum error correction (QEC) is vital for handling qubits, which are vulnerable to external noise. In QEC, actual errors are predicted from the results of syndrome measurements by stabilizer generators, in place of making direct measurements of the data qubits. Here, we propose Quantum Golay code Error Correction (QGEC), a QEC method using Golay code, which is an efficient coding method in classical information theory. We investigated our method's ability in decoding calculations with the Transformer. We evaluated the accuracy of the decoder in a code space defined by the generative polynomials with three different weights sets and three noise models with different correlations of bit-flip error and phase-flip error. Furthermore, under a noise model following a discrete uniform distribution, we compared the decoding performance of Transformer decoders with identical architectures trained respectively on Golay and toric codes. The results showed that the noise model with the smaller correlation gave better accuracy, while the weights of the generative polynomials had little effect on the accuracy of the decoder. In addition, they showed that Golay code requiring 23 data qubits and having a code distance of 7 achieved higher decoding accuracy than toric code which requiring 50 data qubits and having a code distance of 5. This suggests that implementing quantum error correction using a Transformer may enable the Golay code to realize fault-tolerant quantum computation more efficiently.