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

Telecommunications Forum (TELFOR)

Performance of Self-Corrected APP-Based Decoding Algorithm for Decoders with Quantized Input

Aleksei Kharin, Igor Volkov, Aleksei Ovinnikov, Evgeny A. Likhobabin, Vladimir V. Vityazev

Published Nov 1, 2018• 1 citation
Publisher

Abstract

In this paper we explore the impact of self-correction (SC) modification on performance of a posteriori probability (APP) based decoding algorithm for LDPC codes. Influence of self-correction technique was investigated for decoders with 3, 2 and 1 bit input quantization. It was shown that performance of decoders with quantized input can be significantly improved in comparison with regular APP-based decoding algorithm, and in some cases with Min-Sum algorithm. Convergence of algorithm is almost the same as for Min-Sum decoding. Performance and convergence results are presented for two CCSDS (8176, 7156) and IEEE 802.3an (2048, 1723) LDPC codes for APP-based, SC APP-based and Min-Sum decoding algorithms.

DOI
10.1109/telfor.2018.8612112

Related papers

Performance of Self-Corrected Min-Sum Decoding Algorithm for Decoders with Quantized Input

Aleksei Kharin, Igor Volkov, Aleksei Ovinnikov +2 more

In this paper we explore the Min-Sum decoding algorithm for LDPC codes with self-correction (SC-MS) modification and how it affects the error correction performance for quantized input data. Three levels of quantization are observed: 3-, 2- and 1-bit.It was shown that the self-corrected modification significantly improves the performance of Min-Sum decoding algorithm with quantized input in comparison with basic Min-Sum decoding algorithm. Convergence of the algorithm is also better than for basic Min-Sum algorithm.Results of error correction performance and convergence are presented for two codes: CCSDS (8176, 7156) and IEEE 802.3an (2048, 1723) LDPC. The results are also presented for the SC APP-based and basic Min-Sum decoding algorithms.

International Symposium INFOTEH-JAHORINA (INFOTEH)•Mar 1, 2019•1 citation

Performance of Quantized Min-Sum Decoding Algorithms for Irregular LDPC Codes

Daesun Oh, Keshab K. Parhi

This paper analyzes the performance of quantized min-sum decoding algorithms for irregular low-density parity-check (LDPC) codes. For regular LDPC codes, it is known that the normalized or offset min-sum decoding algorithm with quantization bits less than 6 bits achieves good performances over wide range of signal-to-noise ratios (SNR). However, finite precision effects in decoding irregular LDPC codes are different from that in decoding regular LDPC codes which is caused by the difference of convergence speeds between low degree nodes and high degree nodes. This paper proposes a novel method to improve the performance of the conventional normalized or offset min-sum decoding algorithm when it is approximated with finite precision for hardware implementations. The proposed method applies down-scaling factors to intrinsic information which has effects on increasing the reliability of extrinsic information at variable nodes and compensating the quantization errors caused by finite precision. Computer simulation results for irregular LDPC codes show that the proposed normalized and offset min-sum decoding algorithms achieve better performances at high SNR compared to the conventional normalized and offset min-sum algorithms under (6:2) quantization scheme.

IEEE International Symposium on Circuits and Systems proceedings•May 1, 2007•15 citations

Modified APP-Based Decoding Algorithm for Low-Density Parity Check Codes

Xinran Wang, Yan Zhang, Jinlei Chen

In this paper, a modified a posteriori probability (APP) based algorithm for the decoding of low- density parity-check (LDPC) codes is proposed. The modified APP-based algorithm utilizes an additional processing in the bit nodes to improve the accuracy instead of the check nodes normalization performed in normalized APP-based algorithm. The modified APP- based algorithm yields desirable trade-off between performance and decoding complexity.

International Conference on Wireless Communications, Networking and Mobile Computing•Sep 1, 2011•3 citations

Self-corrected UMP-APP decoding of LDPC codes

Vladimir V. Vityazev, Evgeny A. Likhobabin

In this paper we propose a very simple but powerful self-correction method for the UMP-APP decoding of LDPC codes. Like self-corrected min-sum decoding, our method does not try to correct the check node processing approximation, but it modifies the variable node processing by erasing unreliable messages. Monte-Carlo simulations show that the proposed self-corrected UMP-APP decoding performs better then common UMP-APP decoding. Estimation of the complexity and average number of iteration for self-corrected UMP-APP algorithm are also shown.

Mediterranean Conference on Embedded Computing (MECO)•Jun 1, 2016•5 citations

Efficient BP-based Decoding Algorithms for QC-LDPC Codes

Konstantin Zavertkin, Anastasia Panarina, Aleksei Ovinnikov +1 more

In this paper we propose to use a self-correction technique with two computationally efficient LDPC decoding algorithms: QRCBP and adjusted min-sum. It is shown that using such a technique allows to improve performance of this algorithms both in error floor and waterfall regions. Also comparison with other BP-based simplified decoding algorithms is presented for three quasi-cyclic LDPC codes.

Mediterranean Conference on Embedded Computing (MECO)•Jun 7, 2021•1 citation

On Implementation of Min-Sum Algorithm and Its Modifications for Decoding Low-Density Parity-Check (LDPC) Codes

J. Zhao, Farhad Zarkeshvari, Amir H. Banihashemi

The effects of clipping and quantization on the performance of the min-sum algorithm for the decoding of low-density parity-check (LDPC) codes at short and intermediate block lengths are studied. It is shown that in many cases, only four quantization bits suffice to obtain close to ideal performance over a wide range of signal-to-noise ratios. Moreover, we propose modifications to the min-sum algorithm that improve the performance by a few tenths of a decibel with just a small increase in decoding complexity. A quantized version of these modified algorithms is also studied. It is shown that, when optimized, modified quantized min-sum algorithms perform very close to, and in some cases even slightly outperform, the ideal belief-propagation algorithm at observed error rates.

IEEE Transactions on Communications•Apr 1, 2005•283 citations
In this paper we explore the impact of self-correction (SC) modification on performance of a posteriori probability (APP) based decoding algorithm for LDPC codes. Influence of self-correction technique was investigated for decoders with 3, 2 and 1 bit input quantization. It was shown that performance of decoders with quantized input can be significantly improved in comparison with regular APP-based decoding algorithm, and in some cases with Min-Sum algorithm. Convergence of algorithm is almost the same as for Min-Sum decoding. Performance and convergence results are presented for two CCSDS (8176, 7156) and IEEE 802.3an (2048, 1723) LDPC codes for APP-based, SC APP-based and Min-Sum decoding algorithms.
In this paper we explore the Min-Sum decoding algorithm for LDPC codes with self-correction (SC-MS) modification and how it affects the error correction performance for quantized input data. Three levels of quantization are observed: 3-, 2- and 1-bit.It was shown that the self-corrected modification significantly improves the performance of Min-Sum decoding algorithm with quantized input in comparison with basic Min-Sum decoding algorithm. Convergence of the algorithm is also better than for basic Min-Sum algorithm.Results of error correction performance and convergence are presented for two codes: CCSDS (8176, 7156) and IEEE 802.3an (2048, 1723) LDPC. The results are also presented for the SC APP-based and basic Min-Sum decoding algorithms.
This paper analyzes the performance of quantized min-sum decoding algorithms for irregular low-density parity-check (LDPC) codes. For regular LDPC codes, it is known that the normalized or offset min-sum decoding algorithm with quantization bits less than 6 bits achieves good performances over wide range of signal-to-noise ratios (SNR). However, finite precision effects in decoding irregular LDPC codes are different from that in decoding regular LDPC codes which is caused by the difference of convergence speeds between low degree nodes and high degree nodes. This paper proposes a novel method to improve the performance of the conventional normalized or offset min-sum decoding algorithm when it is approximated with finite precision for hardware implementations. The proposed method applies down-scaling factors to intrinsic information which has effects on increasing the reliability of extrinsic information at variable nodes and compensating the quantization errors caused by finite precision. Computer simulation results for irregular LDPC codes show that the proposed normalized and offset min-sum decoding algorithms achieve better performances at high SNR compared to the conventional normalized and offset min-sum algorithms under (6:2) quantization scheme.
In this paper we propose a very simple but powerful self-correction method for the UMP-APP decoding of LDPC codes. Like self-corrected min-sum decoding, our method does not try to correct the check node processing approximation, but it modifies the variable node processing by erasing unreliable messages. Monte-Carlo simulations show that the proposed self-corrected UMP-APP decoding performs better then common UMP-APP decoding. Estimation of the complexity and average number of iteration for self-corrected UMP-APP algorithm are also shown.
The effects of clipping and quantization on the performance of the min-sum algorithm for the decoding of low-density parity-check (LDPC) codes at short and intermediate block lengths are studied. It is shown that in many cases, only four quantization bits suffice to obtain close to ideal performance over a wide range of signal-to-noise ratios. Moreover, we propose modifications to the min-sum algorithm that improve the performance by a few tenths of a decibel with just a small increase in decoding complexity. A quantized version of these modified algorithms is also studied. It is shown that, when optimized, modified quantized min-sum algorithms perform very close to, and in some cases even slightly outperform, the ideal belief-propagation algorithm at observed error rates.