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International Conference on Intelligent Systems and Computer Vision (ISCV)

A thresholding algorithm for improved split-row decoding method of irregular LDPC codes

Rachid El Alami, Hassan Qjidaa, Youness Mehdaoui, Mostafa Mrabti

Published Mar 1, 2015• 3 citations
Publisher

Abstract

In this paper, we propose a thresholding algorithm to improve Split-Row decoding method of irregular LDPC codes, with maintaining the same level of complexity. We present in this paper a full description of this method and its benefits for various row weight and length code word. In this context, we have performed an implementation on MATLAB of an irregular LDPC codes with different code word and code rate. Simulation results over an additive white Gaussian channel show that the error performance of high row-weight codes with thresholding algorithm is within 0.1–0.2 dB of the normalized Min-Sum algorithm. The study result shows that the thresholding algorithm outperforms original Split Row of irregular codes.

DOI
10.1109/isacv.2015.7106166

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In this paper, we propose a thresholding algorithm to improve Split-Row decoding method of irregular LDPC codes, with maintaining the same level of complexity. We present in this paper a full description of this method and its benefits for various row weight and length code word. In this context, we have performed an implementation on MATLAB of an irregular LDPC codes with different code word and code rate. Simulation results over an additive white Gaussian channel show that the error performance of high row-weight codes with thresholding algorithm is within 0.1–0.2 dB of the normalized Min-Sum algorithm. The study result shows that the thresholding algorithm outperforms original Split Row of irregular codes.
In this paper, we have proposed a novel method of decoding algorithm of irregular LDPC codes. A reduced complexity LDPC decoding method for regular LDPC code is extended to irregular LDPC codes. We present in this paper a full description of this method and its benefits for various row weight and length code word. The Split-Row method makes column processing parallelism easier to exploit and significantly simplifies row processors. Recently, irregular LDPC codes have received a lot of attention by many advanced standard, such as WiFi, WiMAX Mobile and digital video broadcasting (DVB-S2). Hence the idea to develop the “Split-Row Method” for irregular LDPC codes. In this context, we have performed an implementation on MATLAB of an irregular LDPC codes with different code word and code rate; simulation results over an additive white Gaussian channel show that the error performance of high row-weight codes with Split-Row decoding is within 0.3–0.5 dB of the Min-Sum algorithm. The study result shows that the “Split Row Method” is better for irregular code than regular LDPC codes.
A reduced complexity LDPC decoding method for regular LDPC code is extended to irregular LDPC codes; we present in this paper a full description of this method and its benefits for various row weight and length code word. The Split-Row method makes column processing parallelism easier to exploit and significantly simplifies row processors. Simulation results over an additive white Gaussian channel show that the error performance of high row-weight codes with Split-Row decoding is within 0.3-0.5 dB of the Min-Sum algorithm.
The recently proposed split-row decoding algorithm provides significant improvements in the throughput, hardware efficiency and energy efficiency when compared to existing soft decision decoding algorithms at the cost of some error performance loss. In this paper we propose split-row threshold which outperforms the split-row algorithm while maintaining the same level of complexity. Simulation results show that the algorithm provides 0.15-0.2 dB coding gain over split-row decoding and is within 0.15-0.2 dB of SPA and MinSum normalized.
This paper presents a low complexity Min-Sum algorithm for decoding irregular Low-Density Parity-Check (LDPC) codes. In the proposed algorithm, the significant improvement in the error correcting performance without enhancing the hardware complexity is achieved by employing adaptive and optimized normalization factors for both the extrinsic information and log-likelihood ratio data bits respectively. The experimental results demonstrate that the proposed algorithm with 6-bit non-uniform quantization achieves reasonable decoding performance in terms of Frame Error Rate (FER) and Bit Error Rate (BER) without increasing the hardware complexity by avoiding finite word length effects of soft information.