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Min-zone selection decoding algorithm for LDPC codes

Zhu Jin-kang

Published Jan 1, 2008• 0 citations
OpenAlex

Abstract

An iterative decoding algorithm for low-density parity-check(LDPC) codes,named min-zone selection(MZS) decoding algorithm,was proposed.The methods of min-zone selecting and approximation were combined in the proposed decoding algorithm in order to deal with the Q(x) function involved in the traditional belief propagation(BP) decoding algorithm.Simulation results show shat the computations in the decoding process of LDPC codes incurred only a little performance degradation.In fact,it is observed that,with proper selection of parameters,the MZS decoding algorithm can provide performance comparable to that of the BP decoding algorithm.

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An iterative decoding algorithm for low-density parity-check(LDPC) codes,named min-zone selection(MZS) decoding algorithm,was proposed.The methods of min-zone selecting and approximation were combined in the proposed decoding algorithm in order to deal with the Q(x) function involved in the traditional belief propagation(BP) decoding algorithm.Simulation results show shat the computations in the decoding process of LDPC codes incurred only a little performance degradation.In fact,it is observed that,with proper selection of parameters,the MZS decoding algorithm can provide performance comparable to that of the BP decoding algorithm.
Reduced-Complexity Decoding of LDPC Codes Various log-likelihood-ratio-based belief-propagation (LLR- BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representation of the decoding computations are shown to achieve a reduction in complexity, by simplifying the check-node update or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from a performance, latency, computational complexity, and memory-requirement perspective.
To improve the decoding performance of Low Density Parity Check(LDPC) code,an efficient decoding algorithm based on min-sum algorithm is proposed. The proposed algorithm analyzes the check node update equation and at the view of probability to form an approximation min-sum equation. It adopts a dynamic normalization factor to improve the decoding performance. Detailed simulation results and comparisons with Belief Propagation(BP) algorithm show that,with little decoding performance loss,the proposed algorithm not only decreases computational load,but also improves decoding efficiency.
The near-capacity performance and effective decoding algorithms of Low-Density Parity-Check (LDPC) codes have made them a mainstay of contemporary error correction. With an emphasis on iterative techniques like the Belief Propagation (BP) algorithm, the Min-Sum algorithm, and their variations, this paper offers a thorough analysis of decoding algorithms for LDPC codes. We investigate the trade-offs between various algorithmic techniques’ computational complexity, decoding latency, and error correction performance. Particular focus is placed on new methods and hardware-friendly algorithms that are tailored for next-generation communication systems, such 5 G and beyond.
Various log-likelihood-ratio-based belief-propagation (LLR-BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representations of the decoding computations are shown to achieve a reduction in complexity by simplifying the check-node update, or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from performance, latency, computational-complexity, and memory-requirement perspectives.
In this paper a modified Offset Min-Sum decoding algorithm for Low-Density Parity Check Codes is presented. In this modified algorithm, the offset factor in the original Offset Min-Sum algorithm is adjusted iteratively with the assistance of the check-node computation outputs of the Normalized Min-Sum algorithm. As a result, the offset factor in our modified algorithm can be calculated adaptively and more efficiently according to the previous decoding procedure, which can minimize the decoding degradation compared with the Belief Propagation decoding algorithm. The simulation results show that, compared with the original Offset Min-Sum decoding algorithm, our modified algorithm can achieve noticeable performance improvement with minor extra hardware complexity. For example, when BER is 10−5, our algorithm can achieve 0.1dB and 0.2dB decoding gain over Offset Min-Sum algorithm for regular and irregular LDPC codes respectively.
In this paper, the decoding of low-density parity-check (LDPC) codes is considered. A new algorithm, named λ−\lambda-λ−Min algorithm, for updating extrinsic information is proposed. The λ−\lambda-λ−Min algorithm offers different trade-off performance versus complexity between the belief propagation (BP) algorithm (optimal but complex) and the universal most powerful (UMP) BP-based algorithm (simple but leading to significant performance degradation in some cases). Hardware implementation of the λ−\lambda-λ−Min algorithm in a serial mode is also discussed. Reduction up to 75 \% of the extrinsic memory information can be obtained with high rate LDPC code without any significant degradation of the performance.