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IEEE Communications Letters

Adaptive offset min-sum algorithm for low-density parity check codes

Ming Jiang, Chunming Zhao, Li Guo Zhang, Enyang Xu

Published Jun 1, 2006• 43 citations
Publisher

Abstract

An improvement is presented to the offset min-sum decoding algorithm for low-density parity check codes. The proposed algorithm introduces a more efficient adjustment for check-node update computation in view of different minimum values. According to the optimal correction factor of normalized min-sum algorithm, we can determine the adaptive offset item. The improved algorithm achieves a noticeable performance gain with only a modest increase in computation complexity.

DOI
10.1109/lcomm.2006.1638623

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An improvement is presented to the offset min-sum decoding algorithm for low-density parity check codes. The proposed algorithm introduces a more efficient adjustment for check-node update computation in view of different minimum values. According to the optimal correction factor of normalized min-sum algorithm, we can determine the adaptive offset item. The improved algorithm achieves a noticeable performance gain with only a modest increase in computation complexity.
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, we present an improvement on the normalized min-sum (NMS) decoding algorithm for low density parity check codes. The modified NMS (M-NMS) algorithm introduces a more efficient adjustment for check node update computation. Taking the sub-minimum value among extrinsic information from the variable nodes into consideration, we make the correction factor varies with different iterations. Results show that, compared with standard min-sum (MS) and NMS decoding algorithms, the proposed method not only offers a better performance but also achieves a lower error floor in the high SNR region.
Offset Min-Sum Algorithm (OMSA) and Normalized Min-Sum Algorithm (NMSA) are widely used in commercial LDPC decoders due to low complexity and reasonable performance. In this paper we proposes an Adaptive Layered Offset min- sum algorithm for the decoding of LDPC codes, which utilizes an adaptive offset factor to improve the accuracy of the soft information transferred during the iterative decoding process, and it provides good performance accordingly. The performed simulations showed that the layered offset min-sum decoding for LDPC codes can provide significant improvement compared to existing MS based algorithm, OMSA and NMSA.
In this study, the authors propose an improved version of the min-sum algorithm for low-density parity check code decoding, which the authors call ‘adaptive normalised BP-based’ algorithm. Their decoder provides a compromise solution between belief propagation and the min-sum algorithms by adding an exponent offset to each variable node's intrinsic information in the check node update equation. The extrinsic information from the min-sum decoder is then adjusted by applying a negative power of the two scale factor, which can be easily implemented by right shifting the min-sum extrinsic information. The difference between their approach and other adaptive normalised min-sum decoders is that the authors select the normalisation scale factor using a clear analytical approach based on the underlying principles. The simulation results show that the proposed decoder outperforms the min-sum decoder and performs very close to the BP decoder with lower complexity.
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.