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International Conference on Future Computer and Communication

Adaptive-normalized min-sum algorithm

Xiaofu Wu, Yue Song, Long Cui, Ming Jiang, Chunming Zhao

Published Jan 1, 2010• 11 citations
Publisher

Abstract

An adaptive-normalized min-sum (AN-MS) algorithm for decoding low-density parity-check (LDPC) codes is proposed. Unlike the normalized min-sum (NMS) algorithm, the normalization factor is adaptively adjusted according to the state of check nodes in each iteration. Simulation results show that the proposed AN-MS algorithm can perform better than the NMS algorithm while still preserving the low complexity of the min-sum algorithm.

DOI
10.1109/icfcc.2010.5497569

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An adaptive-normalized min-sum (AN-MS) algorithm for decoding low-density parity-check (LDPC) codes is proposed. Unlike the normalized min-sum (NMS) algorithm, the normalization factor is adaptively adjusted according to the state of check nodes in each iteration. Simulation results show that the proposed AN-MS algorithm can perform better than the NMS algorithm while still preserving the low complexity of the min-sum algorithm.
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.
This paper proposes an adaptive normalized min-sum algorithm for the decoding of low-density parity check (LDPC) codes, which utilizes an adaptive normalization factor to improve the accuracy of the soft information transferred during the iterative decoding process, and provides superior performance accordingly. This adaptive normalization factor can be adjusted dynamically and adaptively at each decoding iteration according to a look-up table obtained via training and simulation. Its implementation facility and independence from the channel characteristics make the proposed adaptive normalized min-sum algorithm expect a wide application. Simulation results show that the proposed algorithm can achieve performance much closer to the sum-product algorithm and a coding gain of around 0.2dB compared to the conventional normalized min-sum algorithm for DVB-S2's rate 2/5 and 3/5 LDPC codes over the additive white Gaussian noise (AWGN) channel.
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.