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IEEE International Symposium on Information Theory (ISIT)

Towards Understanding Weighted Bit-Flipping Decoding

Xiaofu Wu, Cong Ling, Ming Jiang, Enyang Xu, Chunming Zhao, Xiaohu You

Published Jun 1, 2007• 6 citations
Publisher

Abstract

A natural relationship between weighted bit-flipping (WBF) decoding and message-passing decoding is explored. This understanding can help us develop a dual WBF decoding algorithm from one type of message-passing decoding algorithm and vice versa. For min-sum decoding, one can find that its dual WBF algorithm is the algorithm proposed by Jiang et al. For belief-propagation (BP) decoding, we propose a new WBF algorithm and show its performance advantage. For some high-rate low-density parity-check (LDPC) codes of large row weight, it is shown that the WBF algorithm proposed by Liu and Pados performs extraordinarily well. However, its dual message- passing decoding does not work well. Furthermore, we propose a parallel implementation framework for various WBF algorithms. Compared to serial implementations, various WBF algorithms in their parallel form converge significantly faster and often perform better.

DOI
10.1109/isit.2007.4557461

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A natural relationship between weighted bit-flipping (WBF) decoding and message-passing decoding is explored. This understanding can help us develop a dual WBF decoding algorithm from one type of message-passing decoding algorithm and vice versa. For min-sum decoding, one can find that its dual WBF algorithm is the algorithm proposed by Jiang et al. For belief-propagation (BP) decoding, we propose a new WBF algorithm and show its performance advantage. For some high-rate low-density parity-check (LDPC) codes of large row weight, it is shown that the WBF algorithm proposed by Liu and Pados performs extraordinarily well. However, its dual message- passing decoding does not work well. Furthermore, we propose a parallel implementation framework for various WBF algorithms. Compared to serial implementations, various WBF algorithms in their parallel form converge significantly faster and often perform better.
A natural relationship between weighted bit-flipping (WBF) decoding and belief-propagation-like (BP-like) decoding is explored. This understanding can help us develop WBF algorithms from BP-like algorithms. For min-sum decoding, one can find that its WBF algorithm is the algorithm proposed by Jiang et al. For BP decoding, we propose a new WBF algorithm and show its performance advantage. The proposed WBF algorithms are parallelized to achieve rapid convergence. Two efficient simulation-based procedures are proposed for the optimization of the associated thresholds.
The paper presents an improvement of the weighted bit-flipping (WBF) decoding algorithm for LDPC codes proposed by Liu and Pados (2003). Simulation results show that the improved WBF algorithm provides about 0.25 dB coding gain over the Liu–Pados WBF algorithm. Also included in the paper is a simplified loop-detection algorithm, which is much easier to implement than the one proposed by Liu and Pados. The proposed WBF algorithm is particularly effective for decoding LDPC codes whose parity check matrices have large row/column weights such as finite geometry LDPC codes.
Abstract- Low Density Parity Check (LDPC) codes can be decoded in various ways, namely the Bit-Flipping (BF) algorithm, the Weighted BF algorithm (WBF) ， the Belief Propagation (BP) algorithm and so on. These algorithms provide a wide range of tradeoffs among decoding complexity, decoding speed, and error rate performance. In this paper, a novel self-reliability-based weighted bit-flipping decoding scheme for low-density parity-check codes is proposed. Improvement in performance is observed in comparison with the modified weighted bit-flipping decoding scheme, and the decoding complexity can be significantly reduced as well. Key-Words:- Low-density parity-check (LDPC) codes, decoding algorithm, bit flipping, self reliability
Recently, various decoding algorithms with Low Density Parity Check (LDPC) codes have been proposed. Most algorithms can be divided into a hard decision algorithm and a soft decision algorithm. The Weighted Bit Flipping (WBF) algorithm that is between a hard decision and a soft decision algorithms has been proposed. The Bootstrapped WBF and Modified WBF algorithms have been proposed to improve the error rate performance and decoding complexity of the WBF algorithm. In this letter, we apply the Bootstrap step to the Modified WBF algorithm. We show that the Bootstrapped modified WBF algorithm outperforms the WBF, Bootstrapped WBF, and Modified WBF algorithms. Moreover, we show that the Bootstrapped modified WBF algorithm has the lowest decoding complexity.