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Asia-Pacific Conference on Communications (APCC)

An efficient BF LDPC decoding algorithm based on a syndrome vote scheme

Jui-Hui Hung, Sau-Gee Chen

Published Dec 1, 2008• 4 citations
OpenAlex

Abstract

This work presents a high-performance bit-flipping (BF) algorithm utilizing a proposed syndrome vote technique of a defined culprit bit set, named CVBF algorithm for LDPC decoding. It can achieve significant decoding performance improvements by about 0.9 dB over the most efficient BF algorithm, owing to the introduced an additional syndrome vote procedure after updating the flipping reliabilities of all the received bits in each iteration. Moreover, its performance is comparable to the min-sum algorithm (MSA) under the condition of same iteration number, but with much lower complexity per iteration. The proposed syndrome vote scheme only costs little overhead in hardware realization. Besides, an early termination strategy suited for the proposed algorithm is also devised to further reduce the iteration number.

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This work presents a high-performance bit-flipping (BF) algorithm utilizing a proposed syndrome vote technique of a defined culprit bit set, named CVBF algorithm for LDPC decoding. It can achieve significant decoding performance improvements by about 0.9 dB over the most efficient BF algorithm, owing to the introduced an additional syndrome vote procedure after updating the flipping reliabilities of all the received bits in each iteration. Moreover, its performance is comparable to the min-sum algorithm (MSA) under the condition of same iteration number, but with much lower complexity per iteration. The proposed syndrome vote scheme only costs little overhead in hardware realization. Besides, an early termination strategy suited for the proposed algorithm is also devised to further reduce the iteration number.
For LDPC decoding, bit-flipping (BF) algorithms are much simpler than the min-sum algorithms (MSA). However, BF algorithms have the disadvantages of poorer performances and higher iteration counts than MSA. This paper introduces the concepts of low correlation search and culprit vote to further improve the efficiency of the existing BF algorithms. High decoding performances and low iteration number are achieved by flipping those bits with low correlation as much as possible and introducing an additional syndrome vote procedure. As a result, the proposed algorithm can achieve significant decoding performance which is very close to the min-sum algorithm (MSA) but with much lower computation complexity.
For LDPC decoding, bit-flipping (BF) algorithms are much simpler than the min-sum algorithms (MSA). However, BF algorithms have the disadvantages of poorer performances and higher iteration counts than MSA. To increase the BF performances, this paper presents a novel BF algorithm, called Low-Correlation Multibit-flipping (LCMBF) algorithm, which flips more than one bit in each iteration. High performances are achieved by flipping those bits with low correlation as much as possible. As such, the chances of introducing additional error bits are greatly reduced. Overall, the proposed algorithm achieves better BER performances and requires less iteration numbers than conventional BF algorithms.
Performance of LDPC decoders at high SNR is dominated by trapping sets that induce an error floor in the performance curve. We propose a new algorithm that resolves trapping sets and lowers the error floor. The new algorithm, called Syndrome Bit Flipping (SBF), computes the sum of adjacent parity violations at each symbol node. Bits are flipped by comparing the syndrome sum against a time-varying threshold called the decoding key. SBF is compared to other bit-flipping decoders on the Binary Symmetric Channel (BSC), and is demonstrated as a post-processing step for a Noisy Gradient Descent Bit-Flipping (NGDBF) hardware decoder. We demonstrate the post-processing method for an LDPC code defined in the 802.3an standard, and find that the frame error rate is improved by at least two orders of magnitude, even as the required iterations are reduced by 33%.
The gradient descent bit-flipping (GDBF) decoding algorithm offers an attractive tradeoff between performance and complexity compared with the other bit-flipping (BF) decoding algorithms. However, the performance of GDBF decoding is seriously effected by infinite decoding loop, which always results in decoding failure when maximal iteration number is reached. To avoid the decoding loop and improve the performance, an efficient escaping scheme is proposed for the GDBF algorithm in this paper. This scheme firstly introduces syndrome weight to detect the decoding loop. If a decoding loop is detected, the escaping mechanism works, where only the bit which can decrease the syndrome weight most efficiently is selected to be flipped. In addition, the excluding set is introduced to further prevent the local maximum. Simulation results show that the GDBF algorithm can obtain about 1 dB code gain by utilizing this scheme. Compared with noise GDBF, it has 0.1dB performance loss as well as lower complexity.
An improved bit-flipping(BF) algorithm with low complexity for decoding structured low-density parity-check codes(LDPC) is proposed to improve the performance of the hard-decision BF algorithm.The hard-decision bits are divided into two groups based on the magnitudes of the received symbols,and at each iteration,the proposed algorithm can flip more than one bit with using two thresholds,and the flipping error probability is effectively decreased.The improved BF algorithm requires some real number comparisons before iterations and requires only logical operations during the iterations.Compared with the weighted bit-flipping algorithm and its modifications,the new method is with significantly lower complexity.Simulations show that the proposed algorithm is provided with very fast convergence speed and excellent performance with significantly low complexity.