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International Conference on Signal Processing and Communication Systems

An effective multibit-flipping algorithm for LDPC decoding

Jui-Hui Hung, Sau-Gee Chen

Published Dec 1, 2008• 6 citations
Publisher

Abstract

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.

DOI
10.1109/icspcs.2008.4813653

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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.
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.
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.
A new class of iterative bit flipping (BF) decoding algorithms adapted for low-density parity check (LDPC) convolutional codes is proposed. Compared with Gallager's original BF algorithm, the new BF algorithms improve both the coding gain and the error correction speed. At a bit error rate (BER) of 10 , the best of the new bit-flipping algorithms achieves, after only 6 iterations and with much simpler decoding hardware, a coding gain within 3.5 dB of that of the conventional min-sum belief propagation decoding algorithm.
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.
A novel bit-flipping (BF) algorithm with low complexity for high-throughput decoding of low-density parity-check (LDPC) codes is presented. At each iteration, a novel threshold pattern is used to determine the code bits whether to be flipped or not, and the flipping error probability is effectively decreased. Compared with the weighted BF algorithm and its modifications, the modified BF algorithm has significantly lower complexity and decoding time. Through simulations the proposed BF algorithm is shown to achieve excellent performance and fast convergence speed while maintaining significantly low complexity thus facilitating high-throughput decoding.
In order to improve the bit-flipping (BF) decoding algorithms, a weighted hard-decision based BF decoding algorithm for low-density parity-check (LDPC) codes is presented, in which the weights of variable-nodes are employed. A new metric is defined for the weight of candidate bits not only in unsatisfied parity check equations but also in satisfied ones. The connection between min-sum decoding algorithm and the proposed algorithm is presented. Simulation results show that the new decoding algorithm can provide better FER performance and faster convergence speed than the original candidate bit based bit-flipping (CBBF) algorithm.