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Signal Processing

A New LDPC decoding algorithm

Zongxin Wang

Published Jan 1, 2007• 0 citations
OpenAlex

Abstract

Low-density parity-check code (LDPC) with good application foreground has excellent performance.A news LDPC de-coding algorithm,which combined SIHO (soft-input hard-output)list-based decoding with belief propagation(BP) decoding is presen-ted.Comparing with BP and MLD algorithm in performance and complexity,the complexity of the proposed decoding is reduced apparent-ly and the performance approaches to MLD decoding.

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•Jan 1, 2021•1 citation
Low-density parity-check code (LDPC) with good application foreground has excellent performance.A news LDPC de-coding algorithm,which combined SIHO (soft-input hard-output)list-based decoding with belief propagation(BP) decoding is presen-ted.Comparing with BP and MLD algorithm in performance and complexity,the complexity of the proposed decoding is reduced apparent-ly and the performance approaches to MLD decoding.
In this paper, the decoding algorithm of low-density parity-check (LDPC) codes is analyzed, and a new decoding algorithm based on the belief propagation (BP) algorithm to eliminate the influence of cycles in the factor graph is proposed. In the traditional BP algorithm, the cycles of factor graph sends message back to its source, and this decreases the decoding performance. The new algorithm records each cycle's path and length of each node, and cuts off the path by which message is propagated when the message comes back. It can advance the decoding performance by protect the message of good quality be propagated as widely as possible. Our results of simulation show that the performance of new algorithm is not worse than that of traditional BP algorithm in the low SNR channel and the new algorithm significantly outperform traditional BP algorithm in good channel condition.
In this work, reliability based decoding is combined with belief propagation (BP) decoding for low-density parity check (LDPC) codes. At each iteration, the soft output values delivered by the BP algorithm are used as reliability values to perform reduced complexity soft decision decoding of the code considered. This approach allows to bridge the error performance gap between BP decoding which remains suboptimum, and maximum likelihood decoding (MLD) which is too complex to be implemented for the codes considered. A stopping criterion which reduces the average number of iterations at the expense of very little performance degradation is also proposed for this combined decoding approach. Simulations results for several Gallager LDPC codes and different set cyclic (DSC) codes of hundreds of information bits are given and elaborated.
In this paper, we propose a new very-low-complexity (VLC) decoding algorithm for low-density parity-check (LDPC) codes. This new algorithm only need subtract a correcting factor to improve the accuracy of the soft message values delivered by a previously proposed simplified belief-propagation (BP)-based algorithm called uniformly most powerful (UMP) algorithm. However, the VLC algorithm outperforms the UMP algorithm greatly. The correcting factor can be obtained by simulation or theoretical derivation. This new VLC algorithm is much simpler to implement than BP algorithm, and also simpler than previously proposed normalized BP-based algorithm in which the operation of dividing a normalization factor is needed. However, the performance of the VLC algorithm is slightly better than that of the normalized BP-based algorithm. Also, the VLC algorithm is universal in that the decoding is independent of the channel characteristics. Simulation results demonstrate that the performance of the new VLC algorithm can nearly approach that of BP on the additive white Gaussian noise (AWGN) channel.
In this paper,the decoding algorithm of low-density parity-check(LDPC)codes is analyzed,and a new decoding algorithm based on the belief propagation(BP)algorithm to eliminate the influence of cycles in the factor graph is proposed. In the traditional BP algorithm,the cycles of factor graph will send message back to its source,and this will decrease the decoding performance. The new algorithm records each cycle’s path and length of each node,and cuts off the path by which message is propagated when the message will come back. It can advance the decoding performance by protect the message of good quality be propagated as widely as possible. Our results of simulation show that the performance of new algorithm is not worse than that of traditional BP algorithm in the low SNR channel and the new algorithm significantly outperform traditional BP algorithm in good channel condition.
Low-Density Parity-Check (LDPC) codes are one of the most popular codes used in nowadays’ communication due to their high coding efficiency at low decoding complexity. With these characteristics, LDPC codes are suitable for high-speed information transmission systems. The widely used decoding algorithm for LDPC codes is Belief Propagation (BP) decoding with which the performance of LDPC codes can approach the Shannon limit. With the development of 5G, a lot of attention is given to the so-called ultra-reliable low latency communication scenario which needs the short code to transmit. However, short codes will not have a very good performance with BP decoding. To solve this problem, investigations have been done such as multiple-bases BP decoding. Although this decoding method has a better performance than BP decoding, it will cause a high complexity in the hardware implementation. Besides, an investigation on the stochastic decoding for shortcodes was proposed in 2003. This decoding method is hardware friendly, but its performance can only approach BP decoding. Inspired by multiple-bases BP decoding and stochastic decoding, binary stochastic decoding with parallel decoders is proposed in this thesis firstly. It represents stochastic sequences with multiple parallel Tanner graphs and uses hard-decision decoding in the iterative part because of the binary input bits. However, this decoding method has a severe performance loss compared to BP decoding. To avoid this problem, the enhancement method is used to make the binary sequences be non-binary sequences that can form more powerful parallel decoders. Then the non-binary symbols of these new sequences are transformed to their corresponding log-likelihood ratio which enables the iterative part to use BP decoding. In addition, combining with the ML decision of list decoding, the decision part of our stochastic decoder can fully utilize the output to increase the efficiency of the decoding method. After ensuring the performance of our non-binary stochastic decoding to be better than BP decoding, the complexity of the decoder is reduced to save the computational resources. Finally, with constant adjustments, the bit width of each non-binary symbol is determined to be 15, and the sequence length is reduced to 20 which can let the non-binary stochastic decoding has an acceptable complexity while keeping good performance.