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IEEE Communications Letters

Improvements in belief-propagation decoding based on averaging information from decoder and correction of clusters of nodes

Nedeljko Varnica, M.P.C. Fossorier

Published Dec 1, 2006• 19 citations
Publisher

Abstract

In this letter we provide several methods to improve belief-propagation (BP) decoding of low-density parity-check codes. The methods use additional processing of the decoded information during the decoding process and are grouped into two categories. The first category is useful in the waterfall performance region and is based on processing averaged information obtained from the decoder. Several choices of the feedback information utilized in additional processing are introduced. The second category is useful in the error-floor performance region and is based on fixing clusters of symbols simultaneously, again utilizing the information obtained by monitoring the decoder. We demonstrate on a code example that our methods achieve sizeable gains compared to the original BP decoder

DOI
10.1109/lcomm.2006.060444

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International Journal of Communication Systems•Feb 28, 2017•2 citations
In this letter we provide several methods to improve belief-propagation (BP) decoding of low-density parity-check codes. The methods use additional processing of the decoded information during the decoding process and are grouped into two categories. The first category is useful in the waterfall performance region and is based on processing averaged information obtained from the decoder. Several choices of the feedback information utilized in additional processing are introduced. The second category is useful in the error-floor performance region and is based on fixing clusters of symbols simultaneously, again utilizing the information obtained by monitoring the decoder. We demonstrate on a code example that our methods achieve sizeable gains compared to the original BP decoder
In this paper, we propose a method for enhancing performance of a sequential version of the belief-propagation (BP) decoding algorithm, the group shuffled BP decoding algorithm for low-density parity-check (LDPC) codes. An improved BP decoding algorithm, called the shuffled BP decoding algorithm, decodes each symbol node in serial at each iteration. To reduce the decoding delay of the shuffled BP decoding algorithm, the group shuffled BP decoding algorithm divides all symbol nodes into several groups. In contrast to the original group shuffled BP, which automatically generates groups according to symbol positions, in this paper we propose a method for grouping symbol nodes which generates groups according to the structure of a Tanner graph of the codes. The proposed method can accelerate the convergence of the group shuffled BP algorithm and obtain a lower error rate in a small number of iterations. We show by simulation results that the decoding performance of the proposed method is improved compared with those of the shuffled BP decoding algorithm and the group shuffled BP decoding algorithm.
We propose an augmented belief-propagation (BP) decoder for low-density parity-check (LDPC) codes which can be used on memoryless or intersymbol-interference (ISI) channels. The proposed method is a heuristic algorithm that eliminates a large number of pseudocodewords that can cause nonconvergence in the BP decoder. The augmented decoder is a multistage iterative decoder, where at each stage, the original channel messages on select symbol nodes are replaced by saturated messages. The key element of the proposed method is the symbol-selection process, which is based on the appropriately defined subgraphs of the code graph and/or the reliability of the information received from the channel. We demonstrate by examples that this decoder can be implemented to achieve substantial gains (compared with the standard locally operating BP decoder) for short LDPC codes decoded on both memoryless and ISI Gaussian channels. Using the Margulis code example, we also show that the augmented decoder reduces the error floors. Finally, we discuss types of BP decoding errors and relate them to the augmented BP decoder.
A novel belief propagation(BP) decoding algorithm for convolutional low-density parity-check codes is proposed.The proposed algorithm raises the efficiency of updating the variable information by applying feedback information at each decoding iteration and employs the weighting factor to reduce the error propagation caused by the cycles in the Tanner graph,thus yielding a faster convergence of the decoding.Simulation results show that an error performance better than that of the existing belief propagation algorithm can be achieved,while the 5/8 decoding delay and the computation complexity are effectively reduced.Compared with the existing BP,the proposed algorithm achieves a gain of 0.16dB at the BER of 10-6 with the same number of iterations.
Summary The simplicity of decoding is one of the most important characteristics of the low density parity check (LDPC) codes. Belief propagation (BP) decoding algorithm is a well‐known decoding algorithm for LDPC codes. Most LDPC codes with long lengths have short cycles in their Tanner graphs, which reduce the performance of the BP algorithm. In this paper, we present 2 methods to improve the BP decoding algorithm for LDPC codes. In these methods, the calculation of the variable nodes is controlled by using “multiplicative correction factor” and “additive correction factor.” These factors are obtained for 2 separate channels, namely additive white Gaussian noise (AWGN) and binary symmetric channel (BSC), as 2 functions of code and channel parameters. Moreover, we use the BP‐based method in the calculation of the check nodes, which reduces the required resources. Simulation results show the proposed algorithm has better performance and lower decoding error as compared to BP and similar methods like normalized‐BP and offset‐BP algorithms.