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Novel Efficient Decoding Implementation of LDPC Codes

Wenbo Wang

Published Jan 1, 2007• 1 citation
OpenAlex

Abstract

As for the sum-product decoding algorithm of low density parity check(LDPC) codes,a difference-based decoding method is proposed.The main idea is shown as follows: the messages passed on the bipartite graph of LDPC are based on the probability differences,and the updates of both check nodes and message nodes are carried out on some special addition domain.As for the update of check nodes,only some lowest differences can be selected for reducing computational complexity.In contrast with the traditional log-likelyhood-ratio-based(LLR-based) decoding method,the proposed method has much lower complexity,while it has no obvious loss of performance and convergence.

Related papers

Efficient decoding implementations of LDPC codes

Wenbo Wang

As for the sum-product decoding algorithm of LDPC codes,a new kind of difference-based decoding method is proposed.The main idea about it is that the messages passed on the bipartite graph of LDPC are based on the probability differences,and the renewal of both check nodes and message nodes is carried out on the special addition domain.As for the renewal of check nodes,some differences possessing the minimum absolute values can be selected to further reduce the complexity of computation.In contrast with the traditional LLR-based decoding method,the proposed method has much lower complexity,while it has no obvious loss of performance and convergence.

Systems engineering and electronics•Jan 1, 2010•0 citations

Efficient Difference-based Decoding Implementations of LDPC Codes

Zhanji Wu, Peng Mugen, Wang Wenbo +1 more

Efficient implementations of the sum-product algorithm (SPA) for decoding low-density parity-check (LDPC) codes using difference-based messages between bit nodes and check nodes are presented. As for the updates of check nodes, reduced- complexity derivatives are also put forward. As compared with the traditional Log-Likelihood-Ratio(LLR)-based decoding implementations, the proposed method has much lower complexity and latency, while it has no obvious loss of the error performance.

IEEE Communications Letters•May 1, 2008•3 citations

Differential Decoding of Low-Density Parity-Check Codes Based on Min Sum Algorithm

Shizhang Peng, Zemao Zhao, Jianrong Bao

To improve performance and reduce computation complexity of the low-density parity-check (LDPC) codes, a min-sum based differential decoding algorithm is proposed. The main principle is that the messages passing in the bipartite graph of the codes are the probability differences and the updates of the check nodes adopt min sum algorithm. Simulation results show that the proposed algorithm can achieve about 0.4dB performance gain over the UMP BP-Based decoding algorithm at bit error rate (BER) of under an additive white Gaussian noise (AWGN) channel. And it can obtain good performance very close to that of the BP decoding algorithm with much lower complexity.

International Symposium on Information Science and Engineering•Dec 1, 2010•2 citations

Reduced-Complexity Decoding of LDPC Codes

Reduced-Complexity Decoding of LDPC Codes Various log-likelihood-ratio-based belief-propagation (LLR- BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representation of the decoding computations are shown to achieve a reduction in complexity, by simplifying the check-node update or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from a performance, latency, computational complexity, and memory-requirement perspective.

IEEE Transactions on Communications•Jul 1, 2005•27 citations

A Computationally Efficient Selective Node Updating Scheme for Decoding of LDPC Codes

E. Cavus, Babak Daneshrad

In this paper, we introduce a computationally efficient selective node update algorithm for the decoding of low-density parity check codes. Unlike the standard sum-product algorithm, where all bit and check nodes are updated at each decoding iteration, the developed algorithm only updates unreliable check and bit nodes. A simple reliability criteria is developed to determine the active bit and check nodes per decoding iteration. Based on the developed technique, significant computation reductions are achieved with very little or no loss in the BER performance of the LDPC codes. At a WER of 10-5, 91.8% and 72.7% check node and 80% and 41% bit node computation reductions are obtained for a (96, 48) and a (504, 252) LDPC code, respectively. The proposed method can be implemented with a slight modification to the standard sum-product decoding algorithm with negligible additional hardware complexity. From a hardware point of view, the proposed algorithm offers power reductions proportional to the computation savings and it leads to higher decoding speeds in serial implementations by decreasing the number of required memory accesses

IEEE Military Communications Conference (MILCOM)•Mar 22, 2006•7 citations

Reduced-Complexity Decoding of LDPC Codes

J. Chen, Ajay Dholakia, Evangelos S. Eleftheriou +2 more

Various log-likelihood-ratio-based belief-propagation (LLR-BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representations of the decoding computations are shown to achieve a reduction in complexity by simplifying the check-node update, or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from performance, latency, computational-complexity, and memory-requirement perspectives.

IEEE Transactions on Communications•Aug 1, 2005•952 citations
As for the sum-product decoding algorithm of low density parity check(LDPC) codes,a difference-based decoding method is proposed.The main idea is shown as follows: the messages passed on the bipartite graph of LDPC are based on the probability differences,and the updates of both check nodes and message nodes are carried out on some special addition domain.As for the update of check nodes,only some lowest differences can be selected for reducing computational complexity.In contrast with the traditional log-likelyhood-ratio-based(LLR-based) decoding method,the proposed method has much lower complexity,while it has no obvious loss of performance and convergence.
As for the sum-product decoding algorithm of LDPC codes,a new kind of difference-based decoding method is proposed.The main idea about it is that the messages passed on the bipartite graph of LDPC are based on the probability differences,and the renewal of both check nodes and message nodes is carried out on the special addition domain.As for the renewal of check nodes,some differences possessing the minimum absolute values can be selected to further reduce the complexity of computation.In contrast with the traditional LLR-based decoding method,the proposed method has much lower complexity,while it has no obvious loss of performance and convergence.
To improve performance and reduce computation complexity of the low-density parity-check (LDPC) codes, a min-sum based differential decoding algorithm is proposed. The main principle is that the messages passing in the bipartite graph of the codes are the probability differences and the updates of the check nodes adopt min sum algorithm. Simulation results show that the proposed algorithm can achieve about 0.4dB performance gain over the UMP BP-Based decoding algorithm at bit error rate (BER) of under an additive white Gaussian noise (AWGN) channel. And it can obtain good performance very close to that of the BP decoding algorithm with much lower complexity.
Reduced-Complexity Decoding of LDPC Codes Various log-likelihood-ratio-based belief-propagation (LLR- BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representation of the decoding computations are shown to achieve a reduction in complexity, by simplifying the check-node update or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from a performance, latency, computational complexity, and memory-requirement perspective.
In this paper, we introduce a computationally efficient selective node update algorithm for the decoding of low-density parity check codes. Unlike the standard sum-product algorithm, where all bit and check nodes are updated at each decoding iteration, the developed algorithm only updates unreliable check and bit nodes. A simple reliability criteria is developed to determine the active bit and check nodes per decoding iteration. Based on the developed technique, significant computation reductions are achieved with very little or no loss in the BER performance of the LDPC codes. At a WER of 10-5, 91.8% and 72.7% check node and 80% and 41% bit node computation reductions are obtained for a (96, 48) and a (504, 252) LDPC code, respectively. The proposed method can be implemented with a slight modification to the standard sum-product decoding algorithm with negligible additional hardware complexity. From a hardware point of view, the proposed algorithm offers power reductions proportional to the computation savings and it leads to higher decoding speeds in serial implementations by decreasing the number of required memory accesses
Various log-likelihood-ratio-based belief-propagation (LLR-BP) decoding algorithms and their reduced-complexity derivatives for low-density parity-check (LDPC) codes are presented. Numerically accurate representations of the check-node update computation used in LLR-BP decoding are described. Furthermore, approximate representations of the decoding computations are shown to achieve a reduction in complexity by simplifying the check-node update, or symbol-node update, or both. In particular, two main approaches for simplified check-node updates are presented that are based on the so-called min-sum approximation coupled with either a normalization term or an additive offset term. Density evolution is used to analyze the performance of these decoding algorithms, to determine the optimum values of the key parameters, and to evaluate finite quantization effects. Simulation results show that these reduced-complexity decoding algorithms for LDPC codes achieve a performance very close to that of the BP algorithm. The unified treatment of decoding techniques for LDPC codes presented here provides flexibility in selecting the appropriate scheme from performance, latency, computational-complexity, and memory-requirement perspectives.