A Grouping Based on Local Girths for the Group Shuffled Belief Propagation Decoding

Manada, Akiko; Yoshida, Kyohei; Morita, Hiroyuki; Tatasukawa, Ryuichi

doi:10.1109/lcomm.2016.2598571

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…In literature, Group-Shuffled [21], [24] sum-product decoding algorithms divide either check nodes or variable nodes of the relevant bipartite graph into small sub-groups called layers. Also each main iteration is broken into multiple subiterations.…”

Section: B Group Formationmentioning

confidence: 99%

“…• Between the groups, message passing is processed in serial. The variable nodes can be divided into groups of different sizes based on the local girth and edges of Tanner graph [24] which increases the bit error performance and convergence speed. The local girth of a variable node is used for the appropriate partition.…”

Section: B Group Formationmentioning

confidence: 99%

“…This method of identifying the most unreliable VNs can be explored for possible utilization in the NB-LDPC to improve performance as well as convergence speed. In literature, Group-Shuffled [21], [24] sum-product decoding algorithms divide either check nodes or variable nodes of the relevant bipartite graph into small sub-groups called layers. Also each main iteration is broken into multiple sub-iterations.…”

Section: B Group Formationmentioning

confidence: 99%

“…These partial computed information can be used for the check node update at the same k th iteration instead of previous values calculated at (k − 1) th iteration. This method provides the shuffling of the check and variable nodes and is called a group shuffle decoding [19], [24].…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Group Shuffled Symbol Flipping Decoding Algorithm

Ullah,

Cheng,

Takawira

2024

IEEE Access

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The paper introduces two decoding techniques based on grouping for symbol flipping nonbinary LDPC codes. Firstly, a new technique of adaptive grouping of variable nodes in each iteration using bit reliability and majority voting of each received symbol is presented. Grouping of variable nodes and the subsequent decoding are based on individual symbol reliability and majority voting. To form groups adaptively, the cumulative density of variable nodes is used where a high priority group is considered to contain the most unreliable variable nodes and is decoded while the second priority group contains variable nodes having a second level of reliability and so on. Secondly, a fixed grouping technique is applied to symbol flipping decoding of non-binary LDPC codes. In the fixed grouping decoding, each group contains an equal number of variable nodes, and selected symbols in each group are flipped according to pre-defined flipping criteria. Numerical results and analysis show that the proposed group-based hard decision symbol flipping decoding algorithms have the advantage of reduced computational complexity and show better performance. Therefore, the proposed algorithms can be considered for applications like data storage and mobile communication.

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Section: B Group Formationmentioning

confidence: 99%

Section: B Group Formationmentioning

confidence: 99%