PROJECT TITLE:

Informed shuffled belief-propagation decoding for low-density parity-check codes

ABSTRACT:

Shuffled belief propagation (SBP), as a sequential belief propagation (BP) algorithm, hastens the convergence of BP decoding, and maintains the smallest amount complexity of flooding BP. However, its performance is remarkably inferior to informed dynamic scheduling (IDS) BP algorithms. The authors design an informed dynamic location methodology, based on the residuals of variable node log-chance ratio values, to reorder variable nodes of SBP to be updated. The location methodology considerably accelerates the convergence of SBP algorithm from 2 aspects: the unstable variable node with the most important residual to be updated initial, and selecting the biggest residual locally. Simulation results show that the proposed algorithm performs nearly the same as the best performance of IDS BP algorithms, and behaves prominently at high signal-to-noise ratios.


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