Shuffled belief propagation (Sbp), as a sequential belief propagation (bp) algorithm, speeds up the convergence of bp decoding, and maintains the least complexity of flooding bp. However, its performance is remarkably...
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Shuffled belief propagation (Sbp), as a sequential belief propagation (bp) algorithm, speeds up the convergence of bp decoding, and maintains the least complexity of flooding bp. However, its performance is remarkably inferior to informeddynamicscheduling (IDS) bpalgorithms. The authors design an informeddynamic location method, based on the residuals of variable node log-likelihood ratio values, to reorder variable nodes of Sbp to be updated. The location method significantly accelerates the convergence of Sbpalgorithm from two aspects: the unstable variable node with the largest residual to be updated first, and selecting the largest residual locally. Simulation results show that the proposed algorithm performs nearly the same as the best performance of IDS bpalgorithms, and behaves prominently at high signal-to-noise ratios.
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