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

作     者：Gong, Yi Liu, Xingcheng Han, Guojun Wu, Bin 

作者机构：Sun Yat Sen Univ Sch Math & Computat Sci Guangzhou 510275 Guangdong Peoples R China Sun Yat Sen Univ Dept Elect & Commun Engn Guangzhou 510275 Guangdong Peoples R China Guangdong Univ Technol Sch Informat & Engn Guangzhou 510006 Guangdong Peoples R China 

出 版 物：《IET COMMUNICATIONS》 (IET Commun.)

年 卷 期：2015年第9卷第18期

页      面：2259-2266页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Natural Science Foundation of China [61301180, 61572534, 61173018, 61172076, 61471131, 11501106] Guangdong Province Key Lab of Computational Science and the Fundamental Research Funds for the Central Universities 

主　　题：belief networks parity check codes telecommunication scheduling maximum likelihood decoding convergence informed shuffled belief propagation decoding low-density parity check code sequential belief propagation BP decoding flooding BP informed dynamic scheduling BP algorithm informed dynamic location method variable node log likelihood ratio value reorder variable node SBP algorithm convergene signal-to-noise ratio 

摘      要：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 informed dynamic scheduling (IDS) BP algorithms. The authors design an informed dynamic 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 SBP algorithm 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 BP algorithms, and behaves prominently at high signal-to-noise ratios.