|Authors||I. E. Bocharova, B. D. Kudryashov, E. Rosnes, V. Skachek and Ø. Ytrehus|
|Title||Wrap-around sliding-window near-ML decoding of binary LDPC codes over the BEC|
|Project(s)||SARDS: Secure and Reliable Distributed Storage Systems|
|Publication Type||Proceedings, refereed|
|Year of Publication||2016|
|Conference Name||2016 9th International Symposium on Turbo Codes and Iterative Information Processing (ISTC 2016)|
A novel method of low-complexity near-maximum-likelihood (ML) decoding of quasi-cyclic (QC) low-density parity-check (LDPC) codes over the binary erasure channel is presented. The idea is similar to wrap-around decoding of tail-biting convolutional codes. ML decoding is applied to a relatively short window which is cyclically shifted along the received sequence. The procedure is repeated until either all erasures have been corrected, or no new erasures are corrected at a certain round. A new upper bound on the ensemble-average ML decoding error probability for a finite-length row-regular LDPC code family is derived and presented. Furthermore, a few examples of regular and irregular QC LDPC codes are studied by simulations and their performance is compared with the ensemble-average performance. Finally, the impact of the codeword weight and stopping set size spectra on the ML and belief-propagation decoding performance is discussed.