A Self-Correcting Minimum Sum Algorithm with Dual Reliability

In order to improve the performance and reliability of Low-Density Parity-Check (LDPC) code decoding algorithms, this paper proposes a Self-Correcting Minimum Sum algorithm with Dual Reliability Determination (DRD-SCMS) to address the limitations of the Self-Correcting Minimum Sum (SCMS) algorithm in determining the reliability of variable nodes. Unlike the traditional SCMS algorithm, which relies solely on the symbols from the previous and current iterations to assess the reliability of variable nodes, the DRD-SCMS algorithm combines the number of connections between variable nodes and check nodes with check failures, along with the results of symbol iterations, to provide a more accurate reliability evaluation. By categorizing variable nodes into three groups and prioritizing the updating of the most unreliable nodes, the algorithm optimizes the iteration order in the decoding process. Simulation results demonstrate that the DRD-SCMS algorithm significantly reduces the number of iterations and improves decoding efficiency compared to the SCMS algorithm, offering better applicability and reliability.