🤖 AI Summary
This study addresses the suboptimal scheduling efficiency in layered decoding of generalized low-density parity-check (GLDPC) codes by systematically analyzing how subcode structural properties affect message-passing convergence. It reveals, for the first time, a direct relationship between constraint node scheduling priority and key subcode characteristics—namely, minimum distance, number of minimum-weight codewords, and code length. Building on this insight, the paper proposes a novel scheduling strategy that prioritizes updating constraint nodes associated with subcodes exhibiting larger minimum distance, fewer minimum-weight codewords, and shorter length. Simulation results demonstrate that the proposed algorithm significantly enhances both decoding efficiency and convergence speed for GLDPC codes.
📝 Abstract
In this study, we investigate the characteristics of scheduling sequences that enable efficient decoding of generalized low-density parity-check (GLDPC) codes under the layered message-passing algorithm. In particular, we show that scheduling sequences leading to higher decoding efficiency should prioritize the update of constraint nodes corresponding to subcodes with larger minimum distance, fewer minimum-weight codewords, and shorter code length. Based on these characteristics, we design a scheduling algorithm, which further demonstrates the effectiveness of these characteristics through simulation experiments.