Analysis of Efficient Scheduling in Layered Decoding of GLDPC Codes

📅 2026-04-24
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🤖 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.

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📝 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.
Problem

Research questions and friction points this paper is trying to address.

GLDPC codes
layered decoding
scheduling sequences
decoding efficiency
constraint nodes
Innovation

Methods, ideas, or system contributions that make the work stand out.

GLDPC codes
layered decoding
scheduling algorithm
minimum distance
constraint node update
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