A Study of the Performance of LDPC Codes under Various Decoding Algorithms and Schedules
Sangwon Chae, Hyojeong Choi, Gangsan Kim, Hong‐Yeop Song, Jongsun Ahn · 2025
Low-Density Parity-Check (LDPC) codes have been widely used in modern communication standards due to their strong error correction capabilities. This paper presents an experimental evaluation of a (1200, 600) irregular LDPC code over an additive white Gaussian noise (AWGN) channel, systematically comparing three decoding algorithms (Sum-Product, Normalized Min-Sum, and Offset Min-Sum) with three scheduling methods (Flooding, Layered Belief Propagation, and Residual Belief Propagation). Parameter sweeps are performed to determine the optimal normalization factor ($\alpha$) for the Normalized Min-Sum and the offset factor ($\beta$) for the Offset Min-Sum. A comparative analysis is then performed on the frame error rate (FER) and convergence behavior of each algorithm and scheduling configuration, considering the maximum number of iterations set at 10 and 25. The results show that the Offset Min-Sum algorithm with$\beta=0.5$and Layered Belief Propagation scheduling provides an acceptable trade-off between complexity and error-correction performance, closely matching the Sum-Product algorithm but at a lower computational cost. Although Residual Belief Propagation converges quickly at lower signal-to-noise ratios, it exhibits a high error floor. These findings provide practical guidelines for selecting optimal decoding configurations in resource-constrained applications.