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The Journal of Applied Computer Science & Telecommunications

Comparative Analysis of the Error-Correction Performance of LDPC Decoding Algorithms

Zhuldyz Sailau kyzy, Murat G. Kozhanov, Г. Т. Даненова, М. Жакина, Azamat Amirov

Published May 1, 2026• 0 citations
Publisher

Abstract

The article presents a comparative study of LDPC decoding algorithms in an additive white Gaussian noise (AWGN) channel. The aim is to identify the most efficient algorithms for reliable data transmission. Simulations were carried out using the AFF3CT tool with BPSK modulation. Several decoders were analyzed, including GALA, GALB, GALE, PPBF, WBF and MWBF. Performance was evaluated by bit error rate (BER) and frame error rate (FER). The results show that the GalE decoder achieves the best error-correction performance for long codewords (length 20480), with BER values reaching 10-5 and 10-10. For shorter code lengths, MWBF and GalE algorithms also demonstrate good results, with MWBF reaching BER ≈ 10-3 at SNR = 7 dB. In resource-limited conditions, simplified bit-flipping algorithms can provide reasonable efficiency

DOI
10.52209/3107-1503_2026_1_2

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Low-Density Parity-Check (LDPC) codes are error correcting codes with good performance in communications systems. LDPC codes, however, have heavy computational complexity. In this paper, we find out how many iterations are required for sufficient performance of LDPC codes. The Additive White Gaussian Noise (AWGN) channel model and a log-domain Sum-Product (SP) algorithm are used. Experiments measure Bit Error Rate (BER) at each iteration in given SNRs. The result of performance evaluation, the best performance is showed when the number of iteration is 25 at 5 ㏈.
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 (α\alphaα) for the Normalized Min-Sum and the offset factor (β\betaβ) 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 withβ=0.5\beta=0.5β=0.5and 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.