Abstract

Quasi-Cyclic Low-Density Parity-Check (QC-LDPC) codes are highly effective error correction codes due to their structured sparsity, providing low complexity and superior error performance. However, improving their performance with modern classification techniques remains a challenge. This study integrates Support Vector Machine (SVM) generation with QC-LDPC codes to optimize error correction efficiency. The proposed approach combines the robust coding properties of QC-LDPC codes with SVM’s decision boundary characteristics, enhancing decoding accuracy. Simulation results show that for a code length of 1024 bits, the Bit Error Rate (BER) improved by 15%, while the Frame Error Rate (FER) reduced by 10% compared to traditional QC-LDPC methods. These improvements underscore the potential of SVMs in refining error correction processes for communication systems, particularly in scenarios with high noise interference.