CODING SCHEME IN NETWORKS USING QUASI CYCLIC LOW DENSITY PARITY CHECK CODES GENERATION WITH SVM

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.

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Publication Details

Journal
ICTACT Journal on Data Science and Machine Learning
Published
2024-09-01
DOI
https://doi.org/10.21917/ijdsml.2024.0144
Primary Topic
Error Correcting Code Techniques
Type
article
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article

CODING SCHEME IN NETWORKS USING QUASI CYCLIC LOW DENSITY PARITY CHECK CODES GENERATION WITH SVM

Nalliyanna Goundar Veerappan Kousik, Jayaraman Subramaniam
ICTACT Journal on Data Science and Machine Learning
Error Correcting Code Techniques
article

CODING SCHEME IN NETWORKS USING QUASI CYCLIC LOW DENSITY PARITY CHECK CODES GENERATION WITH SVM

Nalliyanna Goundar Veerappan Kousik, Jayaraman Subramaniam
article en

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.

ICTACT Journal on Data Science and Machine LearningVol. 5(4)
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Openalex Percentile: Top 64%
Error Correcting Code Techniques
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CODING SCHEME IN NETWORKS USING QUASI CYCLIC LOW DENSITY PARITY CHECK CODES GENERATION WITH SVM — Nalliyanna Goundar Veerappan Kousik, Jayaraman Subramaniam · ICTACT Journal on Data Science and Machine Learning (2024) | TGRS Research Map | TGRS