• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Weighted Block-by-Block Decoding Algorithm for CPM-QC-LDPC Code Using Neural Network


Abstract

As one of the most potential types of low-density parity-check (LDPC) codes, CPM-QC-LDPC code has considerable advantages but there still exist some limitations in practical application, for example, the existing decoding algorithm has a low convergence rate and a high decoding complexity. According to the structural property of this code, we propose a new method based on a CPM-RID decoding algorithm that decodes block-by-block with weights, which are obtained by neural network training. From the simulation results, we can conclude that our proposed method not only improves the bit error rate and frame error rate performance but also increases the convergence rate, when compared with the original CPM-RID decoding algorithm and scaled MSA algorithm.


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Cite this article

[IEEE Style]
Z. Xu, J. Zhu, Z. Zhang and Q. Cheng, "A Weighted Block-by-Block Decoding Algorithm for CPM-QC-LDPC Code Using Neural Network," KSII Transactions on Internet and Information Systems, vol. 12, no. 8, pp. 3749-3768, 2018. DOI: 10.3837/tiis.2018.08.011 .

[ACM Style]
Zuohong Xu, Jiang Zhu, Zixuan Zhang, and Qian Cheng. 2018. A Weighted Block-by-Block Decoding Algorithm for CPM-QC-LDPC Code Using Neural Network. KSII Transactions on Internet and Information Systems, 12, 8, (2018), 3749-3768. DOI: 10.3837/tiis.2018.08.011 .