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Open AccessArticle

Codeword Structure Analysis for LDPC Convolutional Codes

by Hua Zhou 1,2,*, Jiao Feng 1, Peng Li 1,2 and Jingming Xia 1
1
Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science and Technology, Ningliu Road No. 219, Nanjing 210044, China
2
Jiangsu Collaborative Innovation Centre on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Ningliu Road No. 219, Nanjing 210044, China
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Information 2015, 6(4), 866-879; https://doi.org/10.3390/info6040866
Received: 16 September 2015 / Revised: 19 November 2015 / Accepted: 8 December 2015 / Published: 14 December 2015
The codewords of a low-density parity-check (LDPC) convolutional code (LDPC-CC) are characterised into structured and non-structured. The number of the structured codewords is dominated by the size of the polynomial syndrome former matrix H T ( D ) , while the number of the non-structured ones depends on the particular monomials or polynomials in H T ( D ) . By evaluating the relationship of the codewords between the mother code and its super codes, the low weight non-structured codewords in the super codes can be eliminated by appropriately choosing the monomials or polynomials in H T ( D ) , resulting in improved distance spectrum of the mother code. View Full-Text
Keywords: low-density parity-check convolutional codes (LDPC-CCs); structured codewords; non-structured codewords; distance spectrum low-density parity-check convolutional codes (LDPC-CCs); structured codewords; non-structured codewords; distance spectrum
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Zhou, H.; Feng, J.; Li, P.; Xia, J. Codeword Structure Analysis for LDPC Convolutional Codes. Information 2015, 6, 866-879.

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