Title :
Distance spectrum estimation of LDPC convolutional codes
Author :
Hua Zhou ; Mitchell, David ; Goertz, N. ; Costello, Daniel J.
Author_Institution :
Inst. of Telecommun., Vienna Univ. of Technol., Vienna, Austria
Abstract :
Time-invariant low-density parity-check convolutional codes (LDPC-CCs) derived from corresponding quasi-cyclic (QC) LDPC block codes (LDPC-BCs) can be described by a polynomial syndrome former matrix (polynomial-domain transposed parity-check matrix). In this paper, an estimation of the distance spectrum of time-invariant LDPC-CCs is obtained by splitting the polynomial syndrome former matrix into submatrices representing “super codes” and then evaluating the linear dependence between codewords of the corresponding super codes. This estimation results in an upper bound on the minimum free distance of the original code and, additionally, a lower bound on the number of codewords Aw with Hamming weight w.
Keywords :
block codes; convolutional codes; cyclic codes; parity check codes; Hamming weight; LDPC convolutional codes; LDPC-B; distance spectrum estimation; low density parity check codes; polynomial domain transposed parity check matrix; polynomial syndrome former matrix; quasicyclic LDPC block codes; super codes; Block codes; Complexity theory; Convolutional codes; Hamming weight; Parity check codes; Polynomials; Vectors;
Conference_Titel :
Information Theory Proceedings (ISIT), 2012 IEEE International Symposium on
Conference_Location :
Cambridge, MA
Print_ISBN :
978-1-4673-2580-6
Electronic_ISBN :
2157-8095
DOI :
10.1109/ISIT.2012.6284234