Asymptotically Good LDPC Convolutional Codes Based on Protographs
Information Theory
2016-11-15 v1 math.IT
Abstract
LDPC convolutional codes have been shown to be capable of achieving the same capacity-approaching performance as LDPC block codes with iterative message-passing decoding. In this paper, asymptotic methods are used to calculate a lower bound on the free distance for several ensembles of asymptotically good protograph-based LDPC convolutional codes. Further, we show that the free distance to constraint length ratio of the LDPC convolutional codes exceeds the minimum distance to block length ratio of corresponding LDPC block codes.
Cite
@article{arxiv.0805.0241,
title = {Asymptotically Good LDPC Convolutional Codes Based on Protographs},
author = {David G. M. Mitchell and Ali E. Pusane and Kamil Sh. Zigangirov and Daniel J. Costello,},
journal= {arXiv preprint arXiv:0805.0241},
year = {2016}
}
Comments
Proceedings of the 2008 IEEE International Symposium on Information Theory, Toronto, ON, Canada, July 6 - 11, 2008