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Generalized LDPC Codes with Convolutional Code Constraints

Farooq, M. U. LU ; Moloudi, S. and Lentmaier, M. LU (2020) 2020 IEEE International Symposium on Information Theory, ISIT 2020 p.479-484
Abstract
Braided convolutional codes (BCCs) are a class of spatially coupled turbo-like codes that can be described by a (2), (3)-regular compact graph. In this paper, we introduce a family of (d v , d c )-regular GLDPC codes with convolutional code constraints (CC-GLDPC codes), which form an extension of classical BCCs to arbitrary regular graphs. In order to characterize the performance in the waterfall and error floor regions, we perform an analysis of the density evolution thresholds as well as the finite-length ensemble weight enumerators and minimum distances of the ensembles. In particular, we consider various ensembles of overall rate R = 1/3 and R = 1/2 and study the trade-off between variable node degree and strength of the component... (More)
Braided convolutional codes (BCCs) are a class of spatially coupled turbo-like codes that can be described by a (2), (3)-regular compact graph. In this paper, we introduce a family of (d v , d c )-regular GLDPC codes with convolutional code constraints (CC-GLDPC codes), which form an extension of classical BCCs to arbitrary regular graphs. In order to characterize the performance in the waterfall and error floor regions, we perform an analysis of the density evolution thresholds as well as the finite-length ensemble weight enumerators and minimum distances of the ensembles. In particular, we consider various ensembles of overall rate R = 1/3 and R = 1/2 and study the trade-off between variable node degree and strength of the component codes. We also compare the results to corresponding classical LDPC codes with equal degrees and rates. It is observed that for the considered LDPC codes with variable node degree d v > 2, we can find a CC-GLDPC code with smaller d v that offers similar or better performance in terms of BP and MAP thresholds at the expense of a negligible loss in the minimum distance. (Less)
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author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2020 IEEE International Symposium on Information Theory (ISIT)
pages
6 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2020 IEEE International Symposium on Information Theory, ISIT 2020
conference location
Los Angeles, CA, United States
conference dates
2020-06-21 - 2020-06-26
external identifiers
  • scopus:85090403387
ISBN
978-1-7281-6432-8
978-1-7281-6433-5
DOI
10.1109/ISIT44484.2020.9174017
language
English
LU publication?
yes
id
01dcb0fe-35aa-4775-adc4-4a2f7252d0c8
date added to LUP
2020-10-04 15:29:57
date last changed
2024-06-12 21:07:51
@inproceedings{01dcb0fe-35aa-4775-adc4-4a2f7252d0c8,
  abstract     = {{Braided convolutional codes (BCCs) are a class of spatially coupled turbo-like codes that can be described by a (2), (3)-regular compact graph. In this paper, we introduce a family of (d v , d c )-regular GLDPC codes with convolutional code constraints (CC-GLDPC codes), which form an extension of classical BCCs to arbitrary regular graphs. In order to characterize the performance in the waterfall and error floor regions, we perform an analysis of the density evolution thresholds as well as the finite-length ensemble weight enumerators and minimum distances of the ensembles. In particular, we consider various ensembles of overall rate R = 1/3 and R = 1/2 and study the trade-off between variable node degree and strength of the component codes. We also compare the results to corresponding classical LDPC codes with equal degrees and rates. It is observed that for the considered LDPC codes with variable node degree d v > 2, we can find a CC-GLDPC code with smaller d v that offers similar or better performance in terms of BP and MAP thresholds at the expense of a negligible loss in the minimum distance.}},
  author       = {{Farooq, M. U. and Moloudi, S. and Lentmaier, M.}},
  booktitle    = {{2020 IEEE International Symposium on Information Theory (ISIT)}},
  isbn         = {{978-1-7281-6432-8}},
  language     = {{eng}},
  month        = {{08}},
  pages        = {{479--484}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Generalized LDPC Codes with Convolutional Code Constraints}},
  url          = {{http://dx.doi.org/10.1109/ISIT44484.2020.9174017}},
  doi          = {{10.1109/ISIT44484.2020.9174017}},
  year         = {{2020}},
}