Generalized LDPC Codes with Convolutional Code Constraints

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

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.

Details

Authors
Organisations
External organisations
  • Ericsson AB
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Other Electrical Engineering, Electronic Engineering, Information Engineering
Original languageEnglish
Title of host publication2020 IEEE International Symposium on Information Theory (ISIT)
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages479-484
Number of pages6
ISBN (Electronic)978-1-7281-6432-8
ISBN (Print)978-1-7281-6433-5
Publication statusPublished - 2020 Aug 24
Publication categoryResearch
Peer-reviewedYes
Event2020 IEEE International Symposium on Information Theory, ISIT 2020 - Los Angeles, CA, United States
Duration: 2020 Jun 212020 Jun 26

Conference

Conference2020 IEEE International Symposium on Information Theory, ISIT 2020
Abbreviated titleISIT 2020
CountryUnited States
CityLos Angeles, CA
Period2020/06/212020/06/26