On Superregular Matrices and Convolutional Codes with Finite Decoder Memory

Jonas Hansen, Jan Østergaard, Johnny Kudahl, John Madsen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)

Abstract

In this paper, we present explicit code constructions for a family of (n,k,δ) convolutional codes with optimum distance profiles. The family of convolutional codes is obtained from sets of jointly superregular matrices. For the case of finite decoder memory, we evaluate the performance of the constructed codes in terms of both symbol loss probability and symbol delay. We then present a combinatorial method to calculate the exact symbol loss probability and symbol delay for each symbol individually. We compare the symbol loss probability for two specific systematic convolutional codes for the cases where the sink has infinite or finite memory. Finally, we compare the performance of our convolutional codes with optimum distance profile and random based convolutional codes.

Original languageEnglish
Title of host publication2018 IEEE 87th Vehicular Technology Conference (VTC Spring)
Number of pages5
PublisherIEEE
Publication date20 Jul 2018
Pages1-5
ISBN (Print)978-1-5386-6356-1
ISBN (Electronic)978-1-5386-6355-4
DOIs
Publication statusPublished - 20 Jul 2018
EventIEEE Vehicular Technology Conference Spring 2018 - Porto, Portugal
Duration: 3 Jun 20186 Jun 2018
http://www.ieeevtc.org/vtc2018spring/

Conference

ConferenceIEEE Vehicular Technology Conference Spring 2018
CountryPortugal
CityPorto
Period03/06/201806/06/2018
Internet address
SeriesI E E E V T S Vehicular Technology Conference. Proceedings
ISSN1550-2252

Keywords

  • Convolutional codes
  • finite memory
  • linear codes
  • superregular matrices

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