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Constructing error-correcting codes with huge distances

Hug, Florian LU (2009) Partnership for Advanced Computing in Europe (PRACE) code porting workshop (invited talk)
Abstract
The class of error-correcting convolutional codes is commonly used for reliable data transmission in mobile, satellite, and space-communication. Demanding simultaneously larger capacities and smaller error probabilities, convolutional codes with large free distances are needed. Such convolutional codes are in general characterized by large overall constraint lengths, increasing the complexity of determining the corresponding code properties, such as the free distance.



The BEAST – Bidirectional Effcient Algorithm for Searching Trees – will be presented as an alternative, less complex, approach to determine the free distance of convolutional codes. As an example a rate R = 5/20 hypergraph-based woven convolutional code... (More)
The class of error-correcting convolutional codes is commonly used for reliable data transmission in mobile, satellite, and space-communication. Demanding simultaneously larger capacities and smaller error probabilities, convolutional codes with large free distances are needed. Such convolutional codes are in general characterized by large overall constraint lengths, increasing the complexity of determining the corresponding code properties, such as the free distance.



The BEAST – Bidirectional Effcient Algorithm for Searching Trees – will be presented as an alternative, less complex, approach to determine the free distance of convolutional codes. As an example a rate R = 5/20 hypergraph-based woven convolutional code with overall constraint length 67 and constituent convolutional codes is presented. Even though using BEAST, determining the free distance of such a convolutional code is a challenge. Using parallel processing and a common huge storage, it was possible to determine the this convolutional code has free distance 120, which is remarkably large. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to conference
publication status
published
subject
conference name
Partnership for Advanced Computing in Europe (PRACE) code porting workshop (invited talk)
conference dates
2009-10-13 - 2009-10-14
language
English
LU publication?
yes
id
56d9dc24-5e51-4695-b454-63a9eb816523 (old id 1540319)
date added to LUP
2016-04-04 14:18:00
date last changed
2018-11-21 21:19:31
@misc{56d9dc24-5e51-4695-b454-63a9eb816523,
  abstract     = {The class of error-correcting convolutional codes is commonly used for reliable data transmission in mobile, satellite, and space-communication. Demanding simultaneously larger capacities and smaller error probabilities, convolutional codes with large free distances are needed. Such convolutional codes are in general characterized by large overall constraint lengths, increasing the complexity of determining the corresponding code properties, such as the free distance.<br/><br>
<br/><br>
The BEAST – Bidirectional Effcient Algorithm for Searching Trees – will be presented as an alternative, less complex, approach to determine the free distance of convolutional codes. As an example a rate R = 5/20 hypergraph-based woven convolutional code with overall constraint length 67 and constituent convolutional codes is presented. Even though using BEAST, determining the free distance of such a convolutional code is a challenge. Using parallel processing and a common huge storage, it was possible to determine the this convolutional code has free distance 120, which is remarkably large.},
  author       = {Hug, Florian},
  language     = {eng},
  title        = {Constructing error-correcting codes with huge distances},
  year         = {2009},
}