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Column Rank Distances of Rank Metric Convolutional Codes


Napp, Diego; Pinto, Raquel; Rosenthal, Joachim; Santana, Filipa (2017). Column Rank Distances of Rank Metric Convolutional Codes. In: Barbero, Ángela I; Skachek, Vitaly; Ytrehus, Øyvind. Coding Theory and Applications: 5th International Castle Meeting on Coding Theory and Applications. Cham: Springer, 248-256.

Abstract

In this paper, we deal with the so-called multi-shot network coding, meaning that the network is used several times (shots) to propagate the information. The framework we present is slightly more general than the one which can be found in the literature. We study and introduce the notion of column rank distance of rank metric convolutional codes for any given rate and finite field. Within this new framework we generalize previous results on column distances of Hamming and rank metric convolutional codes [3, 8]. This contribution can be considered as a continuation follow-up of the work presented in [10]. © 2017, Springer International Publishing AG.

Abstract

In this paper, we deal with the so-called multi-shot network coding, meaning that the network is used several times (shots) to propagate the information. The framework we present is slightly more general than the one which can be found in the literature. We study and introduce the notion of column rank distance of rank metric convolutional codes for any given rate and finite field. Within this new framework we generalize previous results on column distances of Hamming and rank metric convolutional codes [3, 8]. This contribution can be considered as a continuation follow-up of the work presented in [10]. © 2017, Springer International Publishing AG.

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Additional indexing

Item Type:Book Section, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
Dewey Decimal Classification:510 Mathematics
Language:English
Date:30 August 2017
Deposited On:22 Nov 2017 13:31
Last Modified:10 Jan 2018 12:05
Publisher:Springer
Series Name:Lecture Notes in Computer Science
Number:10495
ISSN:0302-9743
ISBN:978-3-319-66277-0
Publisher DOI:https://doi.org/10.1007/978-3-319-66278-7_21

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