Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/18423
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dc.contributor.authorNapp, Diegopt
dc.contributor.authorPinto, Raquelpt
dc.contributor.authorRosenthal, Joachimpt
dc.contributor.authorSantana, Filipapt
dc.date.accessioned2017-10-02T13:59:59Z-
dc.date.available2017-10-02T13:59:59Z-
dc.date.issued2017-
dc.identifier.isbn978-3-319-66277-0pt
dc.identifier.urihttp://hdl.handle.net/10773/18423-
dc.description.abstractIn 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].pt
dc.language.isoengpt
dc.publisherSpringer International Publishingpt
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147206/PTpt
dc.rightsopenAccesspor
dc.titleColumn Rank Distances of Rank Metric Convolutional Codespt
dc.typebookPartpt
degois.publication.firstPage248pt
degois.publication.lastPage256pt
degois.publication.locationChampt
degois.publication.titleCoding Theory and Applications: 5th International Castle Meeting, ICMCTA 2017. Lecture Notes in Computer Science, vol. 10495pt
dc.identifier.doi10.1007/978-3-319-66278-7_21pt
Appears in Collections:CIDMA - Capítulo de livro
SCG - Capítulo de livro

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