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A Linear Programming Bound for Sum-Rank Metric Codes

A linear programming bound for sum-rank metric codes
descriptionPublicationkeyboard_double_arrow_right Article , Preprint 01 Jan 2025Embargo end date: 01 Jan 2024 Netherlands Publisher:Institute of Electrical and Electronics Engineers (IEEE)Journal:IEEE Transactions on Information Theory, volume 71, pages 317-329 (issn: 0018-9448, eissn: 1557-9654, Copyright policy )
Authors: Aida Abiad; Alexander L. Gavrilyuk; Antonina P. Khramova; Ilia Ponomarenko;

doi: 10.1109/tit.2024.3488902 , 10.48550/arxiv.2406.15926

arXiv: 2406.15926

A Linear Programming Bound for Sum-Rank Metric Codes

Abstract

We derive a linear programming bound on the maximum cardinality of error-correcting codes in the sum-rank metric. Based on computational experiments on relatively small instances, we observe that the obtained bounds outperform all previously known bounds.

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Netherlands
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Keywords

sum-rank metric graph, sum-rank metric code, eigenvalues, Information and communication theory, circuits, FOS: Mathematics, linear programming bound, Mathematics - Combinatorics, Combinatorics (math.CO), association scheme

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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