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CATEGORIES:Machine Learning @ CUED
SUMMARY:Subspace Codes for Adversarial Error-Correction in
Network Coding - Azadeh Khaleghi (University of T
oronto)
DTSTART;TZID=Europe/London:20100310T113000
DTEND;TZID=Europe/London:20100310T120000
UID:TALK23686AThttp://talks.cam.ac.uk
URL:http://talks.cam.ac.uk/talk/index/23686
DESCRIPTION:In the context of error control in random linear n
etwork coding\, it is useful to construct codes th
at comprise well-separated collections of subspace
s of a vector space over a finite field. \n\nThis
work concerns the construction of non-constant-dim
ension projective space codes for adversarial erro
r-correction in random linear network coding. The
metric used is the so-called injection distance in
troduced by Silva and Kschischang\, which perfectl
y reflects the adversarial nature of the channel.
\n\nA Gilbert-Varshamov-type bound for such codes
is derived and its asymptotic behavior is analyzed
. It is shown that in the limit as the ambient spa
ce dimension approaches infinity\, the Gilbert-Var
shamov bound on the size of non-constant-dimension
codes behaves similar to the Gilbert-Varshamov bo
und on the size of constant-dimension codes contai
ned within the largest Grassmannians in the projec
tive space.\n\nUsing a multi-level scheme\, new no
n-constant-dimension codes are constructed\; thes
e codes contain more codewords than comparable cod
es designed for the subspace metric. To our knowle
dge this work is the first to address the construc
tion of non-constant-dimension codes designed for
the injection metric.
LOCATION:Engineering Department\, CBL Room 438
CONTACT:Zoubin Ghahramani
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