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dc.contributor.authorGuo, Wangmei
dc.contributor.authorCai, Ning
dc.contributor.authorShi, Xiaomeng
dc.contributor.authorMedard, Muriel
dc.date.accessioned2012-10-09T15:14:43Z
dc.date.available2012-10-09T15:14:43Z
dc.date.issued2011-10
dc.date.submitted2011-07
dc.identifier.isbn978-1-4577-0594-6
dc.identifier.isbn978-1-4577-0596-0
dc.identifier.issn2157-8095
dc.identifier.urihttp://hdl.handle.net/1721.1/73679
dc.description.abstractWe propose an efficient Adaptive Random Convolutional Network Coding (ARCNC) algorithm to address the issue of field size in random network coding. ARCNC operates as a convolutional code, with the coefficients of local encoding kernels chosen randomly over a small finite field. The lengths of local encoding kernels increase with time until the global encoding kernel matrices at related sink nodes all have full rank. Instead of estimating the necessary field size a priori, ARCNC operates in a small finite field. It adapts to unknown network topologies without prior knowledge, by locally incrementing the dimensionality of the convolutional code. Because convolutional codes of different constraint lengths can coexist in different portions of the network, reductions in decoding delay and memory overheads can be achieved with ARCNC.We show through analysis that this method performs no worse than random linear network codes in general networks, and can provide significant gains in terms of average decoding delay in combination networks.en_US
dc.description.sponsorshipNational Natural Science Foundation (China) (China Scholarship Council) (Grant 60832001)en_US
dc.description.sponsorshipGeorgia Institute of Technology (Subcontract RA306-S1)en_US
dc.description.sponsorshipMassachusetts Institute of Technology. Research Laboratory of Electronics (Claude E. Shannon Research Assistantship)en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC) (Postgraduate Scholarship)en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/ISIT.2011.6033714en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleLocalized dimension growth in random network coding: A convolutional approachen_US
dc.typeArticleen_US
dc.identifier.citationGuo, Wangmei et al. “Localized Dimension Growth in Random Network Coding: A Convolutional Approach.” IEEE International Symposium on Information Theory Proceedings (ISIT), 2011. 1156–1160.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Research Laboratory of Electronicsen_US
dc.contributor.mitauthorShi, Xiaomeng
dc.contributor.mitauthorMedard, Muriel
dc.relation.journalProceedings on the IEEE International Symposium on Information Theory Proceedings (ISIT), 2011en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsGuo, Wangmei; Cai, Ning; Xiaomeng Shi, Ning; Medard, Murielen
dc.identifier.orcidhttps://orcid.org/0000-0003-4059-407X
mit.licenseOPEN_ACCESS_POLICYen_US
mit.metadata.statusComplete


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