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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampCardieri, Paulo-
dc.typeArtigopt_BR
dc.titleThroughput analysis of cognitive wireless networks with Poisson distributed nodes based on location informationpt_BR
dc.contributor.authorNardelli, Pedro H.J.-
dc.contributor.authorLima, Carlos H.M. de-
dc.contributor.authorAlves, Hirley-
dc.contributor.authorCardieri, Paulo-
dc.contributor.authorLatva-aho, Matti-
dc.subjectGeometria estocásticapt_BR
dc.subject.otherlanguageStochastic geometrypt_BR
dc.description.abstractThis paper provides a statistical characterization of the individual achievable rates in bits/s/Hz and the spatial throughput of bipolar Poisson wireless networks in bits/s/Hz/m2. We assume that all cognitive transmitters know the distance to their receiver’s closest interferers and use this side-information to autonomously tune their coding rates to avoid outage events for each spatial realization. Considering that the closest interferer approximates the aggregate interference of all transmitters treated as noise, we derive closed-form expressions for the probability density function of the achievable rates under two decoding rules: treating interference as noise, and jointly detecting the strongest interfering signals treating the others as noise. Based on these rules and the bipolar model, we approximate the expected maximum spatial throughput, showing the best performance of the latter decoding rule. These results are also compared to the reference scenario where the transmitters do not have cognitive ability, coding their messages at predetermined rates that are chosen to optimize the expected spatial throughput – regardless of particular realizations – which yields outages. We prove that, when the same decoding rule and network density are considered, the cognitive spatial throughput always outperforms the other optionpt_BR
dc.relation.ispartofAd hoc networkspt_BR
dc.publisher.cityAmsterdampt_BR
dc.publisher.countryPaíses Baixospt_BR
dc.publisherElsevierpt_BR
dc.date.issued2015-
dc.date.monthofcirculationOct.pt_BR
dc.language.isoengpt_BR
dc.description.volume33pt_BR
dc.description.firstpage1pt_BR
dc.description.lastpage15pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn1570-8705pt_BR
dc.identifier.eissn1570-8713pt_BR
dc.identifier.doi10.1016/j.adhoc.2015.04.001pt_BR
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1570870515000748pt_BR
dc.date.available2020-09-11T17:35:41Z-
dc.date.accessioned2020-09-11T17:35:41Z-
dc.description.provenanceSubmitted by Mariana Aparecida Azevedo (mary1@unicamp.br) on 2020-09-11T17:35:41Z No. of bitstreams: 0. Added 1 bitstream(s) on 2021-01-08T19:03:09Z : No. of bitstreams: 1 000362304400001.pdf: 842155 bytes, checksum: c6cc418ce6596dfc95643eb8dc8b631d (MD5)en
dc.description.provenanceMade available in DSpace on 2020-09-11T17:35:41Z (GMT). No. of bitstreams: 0 Previous issue date: 2015en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/349135-
dc.contributor.departmentDepartamento de Comunicaçõespt_BR
dc.contributor.unidadeFaculdade de Engenharia Elétrica e de Computaçãopt_BR
dc.subject.keywordCognitive networkspt_BR
dc.subject.keywordSpatial throughputpt_BR
dc.identifier.source000362304400001pt_BR
dc.creator.orcid0000-0002-7761-0240pt_BR
dc.type.formArtigopt_BR
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