Throughput prediction in wireless networks using statistical learning
Resumen:
The focus of this work is on the estimation of throughput in wireless networks, more specificaly on IEEE 802.11. Our proposal is based on active measurements and statistical learning tools. We present a methodology where the system is trained during short periods with application flows and probe packets bursts. We learn the relation between throughput obtained by the application and the state of the network, which is inferred from the interarrival times of the probe packets bursts. As a result we obtain a continuous non intrusive methodology that allows to determine the maximum throughput of a wireless connection only knowing some characteristics of the network. We use Support Vector Machines (SVM) for regression and we show results obtained by simulations.
| 2010 | |
| Telecomunicaciones | |
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/38727 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522934806282240 |
|---|---|
| author | Rattaro, Claudina |
| author2 | Belzarena, Pablo |
| author2_role | author |
| author_facet | Rattaro, Claudina Belzarena, Pablo |
| author_role | author |
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| collection | COLIBRI |
| dc.creator.none.fl_str_mv | Rattaro, Claudina Belzarena, Pablo |
| dc.date.accessioned.none.fl_str_mv | 2023-08-01T20:33:31Z |
| dc.date.available.none.fl_str_mv | 2023-08-01T20:33:31Z |
| dc.date.issued.es.fl_str_mv | 2010 |
| dc.date.submitted.es.fl_str_mv | 20230801 |
| dc.description.abstract.none.fl_txt_mv | The focus of this work is on the estimation of throughput in wireless networks, more specificaly on IEEE 802.11. Our proposal is based on active measurements and statistical learning tools. We present a methodology where the system is trained during short periods with application flows and probe packets bursts. We learn the relation between throughput obtained by the application and the state of the network, which is inferred from the interarrival times of the probe packets bursts. As a result we obtain a continuous non intrusive methodology that allows to determine the maximum throughput of a wireless connection only knowing some characteristics of the network. We use Support Vector Machines (SVM) for regression and we show results obtained by simulations. |
| dc.identifier.citation.es.fl_str_mv | Rattaro, C., Belzarena, P. Throughput prediction in wireless networks using statistical learning [Preprint] Publicado en Proceedings of the Latin-American Workshop on Dynamic Networks, Buenos Aires, Argentina, 2010. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/38727 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.other.es.fl_str_mv | Telecomunicaciones |
| dc.title.none.fl_str_mv | Throughput prediction in wireless networks using statistical learning |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/submittedVersion |
| description | The focus of this work is on the estimation of throughput in wireless networks, more specificaly on IEEE 802.11. Our proposal is based on active measurements and statistical learning tools. We present a methodology where the system is trained during short periods with application flows and probe packets bursts. We learn the relation between throughput obtained by the application and the state of the network, which is inferred from the interarrival times of the probe packets bursts. As a result we obtain a continuous non intrusive methodology that allows to determine the maximum throughput of a wireless connection only knowing some characteristics of the network. We use Support Vector Machines (SVM) for regression and we show results obtained by simulations. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | COLIBRI_a0ce083c2ce808035a6ed1cb75734f8c |
| identifier_str_mv | Rattaro, C., Belzarena, P. Throughput prediction in wireless networks using statistical learning [Preprint] Publicado en Proceedings of the Latin-American Workshop on Dynamic Networks, Buenos Aires, Argentina, 2010. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/38727 |
| publishDate | 2010 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | 2023-08-01T20:33:31Z2023-08-01T20:33:31Z201020230801Rattaro, C., Belzarena, P. Throughput prediction in wireless networks using statistical learning [Preprint] Publicado en Proceedings of the Latin-American Workshop on Dynamic Networks, Buenos Aires, Argentina, 2010.https://hdl.handle.net/20.500.12008/38727The focus of this work is on the estimation of throughput in wireless networks, more specificaly on IEEE 802.11. Our proposal is based on active measurements and statistical learning tools. We present a methodology where the system is trained during short periods with application flows and probe packets bursts. We learn the relation between throughput obtained by the application and the state of the network, which is inferred from the interarrival times of the probe packets bursts. As a result we obtain a continuous non intrusive methodology that allows to determine the maximum throughput of a wireless connection only knowing some characteristics of the network. We use Support Vector Machines (SVM) for regression and we show results obtained by simulations.Made available in DSpace on 2023-08-01T20:33:31Z (GMT). No. of bitstreams: 5 RB10.pdf: 335449 bytes, checksum: 075622e23d1685d47e8445ab5e75f99d (MD5) license_text: 21936 bytes, checksum: 9833653f73f7853880c94a6fead477b1 (MD5) license_url: 49 bytes, checksum: 4afdbb8c545fd630ea7db775da747b2f (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) license.txt: 4194 bytes, checksum: 7f2e2c17ef6585de66da58d1bfa8b5e1 (MD5) Previous issue date: 2010enengLas obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad De La República. (Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)TelecomunicacionesThroughput prediction in wireless networks using statistical learningPreprintinfo:eu-repo/semantics/preprintinfo:eu-repo/semantics/submittedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaRattaro, ClaudinaBelzarena, PabloTelecomunicacionesAnálisis de Redes, Tráfico y Estadísticas de 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- Universidad de la Repúblicafalse |
| spellingShingle | Throughput prediction in wireless networks using statistical learning Rattaro, Claudina Telecomunicaciones |
| status_str | submittedVersion |
| title | Throughput prediction in wireless networks using statistical learning |
| title_full | Throughput prediction in wireless networks using statistical learning |
| title_fullStr | Throughput prediction in wireless networks using statistical learning |
| title_full_unstemmed | Throughput prediction in wireless networks using statistical learning |
| title_short | Throughput prediction in wireless networks using statistical learning |
| title_sort | Throughput prediction in wireless networks using statistical learning |
| topic | Telecomunicaciones |
| url | https://hdl.handle.net/20.500.12008/38727 |
