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Conference Paper: Resource reservation in wireless networks based on pattern recognition

TitleResource reservation in wireless networks based on pattern recognition
Authors
KeywordsComputers
Computer networks
Issue Date2001
PublisherIEEE.
Citation
International Joint Conference on Neural Networks Proceedings, Washington, DC, USA, 15-19 July 2001, v. 3, p. 2264-2269 How to Cite?
AbstractResource reservation is very important for handoff control in wireless networks. Many researches have aimed to predict the user's destination cell based on its movement pattern for efficient resource reservation. In the future networks with small size cells, handoffs will occur more frequently and the user's movement will be more like a random process, so it is not practical to predict the accurate destination of a user. We propose a statistical strategy for resource reservation through the estimation of a user's transfer probabilities, which represent the possibilities of the user leaving the current cell and entering the neighboring cells. The resources reserved for a user in each base station are proportional to the user's transfer probabilities. A mathematical model is proposed to obtain the transfer probabilities of a user from the initial states (position, velocity and direction) through simulation of the user's movement. Neural networks are developed to predict the transfer probabilities of a user from the initial states and facilitate efficient resource reservation.
Persistent Identifierhttp://hdl.handle.net/10722/46589
ISSN

 

DC FieldValueLanguage
dc.contributor.authorYu, WWHen_HK
dc.contributor.authorHe, Cen_HK
dc.date.accessioned2007-10-30T06:53:34Z-
dc.date.available2007-10-30T06:53:34Z-
dc.date.issued2001en_HK
dc.identifier.citationInternational Joint Conference on Neural Networks Proceedings, Washington, DC, USA, 15-19 July 2001, v. 3, p. 2264-2269en_HK
dc.identifier.issn1098-7576en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46589-
dc.description.abstractResource reservation is very important for handoff control in wireless networks. Many researches have aimed to predict the user's destination cell based on its movement pattern for efficient resource reservation. In the future networks with small size cells, handoffs will occur more frequently and the user's movement will be more like a random process, so it is not practical to predict the accurate destination of a user. We propose a statistical strategy for resource reservation through the estimation of a user's transfer probabilities, which represent the possibilities of the user leaving the current cell and entering the neighboring cells. The resources reserved for a user in each base station are proportional to the user's transfer probabilities. A mathematical model is proposed to obtain the transfer probabilities of a user from the initial states (position, velocity and direction) through simulation of the user's movement. Neural networks are developed to predict the transfer probabilities of a user from the initial states and facilitate efficient resource reservation.en_HK
dc.format.extent577211 bytes-
dc.format.extent3380 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_HK
dc.subjectComputersen_HK
dc.subjectComputer networksen_HK
dc.titleResource reservation in wireless networks based on pattern recognitionen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1098-7576&volume=3&spage=2264&epage=2269&date=2001&atitle=Resource+reservation+in+wireless+networks+based+on+pattern+recognitionen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/IJCNN.2001.938519en_HK
dc.identifier.hkuros63139-

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