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dc.contributor.authorYang, Xen_US
dc.contributor.authorWang, Yen_US
dc.contributor.authorBigham, Jen_US
dc.contributor.authorCuthbert, Len_US
dc.contributor.authorIEEEen_US
dc.date.accessioned2011-11-02T09:43:15Z
dc.date.issued2011en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/2244
dc.description© 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.descriptionThis article was accepted for publication in Wireless Communications and Networking Conference (WCNC), 2011 IEEE, Cancun, Quintana Roo, 28-31 March 2011.
dc.descriptionkeywords: NEAT;QoS constraint;adaptive CAC scheme;adaptive connection admission control scheme;complex constraint reinforcement learning problem;learning approach;mobile network;network revenue maximization;neuroevolution of augmenting topology;noisy fitness;superiority of feasible point;wireless network;adaptive control;control engineering computing;learning (artificial intelligence);mobile radio;quality of service;telecommunication computing;telecommunication congestion control;telecommunication traffic;
dc.format.extent897 - 902en_US
dc.subjectAdaptive CACen_US
dc.subjectNEATen_US
dc.subjectnoise controlen_US
dc.subjectconstraint optimizationen_US
dc.titleAdaptive CAC Using NeuroEvolution to Maximize Throughput in Mobile Networksen_US
dc.typeConference Proceeding
dc.rights.holder© Copyright 2017 IEEE - All rights reserved
pubs.author-urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000297160200152&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6aen_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US


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