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dc.contributor.authorAbdallah, Chaouki T.
dc.contributor.authorRohwer, Judd A.
dc.contributor.authorChristodoulou, Christos G.
dc.date.accessioned2012-03-29T20:19:21Z
dc.date.available2012-03-29T20:19:21Z
dc.date.issued2003-11-09
dc.identifier.citationConference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2003, 1: 207-211en_US
dc.identifier.isbn0-7803-8104-1
dc.identifier.urihttp://hdl.handle.net/1928/20272
dc.descriptionDigital Object Identifier: 10.1109/ACSSC.2003.1291898en_US
dc.description.abstractThis paper presents binary and multiclass machine learning techniques for CDMA power control. The power control commands are based on estimates of the signal and noise subspace eigenvalues and the signal subspace dimension. Results of two different sets of machine learning algorithms are presented. Binary machine learning algorithms generate fixed-step power control (FSPC) commands based on estimated eigenvalues and SIRs. A fixed-set of power control commands are generated with multiclass machine learning algorithms. The results show the limitations of a fixed-set power control system, but also show that a fixed-set system achieves comparable performance to high complexity closed-loop power control systems.en_US
dc.description.sponsorshipIEEEen_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.subjectChaosen_US
dc.subjectEigenvalues and eigenfunctionsen_US
dc.subjectMachine learningen_US
dc.titleMachine learning based CDMA power controlen_US
dc.typeArticleen_US


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