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Machine learning based CDMA power control

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/20272

Machine learning based CDMA power control

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dc.contributor.author Abdallah, Chaouki T.
dc.contributor.author Rohwer, Judd A.
dc.contributor.author Christodoulou, Christos G.
dc.date.accessioned 2012-03-29T20:19:21Z
dc.date.available 2012-03-29T20:19:21Z
dc.date.issued 2003-11-09
dc.identifier.citation Conference Record of the Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, 2003, 1: 207-211 en_US
dc.identifier.isbn 0-7803-8104-1
dc.identifier.uri http://hdl.handle.net/1928/20272
dc.description Digital Object Identifier: 10.1109/ACSSC.2003.1291898 en_US
dc.description.abstract This 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.sponsorship IEEE en_US
dc.language.iso en_US en_US
dc.publisher IEEE en_US
dc.subject Chaos en_US
dc.subject Eigenvalues and eigenfunctions en_US
dc.subject Machine learning en_US
dc.title Machine learning based CDMA power control en_US
dc.type Article en_US


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