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dc.contributor.authorAbdul-Latif, Omar M.en_US
dc.contributor.authorDaba, Jihad S.en_US
dc.description.abstractSupport vector machine (SVM) is a statistical learning tool developed to a more complex concept of structural risk minimization. SVM is playing an increasing role in applications to detection problems in statistical signal processing and communication systems. In this paper, SVM is applied to the detection of root-mean-square-gain combining (RMSGC) diversity signals in single-input-multiple-output (SIMO) systems, in the presence of channel noise characterized as partially developed Rician multipath fading and interference noise generalized as additive color Gaussian noise (ACGN). The structure and performance of SVM in terms of the bit error rate (BER) metric is analysed for these advanced stochastic noise models. The performance of SVM is then compared to conventional SIMO signaling with optimal model-based detection. We show that the SVM performance is superior to that of the maximum likelihood detector for all the selected pre-detection diversity gain combining schemes.en_US
dc.format.extent4 p.en_US
dc.subjectSupport Vector Machine (SVM)en_US
dc.subjectError statisticsen_US
dc.subjectFading channelsen_US
dc.subjectGaussian noiseen_US
dc.subjectLearning (artificial intelligence)en_US
dc.subjectStatistical analysisen_US
dc.subject.lcshSignal processingen_US
dc.titleLS-SVM detector for RMSGC diversity in SIMO channelsen_US
dc.typeConference Paperen_US
dc.relation.conferenceInternational Symposium on Signal Processing and Its Applications (9th : 12-15 Feb. 2007 : Sharjah, United Arab Emirates)en_US
dc.contributor.affiliationDepartment of Electrical Engineeringen_US
dc.relation.ispartoftextSignal Processing and Its Applications, 2007. ISSPA 2007en_US
Appears in Collections:Department of Electrical Engineering
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