Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/4958
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dc.contributor.authorDaba, Jihad S.en_US
dc.contributor.authorAbdul-Latif, Omar M.en_US
dc.date.accessioned2021-02-08T20:50:09Z-
dc.date.available2021-02-08T20:50:09Z-
dc.date.issued2020-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/4958-
dc.description.abstractSupport Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM, as a supervised machine learning tool, is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and 6G wireless communication networks. In this paper, SVM is applied to signal detection in 6G communication systems in the presence of channel noise in the form of fully developed Rayleigh multipath fading and receiver noise generalized as additive color Gaussian noise (ACGN). The structure and performance of SVM in terms of the bit error rate (BER) metric is derived and simulated for these advanced stochastic noise models and the computational complexity of the implementation, in terms of average computational time per bit, is also presented. The performance of SVM is then compared to conventional M-ary signaling optimal model-based detector driven by M-ary phase shift keying (MPSK) modulation. We show that the SVM performance is superior to that of conventional detectors which require as much as 7 bits-coding (M≥128) to produce comparable results to those of SVM. Finally, the SVM-based detector is implemented in an uplink SIMO system using both Equal Gain Combiner (EGC) technique and Root Mean Square Gain Combiner (RMSGC) technique in which the later technique will be proven to be superior to the earlier.en_US
dc.language.isoengen_US
dc.subjectLeast Square-Support Vector Machineen_US
dc.subjectMassive MIMOen_US
dc.subjectM-ary Phase Shift Keyingen_US
dc.subjectOrthogonal frequency division multiplexingen_US
dc.subjectRoot Mean Square Gain Combiningen_US
dc.subjectSingle Input Multiple Outputen_US
dc.subject6G Networksen_US
dc.titleSupervised Machine Learning Classifiers for Diversity Combined Signals in 6G Massive MIMO Receiversen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.13189/ujeee.2020.070604-
dc.description.volume7en_US
dc.description.issue6en_US
dc.description.startpage320en_US
dc.description.endpage327en_US
dc.date.catalogued2021-01-08-
dc.description.statusPublisheden_US
dc.identifier.openURLhttps://www.hrpub.org/journals/article_info.php?aid=10395en_US
dc.relation.ispartoflinkUniversal Journal of Electrical and Electronic Engineeringen_US
Appears in Collections:Department of Electrical Engineering
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