Digital Communication Receivers Using Gaussian Processes for Machine Learning
Digital Communication Receivers Using Gaussian Processes for Machine Learning
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Cham: Springer International Publishing
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Language
English
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Cham: Springer International Publishing
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We propose Gaussian processes (GPs) as a novel nonlinear receiver for digital communication systems. The GPs framework can be used to solve both classification (GPC) and regression (GPR) problems. The minimum mean squared error solution is the expectation of the transmitted symbol given the information at the receiver, which is a nonlinear function...
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Full title
Digital Communication Receivers Using Gaussian Processes for Machine Learning
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TN_cdi_doaj_primary_oai_doaj_org_article_ff565118860c470c8c8d6e472d76e04b
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_ff565118860c470c8c8d6e472d76e04b
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ISSN
1687-6180,1687-6172
E-ISSN
1687-6180
DOI
10.1155/2008/491503
