Active Learning in Cognitive Radio Networks

Reference : Active Learning in Cognitive Radio Networks


	Document type :	Dissertations and theses : Doctoral thesis
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/31999

Title :	Active Learning in Cognitive Radio Networks
Language :	English
Author, co-author :	Tsakmalis, Anestis [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	18-Jul-2017
Institution :	University of Luxembourg, Luxembourg
Name of the degree :	Docteur en Informatique
Number of pages :	165
Supervisor :	Chatzinotas, Symeon
	Ottersten, Björn
	Perez-Neira, Ana Isabel
President of the jury :	State, Radu
Member of the jury :	Marques, Antonio G.
Keywords :	[en] Cognitive radio ; Bayesian Active Learning
Abstract :	[en] In this thesis, numerous Machine Learning (ML) applications for Cognitive Radios Networks (CRNs) are developed and presented which facilitate the e cient spectral coexistence of a legacy system, the Primary Users (PUs), and a CRN, the Secondary Users (SUs). One way to better exploit the capacity of the legacy system frequency band is to consider a coexistence scenario using underlay Cognitive Radio (CR) techniques, where SUs may transmit in the frequency band of the PU system as long as the induced to the PU interference is under a certain limit and thus does not harmfully a ect the legacy system operability.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/31999

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