Reference : Active Learning in Cognitive Radio Networks
Dissertations and theses : Doctoral thesis
Engineering, computing & technology : Electrical & electronics engineering
Security, Reliability and Trust
Active Learning in Cognitive Radio Networks
Tsakmalis, Anestis mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
University of Luxembourg, ​​Luxembourg
Docteur en Informatique
Chatzinotas, Symeon mailto
Ottersten, Björn mailto
Perez-Neira, Ana Isabel
State, Radu mailto
Marques, Antonio G.
[en] Cognitive radio ; Bayesian Active Learning
[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.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM

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