[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.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Disciplines :
Ingénierie électrique & électronique
Auteur, co-auteur :
TSAKMALIS, Anestis ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
