Reference : Active Learning in Cognitive Radio Networks |
Dissertations and theses : Doctoral thesis | |||
Engineering, computing & technology : Electrical & electronics engineering | |||
Security, Reliability and Trust | |||
http://hdl.handle.net/10993/31999 | |||
Active Learning in Cognitive Radio Networks | |
English | |
Tsakmalis, Anestis ![]() | |
18-Jul-2017 | |
University of Luxembourg, Luxembourg | |
Docteur en Informatique | |
165 | |
Chatzinotas, Symeon ![]() | |
Ottersten, Björn ![]() | |
Perez-Neira, Ana Isabel | |
State, Radu ![]() | |
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 | |
Researchers | |
http://hdl.handle.net/10993/31999 |
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