Reference : Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks

	Document type :	Scientific journals : Article
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/34185

Title :	Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks
Language :	English
Author, co-author :	Tsakmalis, Anestis [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Chatzinotas, Symeon [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Ottersten, Björn [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Publication date :	21-Dec-2017
Journal title :	IEEE Journal of Selected Topics in Signal Processing
Publisher :	Institute of Electrical and Electronics Engineers (IEEE)
Peer reviewed :	Yes (verified by ORBilu)
Audience :	International
ISSN :	1932-4553
e-ISSN :	1941-0484
City :	New York
Country :	NY
Keywords :	[en] Cognitive Radio Networks ; Expectation Propagation ; Active Learning
Abstract :	[en] In this paper, a sequential probing method for interference constraint learning is proposed to allow a centralized Cognitive Radio Network (CRN) accessing the frequency band of a Primary User (PU) in an underlay cognitive scenario with a designed PU protection specification. The main idea is that the CRN probes the PU and subsequently eavesdrops the reverse PU link to acquire the binary ACK/NACK packet. This feedback indicates whether the probing-induced interference is harmful or not and can be used to learn the PU interference constraint. The cognitive part of this sequential probing process is the selection of the power levels of the Secondary Users (SUs) which aims to learn the PU interference constraint with a minimum number of probing attempts while setting a limit on the number of harmful probing-induced interference events or equivalently of NACK packet observations over a time window. This constrained design problem is studied within the Active Learning (AL) framework and an optimal solution is derived and implemented with a sophisticated, accurate and fast Bayesian Learning method, the Expectation Propagation (EP). The performance of this solution is also demonstrated through numerical simulations and compared with modified versions of AL techniques we developed in earlier work.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Funders :	Fonds National de la Recherche - FnR
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/34185
DOI :	10.1109/JSTSP.2017.2785826
Other URL :	http://ieeexplore.ieee.org/document/7924387/
Framework Programme / European Project :	H2020 ; 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems

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