Reference : Interference Constraint Active Learning with Uncertain Feedback for Cognitive Radio N...

	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/10993/32000

Title :	Interference Constraint Active Learning with Uncertain Feedback for 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 :	Jul-2017
Journal title :	IEEE Transactions on Wireless Communications
Publisher :	Institute of Electrical and Electronics Engineers
Volume :	16
Issue/season :	7
Pages :	4654-4668
Peer reviewed :	Yes (verified by ORBilu)
ISSN :	1536-1276
e-ISSN :	1558-2248
City :	New York
Country :	NY
Keywords :	[en] Cognitive radio ; Bayesian Active Learning
Abstract :	[en] In this paper, an intelligent probing method for interference constraint learning is proposed to allow a centralized cognitive radio network (CRN) to access the frequency band of a primary user (PU) in an underlay cognitive communication scenario. 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 is implicit channel state information of the PU link, indicating whether the probinginduced interference is harmful or not. The intelligence of this sequential probing process lies in the selection of the power levels of the secondary users, which aims to minimize the number of probing attempts, a clearly active learning (AL) procedure, and expectantly the overall PU QoS degradation. The enhancement introduced in this paper is that we incorporate the probability of each feedback being correct into this intelligent probing mechanism by using a multivariate Bayesian AL method. This technique is inspired by the probabilistic bisection algorithm and the deterministic cutting plane methods (CPMs). The optimality of this multivariate Bayesian AL method is proven and its effectiveness is demonstrated through numerical simulations. Computationally cheap CPM adaptations are also presented, which outperform existing AL methods.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Funders :	Fonds National de la Recherche - FnR
Name of the research project :	SeMIGod
Target :	Researchers
Permalink :	http://hdl.handle.net/10993/32000
DOI :	10.1109/TWC.2017.2701361
Other URL :	http://ieeexplore.ieee.org/abstract/document/7924387/
Framework Programme / European Project :	H2020 ; 645047 - SANSA - Shared Access Terrestrial-Satellite Backhaul Network enabled by Smart Antennas
FnR project :	FnR ; FNR5785257 > Björn Ottersten > SeMIGod > Spectrum Management And Interference Mitigation In Cognitive Radio Satellite Networks > 01/04/2014 > 31/03/2017 > 2013

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