Enhancing cognitive radio systems with robust reasoning

Adamopoulou, E; Demestichas, K; Demestichas, P; Theologou, M

dc.contributor.author	Adamopoulou, E	en
dc.contributor.author	Demestichas, K	en
dc.contributor.author	Demestichas, P	en
dc.contributor.author	Theologou, M	en
dc.date.accessioned	2014-03-01T01:28:16Z
dc.date.available	2014-03-01T01:28:16Z
dc.date.issued	2008	en
dc.identifier.issn	1074-5351	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18789
dc.subject	Bayesian networks	en
dc.subject	Cognitive radio	en
dc.subject	Interference sensing	en
dc.subject	Machine learning	en
dc.subject	Reasoning	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.classification	Telecommunications	en
dc.subject.other	Algorithms	en
dc.subject.other	Bayesian networks	en
dc.subject.other	Cognitive systems	en
dc.subject.other	Learning systems	en
dc.subject.other	Problem solving	en
dc.subject.other	Robust control	en
dc.subject.other	Interference sensing	en
dc.subject.other	Robust reasoning	en
dc.subject.other	Transmission capacity	en
dc.subject.other	Radio systems	en
dc.title	Enhancing cognitive radio systems with robust reasoning	en
heal.type	journalArticle	en
heal.identifier.primary	10.1002/dac.898	en
heal.identifier.secondary	http://dx.doi.org/10.1002/dac.898	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	Cognitive radio systems dynamically reconfigure the algorithms and parameters they use, in order to adapt to the changing environment conditions. However, reaching proper reconfiguration decisions presupposes a way of knowing, with high enough assurance, the capabilities of the alternate configurations, especially in terms of achievable transmission capacity and coverage. The present paper addresses this problem, firstly, by specifying a complete process for extracting estimations of the capabilities of candidate configurations, in terms of transmission capacity and coverage, and, secondly, by enhancing these estimations with the employment of a machine learning technique. The technique is based on the use of Bayesian Networks, in conjunction with an effective learning and adaptation strategy, and aims at extracting and exploiting knowledge and experience, in order to reach robust (i.e. stable and reliable) estimations of the configurations' capabilities. Comprehensive results of the proposed method are presented, in order to validate its functionality. Copyright (C) 2007 John Wiley & Sons, Ltd.	en
heal.publisher	JOHN WILEY & SONS LTD	en
heal.journalName	International Journal of Communication Systems	en
dc.identifier.doi	10.1002/dac.898	en
dc.identifier.isi	ISI:000254409100005	en
dc.identifier.volume	21	en
dc.identifier.issue	3	en
dc.identifier.spage	311	en
dc.identifier.epage	330	en