Intelligent discovery of the capabilities of reconfiguration options in a cognitive wireless B3G context

Demestichas, K; Adamopoulou, E; Theologou, M

dc.contributor.author	Demestichas, K	en
dc.contributor.author	Adamopoulou, E	en
dc.contributor.author	Theologou, M	en
dc.date.accessioned	2014-03-01T01:30:56Z
dc.date.available	2014-03-01T01:30:56Z
dc.date.issued	2009	en
dc.identifier.issn	1432-7643	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19683
dc.subject	B3G wireless infrastructures	en
dc.subject	Capacity estimation	en
dc.subject	Cognitive radio	en
dc.subject	Learning and adaptation	en
dc.subject	SINR estimation	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Computer Science, Interdisciplinary Applications	en
dc.subject.other	B3G wireless infrastructures	en
dc.subject.other	Capacity estimation	en
dc.subject.other	Cognitive radio	en
dc.subject.other	Learning and adaptation	en
dc.subject.other	SINR estimation	en
dc.subject.other	Cellular radio systems	en
dc.subject.other	Channel estimation	en
dc.subject.other	Dynamic models	en
dc.subject.other	Estimation	en
dc.subject.other	Radio	en
dc.subject.other	Radio systems	en
dc.subject.other	Signal to noise ratio	en
dc.subject.other	Wireless networks	en
dc.subject.other	Wireless telecommunication systems	en
dc.subject.other	Cognitive systems	en
dc.title	Intelligent discovery of the capabilities of reconfiguration options in a cognitive wireless B3G context	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/s00500-008-0374-0	en
heal.identifier.secondary	http://dx.doi.org/10.1007/s00500-008-0374-0	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	Beyond 3G (B3G) wireless connectivity can efficiently be realized by exploiting cognitive networking concepts. Cognitive systems dynamically reconfigure the radio access technologies and the spectrum they use, based on experience, in order to adapt to the changing environment conditions. However, dynamic reconfiguration decisions call for robust discovery, i.e., radio-scene analysis and channel identification schemes. This paper intends to contribute in the areas of radio-scene analysis and channel identification: first, by providing an overview of interference estimation methods, and explaining how capacity estimations can be derived based on the measured interference levels; second, by specifying the information flow for the radio-scene analysis process of a cognitive radio system; and third, by enhancing the above with a learning system, which is essential for obtaining a truly cognitive process. The proposed approach lies in the introduction of a robust probabilistic model for optimal prediction of the capabilities of alternative configurations, in terms of capacity. © Springer-Verlag 2008.	en
heal.publisher	SPRINGER	en
heal.journalName	Soft Computing	en
dc.identifier.doi	10.1007/s00500-008-0374-0	en
dc.identifier.isi	ISI:000264874700003	en
dc.identifier.volume	13	en
dc.identifier.issue	10	en
dc.identifier.spage	945	en
dc.identifier.epage	958	en