A Markov Random Field framework for channel assignment in Cognitive Radio networks

Anifantis, E; Karyotis, V; Papavassiliou, S

dc.contributor.author	Anifantis, E	en
dc.contributor.author	Karyotis, V	en
dc.contributor.author	Papavassiliou, S	en
dc.date.accessioned	2014-03-01T02:53:32Z
dc.date.available	2014-03-01T02:53:32Z
dc.date.issued	2012	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/36397
dc.subject	Cognitive Radio networks	en
dc.subject	Markov Random Fields	en
dc.subject	Potential functions	en
dc.subject	Radio channel allocation	en
dc.subject.other	Analysis and simulation	en
dc.subject.other	Channel Assignment	en
dc.subject.other	Cognitive radio network	en
dc.subject.other	Distributed operations	en
dc.subject.other	Frequency agile	en
dc.subject.other	Gibbs sampling	en
dc.subject.other	Hierarchical coordination	en
dc.subject.other	Markov Random Fields	en
dc.subject.other	Operational requirements	en
dc.subject.other	Performance benefits	en
dc.subject.other	Potential function	en
dc.subject.other	Radio channel allocation	en
dc.subject.other	Requirements change	en
dc.subject.other	Wireless protocol	en
dc.subject.other	Cognitive radio	en
dc.subject.other	Image segmentation	en
dc.subject.other	Radio systems	en
dc.subject.other	Ubiquitous computing	en
dc.subject.other	Wireless telecommunication systems	en
dc.subject.other	Wireless networks	en
dc.title	A Markov Random Field framework for channel assignment in Cognitive Radio networks	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/PerComW.2012.6197617	en
heal.identifier.secondary	6197617	en
heal.identifier.secondary	http://dx.doi.org/10.1109/PerComW.2012.6197617	en
heal.publicationDate	2012	en
heal.abstract	In order to alleviate the absence of hierarchical coordination among secondary users of Cognitive Radio networks and to enable fast and efficient reconfiguration of pervasive ad hoc networks, spatially localized and frequency agile mechanisms are required across the wireless protocol stack. In this paper, we introduce a framework for distributed and adaptable radio channel allocation in wireless Cognitive Radio networks, which is based on the theory of Markov Random Fields and Gibbs sampling. By exchanging local only information, secondary users are able to assign efficiently available channels according to desired operational requirements, even if these requirements change over time. Through analysis and simulation we show the applicability of the framework and demonstrate its distributed operation. We study the emerging trade-offs of the proposed approach by demonstrating the performance benefits obtained in radio channel allocation and by analyzing the inherent costs. © 2012 IEEE.	en
heal.journalName	2012 IEEE International Conference on Pervasive Computing and Communications Workshops, PERCOM Workshops 2012	en
dc.identifier.doi	10.1109/PerComW.2012.6197617	en
dc.identifier.spage	770	en
dc.identifier.epage	775	en