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dc.contributor.authorAlabbasi, AbdulRahman
dc.contributor.authorRezki, Zouheir
dc.contributor.authorShihada, Basem
dc.date.accessioned2015-04-09T06:46:37Z
dc.date.available2015-04-09T06:46:37Z
dc.date.issued2014-06
dc.identifier.doi10.1109/ISIT.2014.6875061
dc.identifier.urihttp://hdl.handle.net/10754/362466
dc.description.abstractIn this paper we consider a cognitive radio multi-input multi-output environment in which we adapt our beamformer to maximize both energy efficiency and signal to interference plus noise ratio (SINR) metrics. Our design considers an underlaying communication using adaptive beamforming schemes combined with the sensing information to achieve an optimal energy efficient system. The proposed schemes maximize the energy efficiency and SINR metrics subject to cognitive radio and quality of service constraints. Since the optimization of energy efficiency problem is not a convex problem, we transform it into a standard semi-definite programming (SDP) form to guarantee a global optimal solution. Analytical solution is provided for one scheme, while the other scheme is left in a standard SDP form. Selected numerical results are used to quantify the impact of the sensing information on the proposed schemes compared to the benchmark ones.
dc.publisherInstitute of Electrical & Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6875061
dc.rights© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.titleEnergy efficiency and SINR maximization beamformers for cognitive radio utilizing sensing information
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journal2014 IEEE International Symposium on Information Theory
dc.conference.date29 June 2014 through 4 July 2014
dc.conference.name2014 IEEE International Symposium on Information Theory, ISIT 2014
dc.conference.locationHonolulu, HI
dc.eprint.versionPost-print
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personRezki, Zouheir
kaust.personShihada, Basem
kaust.personAlabbasi, AbdulRahman
refterms.dateFOA2018-06-13T16:40:16Z


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