Energy-Efficient Power Allocation for UAV Cognitive Radio Systems

Handle URI:
http://hdl.handle.net/10754/627307
Title:
Energy-Efficient Power Allocation for UAV Cognitive Radio Systems
Authors:
Sboui, Lokman ( 0000-0003-1134-4055 ) ; Ghazzai, Hakim; Rezki, Zouheir; Alouini, Mohamed-Slim ( 0000-0003-4827-1793 )
Abstract:
We study the deployment of unmanned aerial vehicles (UAV) based cognitive system in an area covered by the primary network (PN). An UAV shares the spectrum of the PN and aims to maximize its energy efficiency (EE) by optimizing the transmit power. We focus on the case where the UAV simultaneously communicates with the ground receiver (G), under interference limitation, and with another relaying UAV (A), with a minimal required rate. We analytically develop the power allocation framework that maximizes the EE subject to power budget, interference, and minimal rate constraints. In the numerical results, we show that the minimal rate may cause a transmission outage at low power budget values. We also highlighted the existence of optimal altitudes given the UAV location with respect to the different other terminals.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program
Citation:
Sboui L, Ghazzai H, Rezki Z, Alouini M-S (2017) Energy-Efficient Power Allocation for UAV Cognitive Radio Systems. 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall). Available: http://dx.doi.org/10.1109/vtcfall.2017.8287971.
Publisher:
IEEE
Journal:
2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)
Issue Date:
12-Feb-2018
DOI:
10.1109/vtcfall.2017.8287971
Type:
Conference Paper
Additional Links:
http://ieeexplore.ieee.org/document/8287971/
Appears in Collections:
Conference Papers; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorSboui, Lokmanen
dc.contributor.authorGhazzai, Hakimen
dc.contributor.authorRezki, Zouheiren
dc.contributor.authorAlouini, Mohamed-Slimen
dc.date.accessioned2018-03-15T06:26:39Z-
dc.date.available2018-03-15T06:26:39Z-
dc.date.issued2018-02-12en
dc.identifier.citationSboui L, Ghazzai H, Rezki Z, Alouini M-S (2017) Energy-Efficient Power Allocation for UAV Cognitive Radio Systems. 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall). Available: http://dx.doi.org/10.1109/vtcfall.2017.8287971.en
dc.identifier.doi10.1109/vtcfall.2017.8287971en
dc.identifier.urihttp://hdl.handle.net/10754/627307-
dc.description.abstractWe study the deployment of unmanned aerial vehicles (UAV) based cognitive system in an area covered by the primary network (PN). An UAV shares the spectrum of the PN and aims to maximize its energy efficiency (EE) by optimizing the transmit power. We focus on the case where the UAV simultaneously communicates with the ground receiver (G), under interference limitation, and with another relaying UAV (A), with a minimal required rate. We analytically develop the power allocation framework that maximizes the EE subject to power budget, interference, and minimal rate constraints. In the numerical results, we show that the minimal rate may cause a transmission outage at low power budget values. We also highlighted the existence of optimal altitudes given the UAV location with respect to the different other terminals.en
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/8287971/en
dc.rights(c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.en
dc.titleEnergy-Efficient Power Allocation for UAV Cognitive Radio Systemsen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.identifier.journal2017 IEEE 86th Vehicular Technology Conference (VTC-Fall)en
dc.eprint.versionPost-printen
dc.contributor.institutionQatar Mobility Innovations Center (QMIC), Qatar University, Doha, Qataren
dc.contributor.institutionDepartment of Electrical and Computer Engineering, University of Idaho, Moscow, ID 83844, USAen
kaust.authorSboui, Lokmanen
kaust.authorAlouini, Mohamed-Slimen
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