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Location awareness in cognitive radio networks

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Title:
Location awareness in cognitive radio networks
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English
Creator:
Celebi, Hasari
Publisher:
University of South Florida
Place of Publication:
Tampa, Fla
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Subjects / Keywords:
Cognitive positioning systems
Dynamic spectrum access
Dispersed spectrum utilization
Environment awareness
Location sensing
Dissertations, Academic -- Electrical Engineering -- Doctoral -- USF   ( lcsh )
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non-fiction   ( marcgt )

Notes

Summary:
ABSTRACT: Cognitive radio is a recent novel approach for the realization of intelligent and sophisticated wireless systems. Although the research and development on cognitive radio is still in the stage of infancy, there are significant interests and efforts towards realization of cognitive radio. Cognitive radio systems are envisioned to support context awareness and related systems. The context can be spectrum, environment, location, waveform, power and other radio resources. Significant amount of the studies related to cognitive radio in the literature focuses on the spectrum awareness since it is one of the most crucial features of cognitive radio systems. However, the rest of the features of cognitive radio such as location and environment awareness have not been investigated thoroughly. For instance, location aware systems are widespread and the demand for more advanced ones are growing.Therefore, the main objective of this dissertation is to develop an underlying location awareness architecture for cognitive radio systems, which is described as location awareness engine, in order to support goal driven and autonomous location aware systems. A cognitive radio conceptual model with location awareness engine and cycle is developed by inspiring from the location awareness features of human being and bat echolocation systems. Additionally, the functionalities of the engine are identified and presented. Upon providing the functionalities of location awareness engine, the focus is given to the development of cognitive positioning systems. Furthermore, range accuracy adaptation, which is a cognitive behavior of bats, is developed for cognitive positioning systems. In what follows, two main approaches are investigated in order to improve the performance of range accuracy adaptation method.The first approach is based on idea of improving the spectrum availability through hybrid underlay and overlay dynamic spectrum access method. On the other hand, the second approach emphasizes on spectrum utilization, where we study performance of range accuracy adaptation from both theoretical and practical perspectives considering whole spectrum utilization approach. Furthermore, we introduced a new spectrum utilization technique that is referred as dispersed spectrum utilization. The performance analysis of dispersed spectrum utilization approach is studied considering time delay estimation problem in cognitive positioning systems. Afterward, the performance of whole and dispersed spectrum utilization approaches are compared in the context of cognitive positioning systems.Finally, some representative advanced location aware systems for cognitive radio networks are presented in order to demonstrate some potential applications of the proposed location awareness engine in cognitive radio systems.
Thesis:
Dissertation (Ph.D.)--University of South Florida, 2008.
Bibliography:
Includes bibliographical references.
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Statement of Responsibility:
by Hasari Celebi.
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Title from PDF of title page.
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Document formatted into pages; contains 121 pages.
General Note:
Includes vita.

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oclc - 319535169
usfldc doi - E14-SFE0002562
usfldc handle - e14.2562
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PAGE 15

Figure1.1Illustrationoflocationandenvironmentawarenessinbatecholocationsystem. Figure1.2Illustrationoflocationandenvironmentawarenessinhumanbeingusingeyesandears(Humanheadimagebycourtesyof[1]).1.2LocationandEnvironmentAwarenessinWirelessSystemsLocationandenvironmentawarenessfeaturescanbeintroducedtoelectronicsystems,andsuchapproacheshavebeeninvestigatedextensivelyforbiologicallyinspiredrobotics[8].However,itisdiculttosaythisforwirelesssystems.Utilizationoflocationinformationinwirelesssystems3

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Figure1.4Simpliedblockdiagramforacognitiveradiosystem.6

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Figure2.1Aconceptualmodelforcognitiveradiosystemswithlocationandenvironmentawarenesscyclesandengines.12

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Figure2.2Blockdiagramoflocationawarenessengineforcognitiveradiosandnetworks.2.2.2.1LocationSensingMethodsOneofthefundamentalfeaturesofthelocationawarenessengineistoestimatethelocationinformationoftargetobjectinagivenformat.Theformatoflocationinformation(e.g.datumanddimension)thatneedstobesensedcanhavesignicanteectsonthecomplexityoflocationawarealgorithms[9].Therefore,ataxonomyoflocationinformationforlocationawarenessincognitiveradiosispresentedinthissection.TheproposedtaxonomyisillustratedinFig.2.3.Inthisstudy,theplaceoccupiedbyadesignateduser,deviceormainlyobjectisdescribedaslocation.Theobjectcanbephysicalorvirtualandconsequentlylocationinformationoftheobject16

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Figure2.4Aconceptualmodelforenvironmentawarenessengine.25

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22ZT0[r(t)s(t)]2dt;(3.2)32

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2;(3.5)I=E 2;(3.6)where=2=2,~Eand^Earedenedas~E=ZT0[s0(t)]2dt;(3.7)^E=ZT0s0(t)s(t)dt:(3.8)FromtherstrowandcolumnelementoftheinverseFIM,i.e.I111,theCRLBforunbiasedtimedelayestimatorscanbeobtainedasCRLB=1

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Figure3.1Blockdiagramofcognitiveradiotransceiverforrangeaccuracyadaptation.3.2.4MainErrorSourcesAnyarbitrarilyrangeaccuracyisaccomplishedpreciselyusing(3.15)iftheparametershaveinnitevalues.However,inpractice,thevalueoftheparametersandthecorrespondingresources36

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v;(3.20)wherev=3108m/sisthespeedoflight,htxandhrxaretheheightsofthetransmitterandreceiverantennasfromtheground,respectively.Inthisstudy,weassumedthathtxandhrxareequalandtypicalhtxandhrxvaluesformobiledevicesinindoorenvironmentsareassumedtobe1:5m[68].ThetotalreceivedpowerPcanbedenedas,P(~d)=Prx(~d)+I+;(3.21)whereIandrepresentthetotalinterferenceseenbythetargetCRreceiverandthermalnoise,respectively.Aftermanipulating(3.21),thefollowingequationinunknown~disobtained,~d2+do~ds

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Parameter Value Unit m m m MHz dBm dBm dBm dBm ~d m m ifUWBtechnologyisconsideredforunderlayspectrumshaping[5]maximumPtxforindooren-vironmentsisdeterminedbyFederalCommunicationsCommission(FCC)intheUnitedStatestobe41:3dBm/MHz[43].TheallocatedfrequencyrangesforUWBdevicesare3:110:6GHzand100960MHz.MaximumallowablePtxmandatedbytheregulatoryagenciesisdenotedbyPtx;max.PrxlevelismainlylimitedbythesensitivityofCRreceiver,whichisdenedastheminimumpowerlevelthatcanbedetected(Prx;min).Itisreportedthatacceptableminimumsignallevelatthemobiledevicesistypically90dBm[68].ThefollowingratioisdenedasmaximumdynamicrangeofCRtransceiver,max=Ptx;max

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Figure4.1Illustrationoftimeofarrival(TOA)rangingrelatedchannelstatistics.4.2.3.1StatisticsofAmplitudeandPhaseoftheLeadingEdgeandPeakSamplesThesestatisticsarehelpfulforsettingathresholdtodetectLESandfortheidenticationofline-of-sight(LOS)andnon-line-of-sight(NLOS)environments.Theaccuracyoftherangingalgorithmcanbeimprovedwiththeknowledgeofthesestatisticssincetheleadingedgedetectionthresholdandsearchbackwindowlength[80](orthesearchstoppingrule[77])canbedeterminedmoreaccurately.Thephasestatisticscanbeusefulforcoherentrangingsystems.TheenergyoftheLES(Ele)andPS(Ep)aretwoimportantparametersthataectrangingaccuracy.Inthissection,wewillanalyzethemeanenergyoftheLES(

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1.12 CM2(ResidentialIndoorNLOS) 1.53 CM3(OceIndoorLOS) 0.03 CM4(OceIndoorNLOS) 0.71 CM5(OutdoorLOS) 0.12 CM6(OutdoorNLOS) 0.13 CM7(IndustrialLOS) -1.103 CM8(IndustrialNLOS) -1.427 Figure4.2Theeectsofabsolutebandwidth(topgure)andcenterfrequency(bottomgure)onthestandarddeviationofthedistanceestimationerrorinlogscale.60

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ChannelModels CIR EB EBWF CM1(ResidentialIndoorLOS) 59 61 55 CM2(ResidentialIndoorNLOS) 83 87 91 CM3(OceIndoorLOS) 42 34 53 CM4(OceIndoorNLOS) 38 38 44 CM5(OutdoorLOS) 127 91 99 CM6(OutdoorNLOS) 380 342 380 CM7(IndustrialLOS) 27 23 46 CM8(IndustrialNLOS) 228 154 213 CM9(OpenOutdoorNLOS) 228 222 228 Table4.3Thevaluesof Channel CIR EB 164103 73:6103 131103 52103 114103 76103 181103 782105 398103

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Figure4.7Numberofclusterspriortothepeaksample()andnumberofdelaysbetweenclustersthatarepriortothepeaksample()statisticsfornon-lineofsight(NLOS)environments.65

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22iZT0ri(t)iej!itsi(t)2dt;(5.2)wherecisaconstantthatisindependentof.Then,themaximumlikelihood(ML)estimateforcanbeobtainedfrom(5.2)as2^ML=argmax(KXi=11

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42PKi=1SNRi2i;(5.26)whereSNRi=Nijdij2jij2Epi

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Figure5.6p

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Figure6.2Illustrationofdispersedspectrumutilizationincognitiveradiosystems.ofdispersedspectrumutilizationsystems,acombiningtechniquebasedonmaximizingSNRcrite-rionisproposed.Cramer-Raolowerbound(CRLB)overAWGNchannelforbothapproachesarepresented.Furthermore,theperformanceofbothapproachesarecomparedforboththeoreticalandpracticalscenariosconsideringmaximumlikelihood(ML)timeofarrival(TOA)estimator.6.2SystemandSignalModelTheCRtransceiverarchitectureshowninFig.6.3isconsidered.Inthisarchitecture,cognitiveenginealongwithlocationawarenessenginewishtosatisfygoaldrivenrangeaccuracyrequire-ments.Inthischapter,rangeinformationisestimatedusingtimeofarrival(TOA)method.Inthetransmitterside,cognitiveengineprovidestransmissionparametersforachievingrangeaccuracy80

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Figure6.3Blockdiagramofcognitiveradiotransceiverforwholeanddispersedspectrumutilization.6.2.1CognitiveRadioTransceiverforWholeSpectrumUtilizationForthewholespectrumutilizationsystems,thesignalisprocessedoverasinglebranch(i.e.K=1)usingtheRFfront-endshowninFig.6.4.Thebasebandtransmitsignals(t)withabsolutebandwidthofBwhinwholespectrumutilizationmethodoccupiesawholebandasshowninFig.6.1.Thebasebandsignals(t)isupconvertedtothecarrierfrequencyfc;wh,amplied,lteredandthen81

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Figure6.4Blockdiagramofcognitiveradiotransceiverforthewholespectrumutilization.6.2.2CognitiveRadioTransceiverforDispersedSpectrumUtilizationInthisarchitecture,thesamebasebandsignals(t)aspreviouscaseistransmittedoverthedispersedspectrumoccupyingKavailablebandsillustratedinFig.6.2.VariousRFtransceivercanbedevelopedfortheimplementationofdispersedspectrumutilizationmethod.OnewaytoimplementthisapproachisusingtheCRtransceiverillustratedinFig.6.5,whichisconsideredinthischapter.Similartowholespectrumutilizationcase,weassumethatthereisnoanyimpairmentsinthetransceiverforthesakeofsimplicity.Inthetransmitterside,thetransmitbasebandsignalatithbranchsi(t)isupconvertedtothecorrespondingcarrierfrequencyfciandthenampliedandlteredout.TheRFsignalateachbranchiscombinedandtransmittedthroughasingleantennaasshowninFig.6.5.Inthereceiverside,thereceivesignalissplitintoKbranchesandthesignalineachbandisprocessedbythecorrespondingbranch.Inotherwords,eachbranchlters,amplies,anddownconvertsthesignaltothebaseband.Thebasebandrepresentationofreceivesignalatithbranchri(t)isgivenbyri(t)=isi(t)+ni(t);(6.2)82

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Figure6.5Blockdiagramofcognitiveradiotransceiverforthedispersedspectrumutilization.6.3CRLBforWholeandDispersedSpectrumUtilizationSystemsInthissection,approximateCRLBoftimedelayestimationforbothwholeanddispersedspec-trumutilizationcasesarepresented.Let=[]representthevectorofunknownsignalparameters,whereandiareassumedtobeknownforwholeanddispersedspectrumutilizationsystems,respectively.Theobservationinterval[0;T]isconsidered.SincetheapproximateCRLBforthewholespectrumutilizationapproachconsideringAWGNchannelisderivedinChapter3,weonlyderiveapproximateCRLBforthedispersedspectrumutilizationapproachoverAWGNchannelinthissection.Forthesakeofconvenienceandcomparison,were-writetheapproximateCRLBoftimedelayestimatesforthewholespectrumutilizationoverAWGNchannelhereinthissection,whichisgivenbyCRLBwh=1

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Figure6.6Energycombiningtechniquefordispersedspectrumutilizationsystems.6.5ResultsandDiscussionsInthissection,performanceofbothexactandapproximateCRLBiscomparedconsideringwholespectrumutilizationsystems.Thisisfollowedbyperformancecomparisonofbothwholeanddispersedspectrumutilizationapproachesfortheoreticalandpracticalcases.86

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Figure6.7ComparisonofexactandapproximateCRLBforwholespectrumutilizationsystems.Performanceofwholeanddispersedspectrumutilizationsystemsarecomparedconsideringap-proximateCRLBandMLTOAestimator.Performancecomparisonofbothapproachesarecon-ductedforthreecases.Thenumberofavailabledispersedbandwidthforallthreecasesisconsideredtobe2,i.e.K=2.Incase1,SNR1+SNR2=SNRwh,Bi=1MHzandBwh=2MHz.Forcase2,SNRi=SNRwh,Bi=Bwh=2MHz.Finally,theparametersforcase3aregivenasfollows:SNR1+SNR2=SNRwh,Bi=B=2MHz.Theremainingsystemparametersforallthreecasesarecommonandtheyaregivenasfollows.Trainingdatadl=1isconsidered,wherethenumberofobservationsymbolsforbothwholeanddispersedspectrumis1,i.e.Ni=N=1.Inaddition,itisassumedthatthespectraldensityofthenoiseisthesameforthewholespectrumandalltheKbranchesofthedispersedspectrumtechniques;i.e.,i=fori=1;:::;K.TheGaussiansecondorderderivativepulseshapeisused.Therefore,thepulseshapeforthewholeanddispersedspectrummethodsaregeneratedusingTp=2:5wh,wherewh=1=2:5Bwh,andTpi=2:5i,wherei=1=2:5Bi,respectively.Forthepracticalcases,MLTOAestimatorisemployed.ThecombiningtechniqueshowninFig.6.6isusedfordispersedspectrumutilizationsystems.Furthermore,theroot88

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Figure6.10RMSEofMLTOAestimatorandp

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Figure7.3Aconceptualmodelforcooperativelocationawarenessbetweentwocognitiveradios.cognitiveradar[21]orcognitivesonartechniques.Ontheotherhand,passiveselflocationaware-nessmethodsobserveandacquirethesignals(e.g.opticoracoustic)fromtheenvironmentwithouttransmittinganysignal.Basically,thisapproachrequiresonlyreceptorsuchasimagesensors.Hu-manvisionandhearingaretwonaturalexamplesofpassiveselflocationawarenesstechnique.Suchcapabilitiescanbeembodiedintocognitiveradiosusingaforementionedlocationsensingmethods.96

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Figure7.5Aconceptualmodelforselflocationawareness:a)active,b)passive.Bothcooperativeandselflocationawarenessmethodshavesomestrengthsandweaknesses.Forinstance,cooperativemethodshavecapabilitytoprovideabsoluteandrelativerangingandpositioninginformationwhereastheselftechniquescanprovideonlyrelativeranginginformation.97

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ApplicationType Applications Location-basedServices(LBS) Location-assistedNetworkOptimization Location-assistedTransceiverOptimization Location-assistedEnvironmentIdentication standardthatallowsunlicenseduserstoutilizethebandsallocatedtoTVbroadcastservicesinanon-interferingfashion.ItisworthtomentionthatlowfrequencyanalogTVbands(54862MHz)hassomeattractivefeaturesforwirelessbroadbandservicessuchasachievinglong-distancetrans-99

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CR1 CR2 UserType Unlicensed Licensed Waveform WLANAP WiMAXMS Locationestimationmethod GPS GPS Datum WGS-84 WGS-84 Dateformat mmddyyyy mmddyyyy Date 12012006 12012006 Timesource GPS GPS Localtimeformat hhmmssAM/PMZone hhmmssAM/PMZone Localtime 092307AMUSEST 092307AMUSEST Dimensionaccuracyunit meter meter Altitudeunit meter meter Frequencyunit MHz MHz Longitude 822434W 822434W Longitudeaccuracy 4 4 Latitude 283116N 283116N Latitudeaccuracy 3 3 Altitude 10 14 Altitudeaccuracy 3 1 CenterFrequency 2415 3475 AbsoluteBandwidth 20 10

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Area NumberofSamples Ave.RSSI(dBm) PriorityFactor A 77865 -107 1 B 65750 -105 2 Forthecoveragepredictioncase,centralcognitiveenginecanconstructgeographictablesuchasillustratedinTable7.31usinginformation(e.g.averagereceivedsignalstrengthindicator(RSSI))reportedbackfromtheCRnodes.Table7.3isconstructedbasedonthemeasurementresults.Twotargetareasareconsidered,whicharelabeledasAandB.Inthismeasurementcampaign,thevehicleequippedwithtestequipmentstocollectdatarepresentsCRnodes.RSSImetricisusedtopredictcoveragelevelinthesetwotargetareas.ThecollecteddataaretabulatedinTable7.3.Accordingtothemeasurementresults,numberofsamples(RSSIvalues)collectedintargetareaAis77865andresultingaverageRSSIis107dBm.InrealCWNscenarios,theRSSIsamplescanbereportedbysingleormultipleCRnodesexistedinthetargetareaalongwiththeirinstantaneouslocationinformation.InthetargetareaB,65750RSSIsamplesarecollectedandtheresultingaverageRSSIis105dBm.BasedontheaverageRSSIlevel,centralcognitiveenginecandeterminethatthecoverageintargetareaAispoorerthanthatoftargetareaB.Asaresult,centralcognitiveenginecangiveprioritytothetargetareaintermsofimprovingthecoverage.HavingnetworkplanandexpansioncapabilityinCWNscomeswithanadditionaloverhead.Hence,suchcapabilityofCWNscanbeactivatedwheneverthereisaneed.Alternatively,CRnodescanstoretherequiredinformationintheirmemoriesandthecentralcognitiveenginecancollecttheinformationperiodically.

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Figure7.6Testphone(MS2)inthededicatedmode:a)signalquality,b)signalstrength,c)handoverpattern.Inthesecondpartoftheexperiment,theperformanceoflocation-assistedhandoveralgorithmisobserved.Sincethetestphonedoesnothavethelocationestimationcapability,theperformanceoflocation-assistedhandovermechanismisobtainedusingapriorilocationinformation.Basically,wehavetheinformationabouttheboundariesofactualcell,inwhichthepartofthehighwaythatisusedduringtheexperimentresides.Thisaprioriinformationisusedtoeliminatetheneedforthelocationsensingdevice.Asaresult,thetestphone(MS1)islockedtotheactualcellpriorto104

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Figure7.7Testphone(MS1)inthelockedmode:a)signalquality,b)signalstrength.

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(I)IaI1aaITa:(B.1)From(5.5),(5.6)and(5.9),(B.1)canbeshowntobeequaltotheCRLBexpressionin(5.18)and(5.19).121


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Location awareness in cognitive radio networks
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Dissertation (Ph.D.)--University of South Florida, 2008.
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Text (Electronic dissertation) in PDF format.
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ABSTRACT: Cognitive radio is a recent novel approach for the realization of intelligent and sophisticated wireless systems. Although the research and development on cognitive radio is still in the stage of infancy, there are significant interests and efforts towards realization of cognitive radio. Cognitive radio systems are envisioned to support context awareness and related systems. The context can be spectrum, environment, location, waveform, power and other radio resources. Significant amount of the studies related to cognitive radio in the literature focuses on the spectrum awareness since it is one of the most crucial features of cognitive radio systems. However, the rest of the features of cognitive radio such as location and environment awareness have not been investigated thoroughly. For instance, location aware systems are widespread and the demand for more advanced ones are growing.Therefore, the main objective of this dissertation is to develop an underlying location awareness architecture for cognitive radio systems, which is described as location awareness engine, in order to support goal driven and autonomous location aware systems. A cognitive radio conceptual model with location awareness engine and cycle is developed by inspiring from the location awareness features of human being and bat echolocation systems. Additionally, the functionalities of the engine are identified and presented. Upon providing the functionalities of location awareness engine, the focus is given to the development of cognitive positioning systems. Furthermore, range accuracy adaptation, which is a cognitive behavior of bats, is developed for cognitive positioning systems. In what follows, two main approaches are investigated in order to improve the performance of range accuracy adaptation method.The first approach is based on idea of improving the spectrum availability through hybrid underlay and overlay dynamic spectrum access method. On the other hand, the second approach emphasizes on spectrum utilization, where we study performance of range accuracy adaptation from both theoretical and practical perspectives considering whole spectrum utilization approach. Furthermore, we introduced a new spectrum utilization technique that is referred as dispersed spectrum utilization. The performance analysis of dispersed spectrum utilization approach is studied considering time delay estimation problem in cognitive positioning systems. Afterward, the performance of whole and dispersed spectrum utilization approaches are compared in the context of cognitive positioning systems.Finally, some representative advanced location aware systems for cognitive radio networks are presented in order to demonstrate some potential applications of the proposed location awareness engine in cognitive radio systems.
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Cognitive positioning systems
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Dispersed spectrum utilization
Environment awareness
Location sensing
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