东南大学研究生英语雅思写作下学期大作业Word文件下载.docx

东南大学研究生英语雅思写作下学期大作业Word文件下载.docx

《东南大学研究生英语雅思写作下学期大作业Word文件下载.docx》由会员分享，可在线阅读，更多相关《东南大学研究生英语雅思写作下学期大作业Word文件下载.docx（6页珍藏版）》请在冰点文库上搜索。

Weconsiderahierarchicalnetworkthatconsistsofmobileusers,atwo-tieredcellularnetwork（namelysmallcellsandmacrocells）andcentralrouters,eachofwhichfollowsaPoissonpointprocess（PPP）.Inthisscenario,smallcellswithlimited-capacitybackhaulareabletocachecontentunderagivensetofrandomizedcachingpoliciesandstorageconstraints.Moreover,weconsiderthreedifferentcontentpopularitymodels,namelyfixedcontentpopularity,distance-dependentandload-dependent,inordertomodelthespatio-temporalbehaviorofusers’contentrequestpatterns.Wederiveexpressionsfortheaveragedelayofusersassumingperfectknowledgeofcontentpopularitydistributionsandrandomizedcachingpolicies.Althoughthetrendoftheaveragedelayforallthreecontentpopularitymodelsisessentiallyidentical,ourresultsshowthattheoverallperformanceofcached-enabledheterogeneousnetworkscanbesubstantiallyimproved,especiallyundertheloaddependentcontentpopularitymodel.

Besides,Becauseofthelimitationofresearchconditions,thetotalnetworkdelay,networkcostandoptimizationofnetworkparametersarenotanalyzed.

Keywords:

edgecaching,Poissonpointprocess,stochasticgeometry,mobilewirelessnetworks,5G

Introduction

Itisknownthatcontentcachingin5Gheterogeneouswirelessnetworksimprovesthesystemperformance,andisofhighimportanceinlimited-backhaulscenarios.Mostexistingliteraturefocusesonthecharacterizationofkeyperformancemetricsneglectingthebackhaullimitationsandthespatio-temporalcontentpopularityprofiles.Inthiswork,weanalyzethegainsofcachinginheterogeneousnetworkdeployment,andconsidertheaveragedelayasaperformancemetric.

Firstlyweusepassionpointprocess（PPP）methodtobuildthemodel.Thisheterogeneousnetworkconsistsofmobileterminals（users）,cache-enabledsmallbasestations（SBSs）,macrobasestations（MBSs）andcentralrouters.Inthisnetworksetting,ausermayexperiencedelaysduetodownlinktransmissions,backhaulandcaches.

Moreover,inordertocapturethespatio-temporalcontentaccesspatternsofusers,wesupposefixedcontentpopularity,distance-dependentandload-dependentcontentpopularities.Assumingthatthecontentpopularitydistributionisperfectlyknownatthesmallbasestations,weexplorethreedifferentcachingpoliciesbasedoncontent-popularityandrandomization.Andfinally,wedrawourconclusion.

Methodology

First,ourResearchobjectiveistogettheaveragedelayinacache-enabledtwo-tieredcellularnetworkmodeledbystochasticgeometry,thefigureoneisanillustrationoftheconsideredsystemmodel,themethodologypartincludingthefollowingparts.

Thefirstisthetopology,thecentralrouters,MBSs,SBSs,USERsaremodeledbytheindependentPPPmodels,thesecondisthesignalmodel,forthetractabilityfortheproblem,weassumethatthetypicaluserexperiencetherayleighfadingandthestandardpowerlawpathloss,alsoweassumethatthenetworkisinterference-limited,thatistosaytheinterferencepowerdominatesoverthenoisepower.

Thenitisthecachingmodel,whenauserhasacontentrequest,weassumethattherequestisdrawnfromacontinuouszipfdistribution,inthefunction,thereisacontentpopularityparameter,basedonthisparameter,wehavethreecachingmodels,Fixedmeansthatthecontentpopularityisidenticalforallusers,allSBSsobservethesamedistribution,Distance-dependentmeansthateachuserhasadistance-dependentsteepnessfactor,thefactorisrelatedtotheaveragedistancebetweentheSBSanditsusers.

Basedonthecachingmodels,hereweconsiderthreecachingpolicies:

StdPopmeanstheSBSscachethemostpopularcontentfromthecatalogue,theUnirandmeansthecontentsarecacheduniformlyatrandom,theMixPoppolicymeansthatpartstoragecachethemostpopularcontents,theotherpartcachethecontentatrandom.

Thelastpartisthedelay,ourmetricinthispaper,thedelayincludingthefollowingthreeparts,WhentheBSsdeliverthecontentstotheirintendedmobileusers,itincursthedownlinkdelay,WhenthecontentrequestedarenotcachedintheBSs,theBSshavetogetthecontentformthecentralroutersthroughthebackhaul,thenitcausesdelay,whenthecontentrequestedinthecaches,thecontentneedtobetakenoutfromthecaches,itcausesdelay.

ReportingandDiscussingResults

Inthissection,wenumericallyvalidateourapproximationsderivedintheprevioussection.Theimpactofcriticalsystemparametersarediscussedasfollows.

Fig.（a）ImpactofMBSdensity.（b）Impactofsmallcelldensity.

（c）ImpactoftargetSIR.（d）Impactofstoragesize.

ImpactofMBSdensityλmc:

ThechangeofaveragedelaywithrespecttotheMBSdensityisgiveninFig.a.Therein,asthenumberofMBSsincreases,weobserveanincrementinaveragedelayatmillisecondlevel.ThisismainlyduetothebackhaulasthedelayinbackhaulisproportionaltothedistanceandaveragenumberofconnectedMBSs.Ontheotherhand,theaveragedelayinSBSsremainsstaticinthissetup.However,wenotethattheaveragedelayexperiencedbyatypicalsmallcelluserisreducedbyaddingcachingcapabilitiesatthebasestations.

Impactofsmallcelldensityλsc:

ThechangeoftheaveragedelaywithrespecttothesmallcelldensityisdepictedinFig.b.SimilarlytothepreviousfigureforMBSdensity,weseethattheaveragedelayincreasesforallkindofsmallcellusers.However,inthisnumericalsetup,therateofincrementindelaywithno-cachingcapabilitiesattheSBSsishigherthanthedelayexperiencedbythetypicaluserswithcache-enabledSBSs.Comparedtothefixedandload-dependentcontentpopularities,thetypicaluserunderload-dependentcontentpopularityexperienceslessdelaywhenthenumberofSBSsincreases.

ImpactoftargetSIRγ:

Inoursetup,yetanotherimportantdesignparameteristhetargetSIR.Inthisregard,theaveragedelayvariationwithrespecttothetargetSIRisillustratedinFig.2c.Asobservedinthefigure,theaveragedelayincreasesbyimposinghighertargetSIRvalues.ThischangeisonlyvisibleinlowvaluesoftargetSIR,whereasthevariationofdelayinhighervaluesoftargetSIRisnegligible.Thismightstemfromthefactthatthedownlinkdelayisnotadominatingfactorinourscenariocomparedtothebackhauldelay.Atypicaluserconnectedtothesmallcellwithnocachingcapabilitiesexperiencesthehighestdelay,whereastheminimumdelayisachievedbyusingMixPoppolicyunderload-dependentcontentpopularity.ThedelayofatypicalMUremainsbetweenaSUwithno-cachingandcachingcapabilitiesatthebasestations.

ImpactofstoragesizeS:

Yetanothercrucialdesignparameterinoursetupisthestoragesize.TheimpactofstoragesizeontheaveragedelayisshowninFig.2d.Indeed,asobservedfromthefigure,dramaticaldecreaseindelayisobservedbyincreasingthestoragesizeofsmallbasestations.Similarlytopreviousobservations,themostsensitivecontentpopularityfortheaveragedelayistheload-dependentcontentpopularity.

Conclusion

Inthiswork,wehavecharacterizedtheaveragedelayofmacrocellusersandsmallcellusersunderbackhaulconstraintsandcachingcapabilitiesatthesmallbasestations.Weconsideramulti-tierheterogeneousnetworkinthetwo-dimensionalEuclideanplaneandatypicalmobileuser,andeachbasestationperfectlyobservesthecontentpopularitiesaccordingtothreedifferentmodels:

fixedmodels,distance-dependentmodelsandload-dependentmodels.Afterthat,somecachingpoliciessuchasstdPoppolicies,unirandpoliciesandmixpoppolicieshavebeenconsidered.Finallytheimpactofcriticalsystemparameterswhichincludemacrobasestationsdensity,smallcelldensity,targetofsignaltointerferenceratioandstoragesizeontheaveragedelayarediscussed.Althoughthetrendoftheaveragedelayforallthreecontentpopularitymodelsisessentiallyidentical,ourresultsshowthattheoverallperformanceofcached-enabledheterogeneousnetworkscanbesubstantiallyimproved,especiallyundertheloaddependentcontentpopularitymodel.Themainconclusionfromthisworkisthatcachingatthesmallbasestationsallowsforbalancingtheaverageaccessdelaytothecontents,especiallyifheterogeneousnetworkdensificationunderlimitedbackhaulisconsidere

Reference

[1]E.Ba¸

stu˘g,M.Bennis,andM.Debbah,“LivingontheEdge:

Theroleofproactivecachingin5Gwirelessnetworks,”IEEECommunicationsMagazine,vol.52,no.8,pp.82–89,August2014.

[2]Z.Chen,J.Lee,T.Q.Quek,andM.Kountouris,“Cooperativecachingandtransmissiondesignincluster-centricsmallcellnetworks,”arXivpreprintarXiv:

1601.00321,2016.

[3]M.Afshang,H.S.Dhillon,andP.H.J.Chong,“Modelingandperformanceanalysisofclustereddevice-to-devicenetworks,”arXivpreprintarXiv:

1508.02668,2015.

[4]B.SerbetciandJ.Goseling,“Onoptimalgeographicalcachinginheterogeneouscellularnetworks,”arXivpreprintarXiv:

1601.07322,2016.

[5]S.Yan,M.Peng,andW.Wang,“Useraccessmodeselectioninfogcomputingbasedradioaccessnetworks,”arXivpreprintarXiv:

1602.00766,2016.

[6]B.BlaszczyszynandA.Giovanidis,“Optimalgeographiccachingincellularnetworks,”inIEEEInternationalConferenceonCommunications（ICC）,June2015,pp.3358–3363.