ì
1/21/16
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Departuretalk
GustavoB.Figueiredo01/15/16
ìPlanningandDimensioninginH-CRAN
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References
ì HeterogeneousCloudRadioAccessNetworks:ANewPerspec=veForEnhancingSpectralAndEnergyEfficiencies.MugenPeng,YuanLI,JiamoJIANG,JianLIandChonggangWang.IEEEWirelessCommunicaGons,December2014.
ì BalancingBackhaulLoadInHeterogeneousCloudRadioAccessNetworks.ChenRan,ShaoweiWang,andChonggangWang.IEEEWirelessCommunicaGons,June2015.
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Motivations
ì (ADSL)-likeuserexperiencewillbeprovidedinthefiVhgeneraGon(5G)wirelesssystemsì averageareacapacityof25Gb/s/km2ì 100GmeshigherthancurrentfourthgeneraGon(4G)
systemsì a1000×improvementinenergyefficiency(EE)is
anGcipatedby2020
ì CelldensificaGon,isanecessarysteptoreachsuchimprovementsì HetNet(Small,picoandfemtocells):useoflow-power
node(LPN).
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Motivations(problems…)
ì ToodenseLPNsincursevereinterference,whichrestrictsperformancegainsandcommercialdevelopmentofHetNets.ì CoMPtransmissionandrecepGonis
themostpromisingtechniquesin4Gbutithasdisadvantagesinrealnetworks
ì performancegaindependsheavilyonthebackhaulconstraintsandevendegradeswithincreasingdensityofLPNs
ì TradiGonalC-RANdeploymentsimposeextremelyhighdemandsonbackhauls…
ì …andatthecurrentdevelopmentstageofmostoperators,itwillrequirehighinvestmentstobedeployed.
IEEE Wireless Communications • December 2014 127
tion for providing high EE together with gigabitdata rates across software defined wireless com-munication networks, in which the virtualizationof communication hardware and software ele-ments place stress on communication networksand protocols. Consequently, heterogeneouscloud radio access networks (H-CRANs) areproposed in this article as a cost-effective poten-tial solution to alleviate inter-tier interferenceand improve cooperative processing gains inHetNets through combination with cloud com-puting. The motivation of H-CRANs is toenhance the capabilities of HPNs with massivemultiple antenna techniques and simplify LPNsthrough connecting to a signal processing cloudwith high-speed optical fibers. As such, the base-band data path processing as well as radioresource control for LPNs are moved to thecloud server to take advantage of cloud comput-ing capabilities. In the proposed H-CRANs, thecloud-computing-based cooperation processingand networking gains are fully exploited, theoperating expenses are lowered, and energy con-sumption of the wireless infrastructure isdecreased.
Figure 1 illustrates the evolution milestonefrom conventional 1G to the H-CRAN-based 5Gsystem. In the traditional first, second, and thirdgeneration (1G, 2G, 3G) cellular systems, coop-erative processing is not demanded becauseintercell interference can be avoided by utilizingstatic frequency planning or code-division multi-ple access (CDMA) techniques. However, forthe orthogonal frequency-division multiplexing(OFDM)-based 4G systems, the intercell inter-ference is severe because of the spectrum reusein adjacent cells, especially when a HetNet isdeployed. Therefore, intercell or inter-tier coop-erative processing through CoMP is critical in4G. To control the more and more severe inter-ference and improve SE and EE performances,cooperative communication techniques haveevolved from two-dimensional CoMP to three-dimensional large-scale cooperative processingand networking with cloud computing. There-fore, for the H-CRAN-based 5G system, cloud-
computing-based cooperative processing andnetworking techniques are proposed to tacklethe aforementioned challenges of 4G systemsand in turn meet the performance demands of5G systems.
In this article, we are motivated to make aneffort to offer a comprehensive survey of thetechnological features and core principles of H-CRANs. In particular, the system architecture ofH-CRANs is presented, and SE/EE performanceof H-CRANs is introduced. Meanwhile, thecloud-computing-based cooperative processingand networking techniques to improve SE andEE performance in H-CRANs are summarized,including advanced signal processing in the phys-ical (PHY) layer, cooperative radio resourcemanagement (RRM), and self-organization inthe upper layers. The challenging issues relatedto techniques and standards are discussed aswell.
The remainder of this article is outlined asfollows. H-CRAN architecture and performanceanalysis are introduced next. Following that, thekey promising technologies related to cloud-computing-based signal processing and network-ing in H-CRANs are discussed. Then futurechallenges are highlighted, followed by the con-clusions in the final section.
SYSTEM ARCHITECTURE ANDPERFORMANCE ANALYSIS OF H-CRANS
Although HetNets are good alternatives to pro-vide seamless coverage and high capacity in 4Gsystems, there are still two remarkable chal-lenges to block their commercial development:• The SE performance should be enhanced
because the intra- and intercell CoMPs need ahuge amount of signaling in backhaul links tomitigate interference among HPNs and LPNs,which often makes the capacity of backhaullinks overconstrained.
• Ultra dense LPNs can improve capacity at thecost of consuming too much energy, whichresults in low EE performance.
Figure 1. Cellular system evolution to 5G.
5G
Non-cooperativecellular RAN
Intercell cooperativecellular RAN
MassiveMIMO
MBS
MBSCoMP
CoMP
RRH
Cloud computing1G/2G/3G 4G
RRH
RRH
RRHRRH
Cloud-computing-based cooperativecommunication
MBS
Optical/micro-meter
PENG_LAYOUT_Layout 12/18/14 4:14 PM Page 127
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H-CRAN
ì CombineLPNswithahigh-powernode(HPN,e.g.,macroormicrobasestaGon)toformaHetNet.
ì IntegratewithaC-RANarchitecturebyconnecGngtheHPNtotheBBUpool
ì DistributeoperaGonsbytheC-RANandtheHPNì HPNsupportsvoiceandlow-
ratedataì C-RANsupportshigh-rate
data
IEEE Wireless Communications • December 2014 127
tion for providing high EE together with gigabitdata rates across software defined wireless com-munication networks, in which the virtualizationof communication hardware and software ele-ments place stress on communication networksand protocols. Consequently, heterogeneouscloud radio access networks (H-CRANs) areproposed in this article as a cost-effective poten-tial solution to alleviate inter-tier interferenceand improve cooperative processing gains inHetNets through combination with cloud com-puting. The motivation of H-CRANs is toenhance the capabilities of HPNs with massivemultiple antenna techniques and simplify LPNsthrough connecting to a signal processing cloudwith high-speed optical fibers. As such, the base-band data path processing as well as radioresource control for LPNs are moved to thecloud server to take advantage of cloud comput-ing capabilities. In the proposed H-CRANs, thecloud-computing-based cooperation processingand networking gains are fully exploited, theoperating expenses are lowered, and energy con-sumption of the wireless infrastructure isdecreased.
Figure 1 illustrates the evolution milestonefrom conventional 1G to the H-CRAN-based 5Gsystem. In the traditional first, second, and thirdgeneration (1G, 2G, 3G) cellular systems, coop-erative processing is not demanded becauseintercell interference can be avoided by utilizingstatic frequency planning or code-division multi-ple access (CDMA) techniques. However, forthe orthogonal frequency-division multiplexing(OFDM)-based 4G systems, the intercell inter-ference is severe because of the spectrum reusein adjacent cells, especially when a HetNet isdeployed. Therefore, intercell or inter-tier coop-erative processing through CoMP is critical in4G. To control the more and more severe inter-ference and improve SE and EE performances,cooperative communication techniques haveevolved from two-dimensional CoMP to three-dimensional large-scale cooperative processingand networking with cloud computing. There-fore, for the H-CRAN-based 5G system, cloud-
computing-based cooperative processing andnetworking techniques are proposed to tacklethe aforementioned challenges of 4G systemsand in turn meet the performance demands of5G systems.
In this article, we are motivated to make aneffort to offer a comprehensive survey of thetechnological features and core principles of H-CRANs. In particular, the system architecture ofH-CRANs is presented, and SE/EE performanceof H-CRANs is introduced. Meanwhile, thecloud-computing-based cooperative processingand networking techniques to improve SE andEE performance in H-CRANs are summarized,including advanced signal processing in the phys-ical (PHY) layer, cooperative radio resourcemanagement (RRM), and self-organization inthe upper layers. The challenging issues relatedto techniques and standards are discussed aswell.
The remainder of this article is outlined asfollows. H-CRAN architecture and performanceanalysis are introduced next. Following that, thekey promising technologies related to cloud-computing-based signal processing and network-ing in H-CRANs are discussed. Then futurechallenges are highlighted, followed by the con-clusions in the final section.
SYSTEM ARCHITECTURE ANDPERFORMANCE ANALYSIS OF H-CRANS
Although HetNets are good alternatives to pro-vide seamless coverage and high capacity in 4Gsystems, there are still two remarkable chal-lenges to block their commercial development:• The SE performance should be enhanced
because the intra- and intercell CoMPs need ahuge amount of signaling in backhaul links tomitigate interference among HPNs and LPNs,which often makes the capacity of backhaullinks overconstrained.
• Ultra dense LPNs can improve capacity at thecost of consuming too much energy, whichresults in low EE performance.
Figure 1. Cellular system evolution to 5G.
5G
Non-cooperativecellular RAN
Intercell cooperativecellular RAN
MassiveMIMO
MBS
MBSCoMP
CoMP
RRH
Cloud computing1G/2G/3G 4G
RRH
RRH
RRHRRH
Cloud-computing-based cooperativecommunication
MBS
Optical/micro-meter
PENG_LAYOUT_Layout 12/18/14 4:14 PM Page 127
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H-CRANarchitecture
IEEE Wireless Communications • December 2014128
Cloud radio access networks (C-RANs) are bynow recognized as curtailing the capital and oper-ating expenditures, as well as providing a hightransmission bit rate with fantastic EE perfor-mance [4]. The remote radio heads (RRHs) oper-ate as soft relay by compressing and forwardingthe received signals from UEs to the centralizedbaseband unit (BBU) pool through the wired/wire-less fronthaul links. To distinguish the advantagesof C-RANs, the joint decompression and decodingschemes are executed in the BBU pool. Accurate-ly, HPNs should still be critical in C-RANs toguarantee backward compatibility with the existingcellular systems and support seamless coveragesince RRHs are mainly deployed to provide highcapacity in special zones. With the help of HPNs,the multiple heterogeneous radio networks can beconverged, and all system control signalings aredelivered wherein. Consequently, we incorporateHPNs into C-RANs, and thus H-CRANs are pro-posed to take full advantage of both HetNets andC-RANs, in which cloud computing capabilitiesare exploited to solve the aforementioned chal-lenges in HetNets.
SYSTEM ARCHITECTURE OF H-CRANSSimilar to the traditional C-RAN, as shown in Fig.2, a huge number of RRHs with low energy con-sumption in the proposed H-CRANs cooperatewith each other in the centralized BBU pool toachieve high cooperative gains. Only the frontradio frequency (RF) and simple symbol process-ing functionalities are implemented in RRHs,while the other important baseband physical pro-cessing and procedures of the upper layers are exe-
cuted jointly in the BBU pool. Subsequently, onlypartial functionalities in the PHY layer are incor-porated in RRHs, and the model with these partialfunctionalities is denoted as PHY_RF in Fig. 2.
However, different from C-RANs, the BBUpool in H-CRANs is interfaced with HPNs tomitigate the cross-tier interference betweenRRHs and HPNs through centralized cloud-computing-based cooperative processing tech-niques. Furthermore, the data and controlinterfaces between the BBU pool and HPNs areadded and denoted as S1 and X2, respectively,whose definitions are inherited from the stan-dardization definitions of the Third GenerationPartnership Project (3GPP). Since voice servicecan be provided efficiently through the packetswitch mode in 4G systems, the proposed H-CRAN can support both voice and data servicessimultaneously, and administration of voice ser-vice is preferred to be done by HPNs, while highdata packet traffic is mainly served by RRHs.
Compared to the traditional C-RAN architec-ture, the proposed H-CRAN alleviates the front -haul requirements with the participation ofHPNs. The control signaling and data symbolsare decoupled in H-CRANs. All control signal-ing and system broadcasting data are deliveredby HPNs to UEs, which simplifies the capacityand time delay constraints in the fronthaul linksbetween RRHs and the BBU pool, and makesRRHs active or sleep efficiently to decreaseenergy consumption. Furthermore, some bursttraffic or instant messaging service with a smallamount of data can be supported efficiently byHPNs. The adaptive signaling/control mecha-
Figure 2. System architecture for proposed H-CRANs.
Gateway
5GMBS
MassiveMIMO
NetworkMACPHY
NetworkMACPHY
MACPHY_baseband
RRH
HPN1
Gateway
Gateway
Gateway
Internet
PHY_RF
PHY_RFPHY_RF
PHY_RF
BSCRRH
RRHRRH
PHY_RF
X2/S1 X2/S1
Backhaul
Fronthaul Fronthaul
Gateway
Fronthaul
Fronthaul
BBU pool
HPNi
HPNN
2G/3G/LTEMBS
Backhaul
X2/S1
Backhaul X2/S1
Backhaul
PHY_RFPHY_RF
PHY_RF
NetworkMACPHY
NetworkMACPHY
HPNj
IEEE 802.16base station
IEEE 802.11access point
Network
Since voice service canbe provided efficientlythrough the packetswitch mode in 4G sys-tems, the proposed H-CRAN can support bothvoice and data servicessimultaneously, andadministration of voiceservice is preferred to bedone by HPNs, whilehigh data packet trafficis mainly served byRRHs.
PENG_LAYOUT_Layout 12/18/14 4:14 PM Page 128
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ConventionalH-CRAN
CO
s1/x2
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CO
Proposedapproach
9
• Localprocessingunits• Trade-offbetweenenergyconsumpGonandfronthaul/backhaulload
CO
s1/x2
Proposedapproach(detailedview)
CO
ì InterconnecGonarchitectureandtechnologies
10
MEC/BBUProcessing/
Linecards
Switch/Spliber
Researchopportunities:staticscenario
Planningì Given:Wirelessnetwork
load
ì Determine:ì Placementofprocessing
funcGonsì Levelofsplicngì ControloperaGon(BBU
processing,CoMP,etc)
Dimensioningì Given:networkloadand
topology
ì Determine:ì Thenumberof
wavelengthstolocalprocessingfuncGons
ì Thenumberofwavelengthstofronthauling
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Researchopportunities:dynamicscenario
Planningì LoadBalancingstrategiesfor
DynamicAccesstopologiesì Enable/Disablelocal
processingunitsaccordingtowirelesstrafficfluctuaGons
ì ConsiderEEtoswitchon/offRRHorHPC.
ì Re-opGmizeparametersforthenewtopology
Dimensioningì Re-sizetheamountof
resources(wavelengths)availabletoeachfuncGonì Processingì Fronthauling
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ìQoS-awareservicedegradationinEON
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Motivations
ì Intheoccurrenceofresourceshortage,servicesshouldbedegradedbyeither:ì ChangethemodulaGonformat
ì Decreasetransmissionrateì Increasedelay
ì PreemptexisGngconnecGonstoaccommodatenewones
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wi
w2
w1
References
ì OnQoS-AssuredDegradedProvisioninginServiceDifferenGatedOpGcalTelecomNetworks.ZhizhenZhong,NanHua,JipuLi,GustavoB.Figueiredo,YanheLi,XiaopingZheng,and
BiswanathMukherjee.SubmibedtoICC’16
ì OnHybridIRandARServiceProvisioninginElasGcOpGcalNetworks.WeiLu,ZuqingZhu,andBiswanathMukherjee.JournalOfLightwaveTechnology,November2015.
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QoSmodelvs.stateofthenetwork
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wi
w2
w1
WeightsassignedtolightpathsmightbeGghtenedtoaQoSmodel,whichinitsturndeterminestheprecedencerelaGonamonglightpaths
QoSmodelvs.stateofthenetwork
ì ForpracGcalpurposesnotonlytheQoSmodelshouldbeconsideredbutalsothestateofthenetwork
ì However,consideringthestateofthenetworktomakedecisionswouldleadtoviolaGonoftheQoSmodel..
ProposedsoluGon:toprovideadynamicweighGngfuncGontoprioriGzerequestsdependingontheQoSmodelandon
thestateofthenetwork.
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ProportionalQoSmodel
ì InaproporGonalservicedifferenGaGonmodelitisexpectedthat:
(1)
whereqi is istheevaluatedQoSmetricandsi thedifferenGaGonfactorforclassiandNthetotalnumberofclasses
letτbeashortinterval,themeasuredQoSmetricsobtainedduringτisgivenby:
(2)
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qisi=qjsj
(i, j =1,...,N )
qi (t, t +τ )si
=qj (t, t +τ )
sj±Δij
Dynamicweightingfunction
FromequaGon1wehave
Orinanotherwords:
whereqT and swt representthetotalblockingprobabilityandthesumofweighteddifferenGaGonparametersandaregivenby:
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a1q1a1s1
= ... = anqnansn
=a1q1 + a2q2 +...+ anqna1s1 + a2s2 +...+ ansn
qi* = si
akqkk=1
N
∑
akskk=1
N
∑=
siakaTk=1
N
∑ sk
akqkk=1
N
∑aT
=siswt
qT
qT =akqk
k=1
N
∑aT
, swt =akaT
∑ sk
Dynamicweightingfunction
ì ThefollowingequaGoncanbeusedtomeasurethedeviaGonofqiregardingq*i
ì ThefollowingdynamicweighGngfuncGoncanbeappliedtoprioriGzerequestsaccordingtothepreviousdeviaGon
ì WherePirepresentsthestaGcpriorityofeachclass,Niisthenumberofclass“I”requestsands(t)isacompoundfuncGonrepresenGngthestateofthenetwork(fragmentaGon,lightpathcompleGonGme,signalingcost..etc)
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Δi =qiqi*
wi =α(ΔipiNi
)+βs(t)
IndividualActivities
ì Problem:SchedulingVPONstosupportCoMP
ì DesignandimplementaGonofsimulator
ì Analysisof2Kfactorialexperimentaldesign
ì ICCpaper:LoadBalancingandLatencyReducGoninMulG-UserCoMPoverTWDM-VPONs
ì Extensiontojournal:Newmetrics,load-awareschedulingalgorithm
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Collaborations
Task Fellow
1 BayesianNetworkandDynamicBayesianNetworkmodelsforcapturingcascadingfailures
Carlos
2 AnalyGcalModelbasedonGeneralizedProcessorSharingtoesGmatemaximumachievabledelayindegradedservicenetworks
Zhizhen
3 SchedulingalgorithmforVPONformaGontosupportSU-CoMP Zhizhen
4 BBUplacementinOpGcalDatacenterstosupportmulG-tenantsystems
Carlos
5 AfuzzyopGmizaGonmodeltothetravelingrepairman, ChenMa
6 OpGmalityproofsforschedulingalgorithmsinNG-EPON Lin
7 CoMP-setSelecGonandVPONschedulingforMU-COMP Xinbo
8 ResourceallocaGonstrategiesinapplicaGon-awarenetworks Divya
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Learnings
ì “Bealeader“
ì “Beproblem-orientedratherthansoluGonoriented”
ì “Everythingcanberesearchablebutwe'reonlyinterestedinproblemsthatcanbefundable”
ì “We'renotinterestedinproblemsthatwerestudiedbyothers”
Bis
ì “High-qualityresultsandpresentaGonistheminimumexpected”
Massimo
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Goals
ì ImproveEnglish
ì Makefriends
ì Collectculturalexperiences
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PersonalGoals
ì ImproveEnglish
ì Makefriends
ì Collectculturalexperiences
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