ì

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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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