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IEEE Communications Magazine November 2014 650163-6804/14/$25.00
2014 IEEE
Patrick KwadwoAgyapong, Mikio Iwamu-ra, Dirk Staehle,
andWolfgang Kiess are withDOCOMO Communica-tions Laboratories
EuropeGmbH.
Anass Benjebbour is withNTT DOCOMO Inc.
1 In [3], reliability isdefined as the probabil-ity that a
certain amountof data to or from an enduser device is successful-ly
transmitted to anotherpeer (e.g., Internet serv-er, mobile device,
sensor,etc.) within a predefinedtime frame, i.e., before acertain
deadline expires.The amount of data tobe transmitted and
thedeadline are dependenton the service character-istics.
2 In the context of thisarticle, robustness isdefined as the
ability ofthe network to support aminimum predefinedservice level
(e.g., mini-mum signal-to-interfer-ence-plus-noise ratio,SINR, to
support basicvoice communications)regardless of the
networkconditions (e.g., in natu-ral disasters).
INTRODUCTIONDespite the advances made in the design andevolution
of fourth generation cellular networks,new requirements imposed by
emerging commu-nication needs necessitate a fifth generation(5G)
mobile network. New use cases such ashigh-resolution video
streaming, tactile Internet,road safety, remote monitoring, and
real-timecontrol place new requirements related tothroughput,
end-to-end (E2E) latency,reliability,1 and robustness2 on the
network. Inaddition, services are envisioned to provideintermittent
or always-on hyper connectivity formachine-type communications
(MTC), coveringdiverse services such as connected cars, connect-ed
homes, moving robots, and sensors that mustbe supported in an
efficient and scalable man-ner. Furthermore, several emerging
trends suchas wearable devices, full immersive experience(3D), and
augmented reality are influencing thebehavior of human end users
and directly affect-ing the requirements placed on the network.
Atthe same time, ultra-dense small cell deploy-ments and new
technologies such as massivemultiple-input multiple-output (mMIMO),
soft-ware defined networking (SDN), and networkfunction
virtualization (NFV) provide an impe-
tus to rethink the fundamental design principlestoward 5G.
This article proposes a novel 5G mobile net-work architecture
that accommodates the evolu-tion of communication types, end-user
behavior,and technology. The article first highlights trendsin
end-user behavior and technology to motivatethe challenges of 5G
networks. Some potentialenablers are identified, and design
principles fora 5G network are highlighted. This is followedby the
articulation of a 5G mobile network archi-tecture together with
details of some fundamen-tal technology enablers and design
choices, anda discussion of issues that must be addressed torealize
the proposed architecture and an overall5G network. The article
wraps up with proof ofconcept evaluations and conclusions.
CURRENT TRENDSIt is well known that mobile data consumption
isexploding, driven by increased penetration ofsmart devices
(smartphones and tablets), betterhardware (e.g., better screens),
better user inter-face design, compelling services (e.g.,
videostreaming), and the desire for anywhere, anytimehigh-speed
connectivity. What is perhaps notwidely mentioned is that more than
70 percentof this data consumption occurs indoors inhomes, offices,
malls, train stations, and otherpublic places [1]. Furthermore,
even thoughmobile data traffic is increasing at a brisk
pace,signaling traffic is increasing 50 percent fasterthan data
traffic [2].
More end users are using multiple deviceswith different
capabilities to access a mix of besteffort services (e.g., instant
messaging and email)and services with quality of experience
(QoE)expectations (e.g., voice and video streaming).Over-the-top
(OTT) players provide services andapps, some of which compete
directly with coreoperator services (e.g., voice, SMS, and
MMS).Connectivity is increasingly evaluated by endusers in terms of
how well their apps work asexpected, regardless of time or location
(in acrowd or on a highway), and they tend to beunforgiving toward
the mobile operator whenthese expectations are not met. Moreover,
thebattery life of devices and a seamless experienceacross multiple
devices (or a device ecosystem)
ABSTRACTThis article presents an architecture vision to
address the challenges placed on 5G mobile net-works. A
two-layer architecture is proposed, con-sisting of a radio network
and a network cloud,integrating various enablers such as small
cells,massive MIMO, control/user plane split, NFV,and SDN. Three
main concepts are integrated:ultra-dense small cell deployments on
licensedand unlicensed spectrum, under control/userplane split
architecture, to address capacity anddata rate challenges; NFV and
SDN to provideflexible network deployment and operation;
andintelligent use of network data to facilitate opti-mal use of
network resources for QoE provision-ing and planning. An initial
proof of conceptevaluation is presented to demonstrate thepotential
of the proposal. Finally, other issuesthat must be addressed to
realize a complete 5Garchitecture vision are discussed.
5G NETWORKS: END-TO-END ARCHITECTURESAND INFRASTRUCTURE
Patrick Kwadwo Agyapong, Mikio Iwamura, Dirk Staehle, Wolfgang
Kiess, and Anass Benjebbour
Design Considerations for a 5G Network Architecture
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IEEE Communications Magazine November 201466
have also become important issues for many endusers.
The Internet of Things (IoT), which addsanything as an
additional dimension to connec-tivity (in addition to anywhere and
anytime), isalso becoming a reality. Smart wearable devices(e.g.,
bracelets, watches, glasses), smart homeappliances (e.g.,
televisions, fridges, thermostats),sensors, autonomous cars, and
cognitive mobileobjects (e.g., robots, drones) promise a
hyper-connected smart world that could usher in manyinteresting
opportunities in many sectors of lifesuch as healthcare,
agriculture, transportation,manufacturing, logistics, safety,
education, andmany more. Even though operators currently relyon
existing networks (especially widely deployed2G/3G networks and
fixed line networks) to sup-port current IoT needs, many of the
envisagedapplications impose requirements, such as, verylow latency
and high reliability, that are not easi-ly supported by current
networks.
To cope with such evolving demands, opera-tors are continuously
investing to enhance net-work capability and optimize its usage.
Operatorsare deploying more localized capacity, in theform of small
cells (e.g., pico and femto cellsand remote radio units, RRUs, that
are connect-ed to centralized baseband units by optical fiber)to
improve capacity. In addition, traffic offload-ing to fixed
networks through local area tech-nologies such as Wi-Fi in
unlicensed frequencybands has become widespread. To optimize
net-work usage for better QoE in a fair manner,mobile networks are
also integrating more func-tionality such as deep packet inspection
(DPI),
caching, and transcoding. All these improve-ments come at
significant capital and operatingcosts, however.
With the increasing complexity and associatedcosts, several
concepts and technologies that haveproved useful to the information
technology (IT)sector are becoming relevant to cellular networksas
well. For instance, an industry specificationgroup (ISG) set up
under the auspices of the Euro-pean Telecommunications Standards
Institute(ETSI ISG NFV) is currently working to definethe
requirements and architecture for the virtual-ization of network
functions and address identifiedtechnical challenges. Similarly,
the Open Network-ing Foundation approved a Wireless and
MobileWorking Group in November 2013 to identify usecases in the
wireless and mobile domain that canbenefit from SDN based on
OpenFlow.
5G CHALLENGES, ENABLERS, ANDDESIGN PRINCIPLES
Based on current trends, it is generally under-stood that 5G
mobile networks must address sixchallenges that are not adequately
addressed bystate-of-the-art deployed networks (Long
TermEvolution-Advanced, LTE-A): higher capacity,higher data rate,
lower E2E latency, massivedevice connectivity, reduced capital and
opera-tions cost, and consistent QoE provisioning [3,4]. These
challenges are briefly discussed belowtogether with some potential
enablers to addressthem. Figure 1 provides an overview of the
chal-lenges, enablers,3 and corresponding design prin-
Figure 1. 5G challenges, potential enablers, and design
principles.
Capacity
x1000> 70% indoor
Data rate
x10-100
E2E latency
< 5ms
Cost
Sustainable
QoE
Consistent
Massivenumber of
connections
x10-100
Spectrum
5G design principlesEnablers to address challengesChallenges
Use high frequencies and other spectrumoptions (e.g., pooling,
aggregation).
C/U-plane split Address coverage and capacity separately.
NFV/SDN/cloud Minimize number of network layers andpool
resources as much as possible.
3rd party/userdeployment models
Simple access points
Minimize functionalities performed byaccess points.
Energy-efficienthardware
Energy managementtechniques
Maximize energy efficiency across allnetwork entities.
All optical networks Optical transmission and switchingwherever
possible.
Small cells
Local offload (e.g., D2D, enhanced local area)
Caching/pre-fetching/CDN
Bring communicating endpoints closertogether.
SON
Traffic management
Big data-drivennetwork intelligence
Use an intelligent agent to manage QoE,routing, mobility and
resource allocation.Redesign NAS protocols, services andservice
complexity.
Massive/3D MIMO
New air interface(e.g., new waveform, advanced
multiple access, shorter TTI)
Design new air interface, new multipleaccess scheme and L1/L2
techniques thatcan be optimized for high frequencies,latency and
massive connectivity.
Based on current
trends, it is generally
understood that
5G mobile networks
must address six
challenges that are
not adequately
addressed by state-
of-the-art deployed
networks: higher
capacity, higher data
rate, lower E2E
latency, massive
device connectivity,
reduced capital and
operations cost,
and consistent
QoE provisioning.
3 The connectionsbetween the challengesand enablers depict
themost significant linking,but not necessarily allpossible
connections.
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IEEE Communications Magazine November 2014 67
ciples for 5G. It must be noted that the enablershighlighted in
Fig. 1 also introduce their own setof challenges and corresponding
key perfor-mance indicators (KPIs). Some of these chal-lenges are
discussed in the relevant sections.Nevertheless, a detailed
discussion of the rele-vant KPIs is outside the scope of this
article.The interested reader is referred to [4] for moredetails on
this aspect.
SYSTEM CAPACITY AND DATA RATEBeyond 2020 mobile networks need to
support a1000-fold increase in traffic relative to 2010 lev-els,
and a 10- to 100-fold increase in data rateseven at high mobility
and in crowded areas ifcurrent trends continue [1, 3, 4]. This
requiresnot only more capacity in the radio access net-work (RAN),
but equally important, also in thebackbone, backhaul, and
fronthaul. Pricingschemes can be used to manage and
potentiallyreduce the increase in data consumption, asalready
demonstrated by operators in the mar-ket. However, as customers are
willing to pay forthe provisioned service rather than the data
vol-ume, pricing models may not be effective to sup-press traffic
in the future.
The current consensus is that a combination ofmore spectrum,
higher spectrum efficiency, net-work densification, and offloading
are necessary toaddress these challenges in the RAN [5].
Opportu-nities for more spectra include higher frequencybands
(e.g., millimeter-wave, mmW), unlicensedspectrum, and aggregation
of fragmented spectrumresources using carrier aggregation
techniques.Dual connectivity of terminals to multiple base
sta-tions can exploit aggregated use of spectrumdeployed at
different base stations. Besides theavailable bandwidth, high
frequency bands alsoallow for mMIMO using antenna arrays with
smallform factors, which can provide a 10-fold increasein capacity
compared to conventional single-anten-na systems [6]. Nevertheless,
high frequency bandssuffer from high path loss attenuation and are
lim-ited to line of sight (LOS) and short-range non-LOS
environments. Massive MIMO can beexploited to extend the coverage
of higher frequen-cy bands by relying on beamforming gains.
Advanced physical layer techniques, such ashigher-order
modulation and coding schemes(MCS), such as 256-quadrature
amplitude mod-ulation (QAM), increase spectral efficiency andcan be
combined with mMIMO to increase sys-tem capacity. By adding some
intelligence at thetransmitter and receiver, potential
interferencecan be coordinated and cancelled at the receiverto
increase system throughput [7]. With suchtechniques in place, new
schemes such as non-orthogonal multiple access (NOMA), filter
bankmulticarrier (FBMC), and sparse coded multipleaccess (SCMA) can
further be utilized toimprove spectral efficiency. For example,
NOMAwith successive interference cancelling (SIC)receivers has been
shown to improve overallthroughput in macrocells compared to
orthogo-nal multiple access schemes by up to 30 percenteven for
high-speed terminals, with further gainsexpected with advanced
power control [8].
Network densification refers to the densedeployment of many
small cells. High carrier fre-quencies are well suited for small
cells. The high
attenuation they suffer is no longer seen as adrawback, but
rather as an enabler to provideeffective separation and mitigate
interferencebetween densely deployed small cells. To allowefficient
improvement of capacity at critical loca-tions, it is desirable
that coverage and capacitybe addressed independently. This can be
realizedthrough an architecture where control (C) anduser data (U)
planes are split among differentcells [9]. The benefit of this
approach is that U-plane resources can be scaled independent of
C-plane resources. This allows more U-planecapacity to be provided
in critical areas where itis needed, without the need to also
provide co-located C-plane functionalities. Thus, more flexi-ble
deployments at lower costs can be realized.In such a C/U-plane
split architecture, macro-cells can provide coverage (C+U), and
smallcells can provide localized capacity (U).
Techniques like mMIMO and higher-orderMCS can be employed in
small cells to boostthroughput [5]. Massive MIMO has an
increasedrisk of link failure due to narrow beamforming,but this
could be mitigated by employing robusttechniques like dual
connectivity, which alwaysprovides uninterrupted fallback to the
coveragelayer. Additionally, local offload through tech-niques such
as network-controlled device-to-device (D2D) communications can
furtherincrease achievable system throughput [10].
Advances in optical networking, includingoptical switching, may
be able to address thecapacity requirements in the backbone,
back-haul, and fronthaul. In addition, mMIMO can beused to provide
high-capacity wireless backhauland fronthaul links in LOS
conditions.
END-TO-END LATENCYEnd-to-end latency is critical to enable new
real-time applications. For example, remote con-trolled robots for
medical, first response, andindustrial applications require rapid
feedbackcontrol cycles in order to function well. Safety-critical
applications for cars and humans, builtaround vehicle-to-vehicle
(V2V) and vehicle-to-infrastructure (V2I) communication, also
requirevery quick request-response and feedback con-trol cycles
with high availability and reliability.Augmented and virtual
reality applications (e.g.,immersive displays and environments)
requirevery fast request-response cycles to mitigatecyber sickness.
In order to realize these applica-tions, networks must be able to
support a targetof 1 ms E2E latency with high reliability [11].
Innovations in air interface, hardware, protocolstack, backbone,
and backhaul (all-optical trans-mission and switching), as well as
network archi-tecture can all help to meet this challenge. A newair
interface with new numerology, such as shortertransmission time
interval (TTI), can reduce over-the-air latency to a few hundred
microseconds.Shorter TTI requires high available bandwidth,but this
can be supported by using higher frequen-cy bands. Note that such
new numerology relieson significant improvements in receiver
hardware(e.g., processing power and buffer size).
In addition, E2E latency can be reduced byenhancements in
higher-layer protocols (e.g., usecase and network-aware
admission/congestioncontrol algorithms to replace TCP slow
start),
To allow efficient
improvement of
capacity at critical
locations, it is desir-
able that coverage
and capacity be
addressed indepen-
dently. This can be
realized through an
architecture where
control (C) and user
data (U) planes are
split among
different cells.
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IEEE Communications Magazine November 201468
bringing communicating endpoints closer (e.g.,through
network-controlled D2D and ultra-densesmall cell deployments with
local breakout) andadding more intelligence at the edge of the
net-work. The latter is realized, e.g., through cachingand
pre-fetching techniques, service-dependentlocation of C-plane
protocols and orchestration.For example, C-plane protocols
necessary forlatency-critical MTC services may be distributedat the
edge of the network, whereas C-plane pro-tocols required for
services with more relaxedlatency requirements could be located at
a cen-tral entity. Efficient design of the non-access stra-tum
(NAS) could also help reduce E2E latency.For example, integrating
NAS and access stratum(AS) could reduce the control signaling
requiredto set up and maintain a data connection, whichcan reduce
the E2E latency. Alternatively, devel-oping NAS protocols better
tailored to new usecases could also yield a similar result.
MASSIVE NUMBER OF CONNECTIONSThe number of connected devices is
expected toincrease between 10- and 100-fold beyond 2020[3]. These
will range from devices with limitedresources that require only
intermittent connec-tivity for reporting (e.g., sensors) to devices
thatrequire always-on connectivity for monitoringand/or tracking
(e.g., security cameras, transportfleet). In addition to the sheer
number of con-nected devices, a challenge is to support
thediversity of devices and service requirements in ascalable and
efficient manner.
A combination of advances in air interfacedesign, signaling
optimization, and intelligentclustering and relaying techniques can
all con-tribute to support hyperconnectivity. Forinstance, using
one device as a gateway or relayto aggregate traffic from multiple
devices canreduce the signaling load on the network. Moreefficient
protocols that combine AS and NASalso reduce the signaling burden.
Moreover, con-tention-based and connectionless access proce-dures
can be used to efficiently support MTCapplications that only
require intermittent con-nectivity to transmit small packets.
Not all devices may be equipped with high-pre-cision devices to
cope, for example, with tight syn-chronization to maintain
orthogonality of signalsin a multiple access environment when
newnumerology is introduced to reduce latency. Tomitigate this, new
waveforms such as FBMC,which can suppress out-of-band emission to
reduceinterference under an asynchronous environment,can be
explored [12]. FBMC also has a potentialto cope better than OFDM
with doubly dispersivechannels when both the transmitting and
receivingendpoints are moving (e.g., in a V2V application).
In addition, supporting devices with limitedresources such as
sensors will require advances inbattery and energy harvesting
technologies onone hand and efficient signaling and data
trans-mission protocols on the other. For instance,robust medium
access techniques combining bothcontrol and data transmission could
be explored.
COSTConnectivity is seen as an important enabler
forsocio-economic development. Therefore, it isimportant to reduce
the infrastructure cost as
well as the costs associated with their deploy-ment,
maintenance, management, and operationto make connectivity a
universally available,affordable, and sustainable utility. The
challengefor the design of 5G is that huge improvementsare needed
to address the new requirements, butcustomers are not willing to
pay proportionally.In effect, 5G should be a network (RAN,
core,backbone routers, and backhaul) that addressesall the new
requirements at a cost that will makeservice provisioning
sustainable.
Solving the capacity and data rate challengeswith network
densification could be very expen-sive in terms of equipment,
maintenance, andoperations. One way to reduce equipment cost isto
minimize the number of functionalities at thebase station. This
could be done by implementingonly layer 1/2 (L1/L2) functionalities
in the basestation and moving higher-layer functionalities toa
network cloud that serves many base stations.Reducing the number of
functionalities results insimpler base stations, which could be
deployed byusers and remotely or autonomously managed toreduce
deployment and operation costs.
Energy consumption is a significant opera-tions cost driver,
with the RAN estimated toconsume 7080 percent of the energy
require-ments [13]. Therefore, intelligent energy man-agement
techniques, especially in the RAN,could provide a viable means to
reduce overallnetwork operations costs. Energy-efficient hard-ware
design, low-power backhaul, and intelligentenergy management
techniques, especially inultra-dense networks, to put base stations
tosleep when not in use can all contribute to reduc-ing the cost of
operating a 5G network [13].
NFV and SDN are also viable enablers toreduce costs. NFV
decouples network function-ality from dedicated hardware and
promotesimplementation of functionality in software
ongeneral-purpose IT hardware operated accordingto a cloud model
[14]. SDN decouples C- and U-planes of network devices, and
provides a logi-cally centralized network view and control,
whichfacilitates transport network optimization. Thesetechnologies
will make the network more flexibleas new functionality can be
introduced with sim-ple software upgrades, and more
sophisticatedalgorithms can be employed to manage the net-work from
a holistic viewpoint. Moreover,pooled hardware resources can be
shared amongmultiple functions, thus realizing multiplexinggains
and lowering the amount of necessaryhardware. The flexibilities
enabled by NFV andSDN can make the network quick to deploy andmore
adaptable, and reduce time to market fornew services.
QOEQuality of experience describes the subjectiveperception of
the user as to how well an applica-tion or service is working.
Quality of experienceis highly application- and user-specific, and
can-not be generalized. For example, the QoE ofvideo applications
depends on the quality of theencoded and delivered video in the
context ofthe display on which the video is shown. Deliver-ing an
application with too low QoE leads touser dissatisfaction, whereas
too high QoEunnecessarily drains resources on both the user
Pooled hardware
resources can be
shared among multi-
ple functions, thus
realizing multiplexing
gains and lowering
the amount of nec-
essary hardware. The
flexibilities enabled
by NFV and SDN can
make the network
quick to deploy and
more adaptable, and
reduce time to mar-
ket for new services.
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IEEE Communications Magazine November 2014 69
(e.g., device battery) and operator (e.g., radioand transport
network resource, base stationpower) sides. Hence, a challenge for
5G is tosupport applications and services with an opti-mal and
consistent level of QoE anywhere andanytime.
Despite the diversity of QoE requirements,providing low latency
and high bandwidth gener-ally improves QoE. As such, most enablers
men-tioned previously can improve QoE. Additionally,traffic
optimization techniques can be used tomeet increasing QoE
expectations. Furthermore,installing caches and computing resources
at theedge of the network allows an operator to placecontent and
services close to the end user. Thiscan enable very low latency and
high QoE fordelay-critical interactive services such as
videoediting and augmented reality.
Better models that describe the relationshipof QoE to measurable
network service parame-ters (e.g., bandwidth, delay) and context
parame-ters (e.g., device, user, and environment) arealso emerging.
Big data, including informationfrom sensors (e.g., on the device)
and statisticaluser data, can be used intelligently with suchmodels
to more precisely assess the QoE expect-ed by a user and determine
the optimal resourcesto use to meet the expected QoE. SDN can
thenbe used to flexibly provision the necessaryresources.
Besides the mobile network, advances in thefixed network and
potential convergence of thefixed and mobile networks are also
needed to
address the challenges highlighted above. How-ever, specific
discussions related to the fixed net-work and convergence of the
mobile and fixednetworks are outside the scope of this article.
5G MOBILE NETWORKARCHITECTURE VISION
Figure 2 illustrates a 5G mobile network archi-tecture that
utilizes the enablers discussed previ-ously. The key elements in
the architecture aresummarized below: Two logical network layers, a
radio network
(RN) that provides only a minimum set ofL1/L2 functionalities
and a network cloudthat provides all higher layer
functionalities
Dynamic deployment and scaling of func-tions in the network
cloud through SDNand NFV
A lean protocol stack achieved throughelimination of redundant
functionalitiesand integration of AS and NAS
Separate provisioning of coverage andcapacity in the RN by use
of C/U-planesplit architecture and different frequencybands for
coverage and capacity
Relaying and nesting (connecting deviceswith limited resources
non-transparently tothe network through one or more devicesthat
have more resources) to support multi-ple devices, group mobility,
and nomadichotspots
Figure 2. 5G mobile network vision and potential technology
enablers.
Lean protocol stack
OTT
Gateway U-plane mobility anchor OTA security provisioning
Authentication Mobility management Radio resource control NAS-AS
integration
L1/L2 functions High CF with M-MIMO - for capacity
Extraction of actionable insights from big data Orchestration of
required services and functionalities (e.g., traffic optimization,
context-aware QoE provisioning, caching, ...)
API
XaaS
API
Operatorservices
Internet
CPE C-plane entity
C-plane path
UPE
UPE
NFV enabled NW cloud
Macro cell
Small cell
RRU
CPE
NI
U-plane entityNI Network intelligence
U-plane pathRadio access linkBackhaul (fiber, copper, cable)
Wireless fronthaul
AS Access stratumCF Carrier frequencyD2D Device-to-deviceM-MIMO
Massive MIMOMTC Machine-type communicationsNAS Non-access
stratumNOMA Non-orthogonal multiple accessNW NetworkOTA Over the
airOTT Over-the-top playerRRU Remote radio unit
Data-drivenNW intelligence
C/U-plane split
Resource pooling
L1/L2 functions Low CF with NOMA - fall back for coverage High
CF with M-MIMO - wireless backhaul
L1/L2 functions Super high CF and/or unlicensed spectrum - for
local capacity Switched on on-demand
Dual connectivity Independent C/U-plane mobility
Nesting and relaying to support low-powered devices, nomadic
cells and group mobility
Network-controlled D2D
Connectionless, contention-based access with new waveforms for
MTC asynchronous access
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IEEE Communications Magazine November 201470
Connectionless and contention-based accesswith new waveforms for
asynchronousaccess of massive numbers of MTC devices
Data-driven network intelligence to opti-mize network resource
usage and planning
LOGICAL NETWORK LAYERS: RADIO NETWORK AND NETWORK CLOUD
The network architecture consists of only twological layers: a
radio network and a networkcloud. Different types of base stations
and RRUsperforming a minimum set of L1/L2 functionsconstitute the
radio network. The network cloudconsists of a U-plane entity (UPE)
and a C-plane entity (CPE) that perform higher-layerfunctionalities
related to the U- and C-plane,respectively (Fig. 2).
As shown in Fig. 3, the physical realization ofthe network cloud
could be tailored to meet vari-ous performance targets. For
example, instancesof UPEs and CPEs could be located close to
basestations and RRUs to meet the needs of latency-critical
services. To support latency-critical ser-vices, for example, it
may be better to connectRRU3 to a small nearby data center (data
center3) rather than a large data center farther away(data center
2). On the other hand, RRU1 may beconnected to a large data center
located fartheraway (data center 2) rather than a nearby smalldata
center (data center 1) if support for latency-critical services is
not required. Such flexibilityallows the operator to deploy both
large and smalldata centers to support specific service needs.
Such architecture simplifies the network andfacilitates quick,
flexible deployment and man-agement. Base stations would become
simpler
and consume less energy due to the reducedfunctionalities,
thereby making dense deploy-ments affordable to deploy and operate
[15, 16].Additionally, the network cloud allows forresource
pooling, reducing overprovisioning andunderutilization of network
resources.
DYNAMIC DEPLOYMENT AND SCALING OFNETWORK FUNCTIONS WITH SDN AND
NFV
By employing SDN and NFV, CPE and UPEfunctions in the network
cloud can be deployedquickly, orchestrated and scaled on demand.
Forinstance, when a local data center is unable tocope with a flash
crowd (e.g., due to a local disas-ter), additional capacity can be
borrowed quicklyfrom other data centers. In addition,
resourceswithin a data center can be quickly shifted tosupport
popular applications simply by addingadditional instances of the
required software.
Besides this application-level flexibility, theuse of a cloud
infrastructure also provides flexi-bility with respect to the
available raw processingcapacity. Spare cloud resources can be lent
outwhen demand is low, whereas additionalresources can be rented
through infrastructureas a service (IaaS) business models during
peakhours. Furthermore, a broad range of as a ser-vice business
models based on providing specif-ic network functionalities as a
service (i.e.,XaaS) could also be envisioned. The completeor
specific parts of the network could be provid-ed to customers
(e.g., network operators, OTTplayers, enterprises) that have
specific require-ments, for example in a mobile network as aservice
or radio network as a service model.UPE/CPE/NI as a service models,
where spe-
Figure 3. Realization of a 5G network cloud. The network cloud
is a logical entity with physical realization that can be tailored
tomeet specific needs.
Data center 4
Data center 1
Data center 2
Network intelligence(data collection,
analyses and controlover network entities)
RRU3
RRU2
RRU1
Macrocell
Small cell
NFV-enablednetwork cloud
Data center 3
CPE C-plane entity
C-plane path
UPE U-plane entity
RRU Remote radio unit
U-plane pathWireless fronthaul
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IEEE Communications Magazine November 2014 71
cific core network functionalities (Fig. 2) of themobile network
are provided a la carte as a ser-vice, could also be envisioned.
Last but not least,parts of the platform could be rented out
tothird parties like OTT players to enable the pro-vision of
services and applications that requireextremely low latency to end
users. Besides theXaaS business models that could be
facilitated,the flexibility of a cloud, coupled with SDN andNFV
technologies, also makes the network easi-er, faster, and cheaper
to deploy and manage.
LEAN PROTOCOL STACKWith virtualization, interfaces between
networkfunctionalities become interfaces between soft-ware. Two
separate protocols for the C-planemay no longer be relevant if both
NAS and ASprotocols can be virtualized. Under a unifiedcloud
paradigm, the NAS and AS protocols canbe integrated into a single
protocol, removingredundant functionality. In current LTE,
forexample, the NAS ServiceRequest and RRCConnectionRequest
messages are concatenated,but these could be merged into a single
messagein a future cloud-based and virtualized network.Similarly,
some procedures related to mobilitymanagement, session management,
and securitycan potentially be removed. As an example,
theconnection establishment procedure can be sig-nificantly
simplified by requiring a handshakeonly between the peer entities
of a single proto-col. This in turn will realize faster
connectionestablishment. Bearer-based QoS managementcould also be
replaced by simple IP marking,with proper mechanisms in place to
prevent allpackets being marked with the highest QoS class.
Similarly for the U-plane, merging of func-tionalities in the
RAN L2 and gateway function-alities in the current core network
(CN) can beconsidered. Virtualization of the U-plane is gen-erally
considered to be more difficult than thatof the C-plane due to the
sheer volume of datato be processed. Virtualization of the RAN
L2protocols can demand significant processingpower, as L2 protocols
support various featuresthat are dynamic in nature, like dynamic
trans-port block size (according to resource allocationand
instantaneous radio condition), segmenta-tion and concatenation of
packets, and hybridautomatic repeat request (ARQ). The
radioscheduler functionality and advanced featureslike mMIMO
require accurate channel stateinformation (CSI) to be effective.
Hence, if suchfeatures are to be virtualized, CSI also needs tobe
delivered to the virtualized entity, potentiallyimposing
significant transport overhead. Howev-er, with sufficient
advancements in technologyand careful selection of functionalities,
some ofthe services provided by L2 can be feasible
forvirtualization around 2020. In principle, thisallows the
functionalities provided by differentRAN and CN protocols to be
merged and a sin-gle U-plane entity to provide radio transport
ser-vices and gateway functionalities. Nevertheless,careful study
is needed to determine for whichlayers such integration can
occur.
One feature that can be potentially removedfrom the U-plane
stack is ciphering, since this isincreasingly implemented by
transport layersecurity (TLS) over IP. Generally, E2E solutions
are more efficient than encrypting segmentsalong the path.
However, E2E encryption impliesno traffic visibility along the path
and makestraffic control in networks difficult. In manyoperator
networks today, intelligent mechanismssuch as deep packet
inspection (DPI) andcaching are used to optimize resource usage
andimprove QoE. End-to-end encryption wouldmake these intelligent
mechanisms dysfunction-al. As security of signals transmitted over
the airis essential, due to the broadcast nature of radiosignals,
where to terminate ciphering in the net-work is an important
issue.
INDEPENDENT PROVISIONING OFCOVERAGE AND CAPACITY WITHC/U-PLANE
SPLIT ARCHITECTURE
Coverage and capacity are provided indepen-dently in the RN with
a C/U-plane split architec-ture. Macro and metro base stations
providecoverage using licensed spectrum in lower fre-quency bands
and existing cell sites, integrating,for example, NOMA and SIC to
boost capacity[8].
Small cell base stations (e.g., Phantom cells[9]) and RRUs
provide localized capacity using acombination of licensed and
unlicensed spec-trum in low and high frequency bands. Thesecells
are deployed indoors and at outdoorhotspots. Advanced schemes
(e.g., mMIMO) arealso implemented in some RRUs and small cellsto
boost capacity. Because of the highly variableuser and traffic
distribution in small cells, theycan be put to sleep or switched
off completelywhen they are not needed to save energy.Dynamically
switching small cells on and off canprovide significant energy
savings withoutdegrading network performance [16].
Separating coverage from capacity enablesindependent mobility of
the C-plane and U-plane in areas with overlapping coverage ofmacro
and small cell base stations. In effect, theC- and U-planes for a
terminal can take differ-ent paths. This requires the terminal to
supportconnectivity to multiple base stations at the sametime.
RELAYING AND NESTING TO SUPPORTMULTIPLE DEVICES, GROUP MOBILITY,
AND
NOMADIC HOTSPOTS
Relays are used as a means to support groupmobility (e.g.,
terminals in a moving vehicle) andnomadic hotspots. In such
scenarios, all trans-missions within the group are aggregated at
oneor more entities (e.g., a small cell) and relayedto the network
through a wireless backhaul thatconnects to the network cloud (Fig.
2). Deviceswith limited resources, such as low-poweredwearable
devices, connect non-transparently tothe network through one or
more devices thathave more resources (nesting, Fig. 2). By
con-necting non-transparently, network paging pro-cedures can be
used to initiate connections tosuch devices, thus reducing
signaling traffic andpower consumption. Together, relaying and
nest-ing provide support for a huge number of deviceswith diverse
capabilities in a scalable and effi-cient manner.
Because of the highly
variable user and
traffic distribution in
small cells, they can
be put to sleep or
switched off com-
pletely when they
are not needed to
save energy. Dynami-
cally switching small
cells on and off can
provide significant
energy savings with-
out degrading net-
work performance.
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IEEE Communications Magazine November 201472
DATA-DRIVEN NETWORK INTELLIGENCEThe architecture allows the
network cloud to col-lect various types of user-centric,
network-centric,and context-centric data. The network cloud
usesintelligent algorithms to provide real-time insightsfor
efficient resource management, mobility man-agement, local offload
decisions (e.g., network-con-trolled D2D communications), QoE
management,traffic routing, and context-aware service provision-ing
(e.g., geocasting). Furthermore, the aggregateddata can provide
useful input for network plan-ning. By providing application
programming inter-faces (APIs) to the network cloud, the
collecteddata can be used in various forms for useful public(e.g.,
urban planning) and commercial purposes.For example, the APIs can
be used to facilitatenew businesses based on selling knowledge
aboutnetwork conditions as a service to OTT players,which can allow
them to provide consistent servicequality to end users.
ISSUESSeveral issues need to be addressed in order torealize the
proposed network architecture inparticular, and 5G networks in
general. Some ofthese issues are summarized in Fig. 4 and
brieflydiscussed below.
One issue that must be addressed is how lega-cy networks will
interface and interoperate withthe new network architecture. One
could imaginea migration step where the legacy CN and RANare
migrated to separate cloud platforms duringthe development phase of
5G (Fig. 4). In orderto avoid building parallel networks, it will
beessential to specify interfaces and protocolsbetween entities in
the legacy clouds and the newnetwork cloud to ensure
interoperability.
Another issue is to determine the optimalphysical realization of
the network cloud to meetperformance and cost targets. Whereas
central-ization of resources could result in savings frompooling,
it could also lead to performance bottle-necks, higher latency, and
single points of fail-ure. Additional robustness measures will also
beneeded to avoid devastating impact on serviceavailability if the
central entity fails. Moreover,centralization could lead to the
need for largerprocessing and transport capacity at the
centralentity to process and transport the aggregatedtraffic, which
could diminish the cost savingsachieved by pooling. On the other
hand, dis-tributing resources could lead to performanceimprovements
and reduced latency, but may becostly due to reduced pooling gains
and anincreased number of data center locations atcorresponding
higher operational expenses.Finding the right balance is an
important issue.
Ultra-dense small cell deployments will beespecially useful for
indoor and hotspot environ-ments. As shown in Fig. 5, different
deploymentoptions have different implications for the net-work. In
addition to spectrum, backhaul is alsoan important issue,
especially for user deploy-ment. Local breakout may be required for
moreefficient routing through the user-provisionedbackhaul.
However, this has implications on thefunctionalities needed at the
small cell base sta-tion. For instance, U-plane processing
function-alities are needed to support local breakout.Additionally,
support for local breakout makestraffic invisible to the network,
which affectsintelligent QoE provisioning.
Besides the issues highlighted above, seam-less mobility
provisioning among different typesof deployed local and wide-area
technologies
Figure 4. Overview of issues that must be addressed to realize
the 5G architecture vision.
RAN cloud
CN cloud
Current NW Future NW
Migration and required interfaces
Minimum functionalities needed
Traffic management OTA security provisioning Seamless
mobility
Device/ID management Device discovery
Deployment and spectrum Discovery and measurement Network
synchronization
Support V2X communications
Support cognitive mobile objects
Support potentially different air interfaces
Intermediate step
LegacyCN
LegacyRAN
MME
BBU
P/S-GW
Macro cell
Small cell
BBU Baseband unitCN Core networkGW GatewayMME Mobility mgt.
entityOTA Over the airOTT Over-the-top playerP/S Packet/servingRAN
Radio access networkRRU Remote radio unit
Internet
OTT
API
XaaS
API
Operatorservices
NFV enabled NW cloudCPE
CPE
RRU
UPE
NI
C-plane entity
C-plane path
UPE U-plane entityNi Network intelligence
U-plane pathRadio access linkBackhaul (fiber, copper,
cable)Wireless backhaul
By connecting non-
transparently, net-
work paging
procedures can be
used to initiate con-
nections to such
devices, thus reduc-
ing signaling traffic
and power con-
sumption. Together,
relaying and nesting
provides support for
a huge number of
devices with diverse
capabilities in a scal-
able and efficient
manner.
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IEEE Communications Magazine November 2014 73
with potentially different functionalities also hasto be
addressed to improve the overall QoE forend users. Mechanisms to
support simultaneoussessions and seamless session mobility across
dif-ferent access networks will also be required tosupport
consistent QoE for end users. Further-more, different types of edge
networks will alsoneed to be integrated within the 5G
networkarchitecture. For instance, the communicationneeds of
cognitive mobile objects (robots2X,drones2X, etc.) will all need to
be efficientlysupported and integrated in 5G networks. Final-ly,
new paradigms of identity management andcharging will need to be
developed for 5G, inparticular, to cope with the huge number
ofdevices expected to be connected to the net-work, the diverse use
cases, and different edgenetwork topologies.
INITIAL PROOF OF CONCEPTA real-time simulator is used to
evaluate the sys-tem-level gains when some of the candidate
5Gtechnologies described in the previous sectionare introduced for
downlink transmission. Specif-ically, the gains from the hybrid
usage of macro-cells at lower frequency bands and small cells
athigher frequency bands, together with mMIMO,are demonstrated.
Figure 6 shows the deployment environmentstudied, which consists
of buildings, moving vehi-cles, users, macro base stations, and a
densedeployment of small cell base stations. A seven-cell model is
assumed with an inter-site distanceof 500 m. Each macrocell has
three sectors, andeach sector has 30 outdoor users (i.e.,
penetrationloss = 0 dB). A 3 km/h user speed is assumed.Ray tracing
is applied using the vertical planelaunch (VPL) method to emulate a
real propaga-tion environment of a 750 m 750 m dense urbanarea in
Shinjuku, Tokyo. The baseline system
consists of LTE-based macrocells using 20 MHzbandwidth at 2 GHz.
Each macrocell uses twotransmit (Tx) antennas. An antenna gain of
14dBi and a total transmit power of 49 dBm areassumed for each
macrocell base station. Forevaluating the gains of network
densification andwideband transmission at higher frequency bands,12
small cells are deployed per sector. Each smallcell uses 1 GHz
bandwidth at 20 GHz. The num-ber of Tx antennas per small cell is
64. An anten-na gain of 5 dBi and a total transmit power of 30dBm
are assumed for each small cell base station.The number of receive
antennas at the user ter-minal is 4 at both 2 GHz and 20 GHz.
For 2 4 MIMO transmission in macrocells,single-user MIMO is
applied based on implicit
Figure 5. Small cell deployment options and issues.
Unlicensedspectrum
Pros Reduced cost (equip., deployment, operation)
Cons Lack of QoE guarantees
Issues Access control Mechanisms to ensure fair-play (definition
and implementation of incentive-compatible spectrum etiquette)
Coexistence with Wi-Fi, Bluetooth, etc. Impact of diverse backhaul
types on advanced RA techniques (e.g., CoMP) Provisioning of
over-the-air security
Pros Cell sites fully controlled by the operator Additional
spectrum for operators to exploit
Cons Cost (equipment, deployment, operation) Lack of QoE
guarantees
Issues Mechanisms to ensure fair-play (definition and
implementation of incentive- compatible spectrum etiquette)
Coexistence with Wi-Fi, Bluetooth, etc. Backhaul provisioning
Licensedspectrum
Pros Reduced cost (equip., deployment, operation)
Cons Additional operation costs to provide after- service
customer support
Issues Regulatory issues Access control (public or private)
Ensuring QoE, e.g., new mechanisms to control interference (e.g.,
low Tx power) Impact of diverse backhaul types on advanced RA
techniques (e.g., CoMP) Provisioning of over-the-air security
User-deployed
Pros Cell sites fully controlled by the operator Easier to
provide QoE Advanced resource allocation (RA) techniques become
easier to realize
Cons Cost (equipment, deployment, operation) Limited spectrum
Spectrum license fees
Issues Backhaul provisioning
Operator-deployed
Figure 6. The deployment environment of the 5G real-time
simulator.
Macrocell w/3 sectors
Movingvehicles
BeamsUsers
Smallcells
Buildings
Color code of users and the ground indicates achievable user
data rate:Blue: 010 Mb/s; Green: 10100 Mb/s; Yellow: 100500
Mb/s;Orange: 500 Mb/s1 Gb/s; Red: 110 Gb/s; Pink: over 10 Gb/s
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IEEE Communications Magazine November 201474
CSI feedback using the LTE Release 8 code-book. For 64 4 mMIMO
transmission in smallcells, the CSI of users is assumed to be
perfectlyknown at the small cell base station side, andHermitian
precoding is applied for multi-layertransmission. In order to
improve both cell cov-erage (by beamforming gain) and spectrum
effi-ciency (by spatial multiplexing gain) of smallcells,
single-user MIMO and multi-user MIMOdynamic switching (up to 4
users) and rankadaptation (up to 4 layers/user) are
introduced.Proportional fairness scheduling is applied toallocate
frequency/time resources to users atmacrocells and small cells
disjointly. Note thatno intercell interference coordination (ICIC)
isapplied among either macrocells or small cells.
The performance of the candidate technolo-gies are shown in Fig.
7. Figure 7a illustrates thespectrum usage for macrocells and small
cells. Itcan be seen that the power spectrum density(PSD) becomes
lower as the spectrum band-
width is extended to 1 GHz for small cells. InFigs. 7b and 7c,
the x-axis (time [subframe])refers to the number of subframes being
pro-cessed and also the time in milliseconds (onesubframe = 1 ms).
The system throughput persubframe of a 500 m 500 m area is shown
inFig. 7b, which demonstrates that compared to amacro-only 3GPP
Release 8 LTE deployment,around 1300 system throughput gains
areachieved by a combination of dense deploymentof small cells,
using large bandwidths at higherfrequency bands and employing mMIMO
tech-niques at small cells. By simulating each of thecandidate 5G
technologies above, we see the1300 system throughput gains as the
combina-tion of almost 50 from bandwidth extensionfrom 20 MHz to 1
GHz, 4 from antenna densi-fication by adding 12 small cells per
sector, andaround 6.5 from mMIMO by introducing 64 4 mMIMO with
single-user MIMO and multi-user MIMO dynamic switching.
Figure 7. Spectrum usage and performance evaluation results of
the 5G real-time simulator: a) powerspectrum density (dBm/Hz) vs.
frequency (GHz); b) throughput (Mb/s) vs. time (subframe); c)
classi-fied UE ratio vs. time (subframe).
(a) (b)
(c)
Color code for Figure 7c indicates the fraction of users able to
achieve a particular range of data rate: Blue: 010 Mb/s; Green:
10100 Mb/s; Yellow: 100500 Mb/s; Orange: 500 Mb/s1 Gb/s;
Red: 110 Gb/s; Pink: over 10 Gb/s
New paradigms of
identity management
and charging will
need to be
developed for 5G,
in particular, to cope
with the huge
number of devices
expected to be
connected to the
network, the diverse
use cases and
different edge
network topologies.
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IEEE Communications Magazine November 2014 75
Finally, Fig. 7c shows the classified UE ratio,which gives the
fraction of users who are able toachieve a particular range of data
rate. It can beseen from this that more than 90 percent ofusers are
able to achieve data rates in excess of 1Gb/s (i.e., the red color
zone expanded to below0.1) with such a network. These initial
resultsdemonstrate the potential of network densifica-tion using
small cells, bandwidth extension inhigher frequency bands, and
mMIMO at smallcells to address the capacity and data rate
chal-lenges of 5G networks.
CONCLUSIONSThe important challenges that must be addressedby 5G
networks have been highlighted: highercapacity, higher data rate,
lower E2E latency,massive device connectivity, reduced capital
andoperation cost, and consistent QoE provisioning.A 5G
architecture vision to address some of thosechallenges is presented
and a two-layer architec-ture proposed, consisting of a radio
network anda network cloud. The proposed architecture inte-grates
various enablers such as small cells, mas-sive MIMO, C/U-plane
split, NFV, and SDN. Themain concepts can be summarized as follows:
Ultra-dense small cell deployments on
licensed and unlicensed spectrum, underC/U-plane split
architecture, to addresscapacity and data rate challenges
NFV and SDN to provide flexible networkdeployment and operation,
with integratedAS and NAS features
Intelligent use of network data to facilitateoptimal use of
network resources for QoEprovisioning and planningInitial proof of
concept investigations suggest
more than 1000 times throughput gains com-pared to a macro-only
3GPP Release 8 LTEdeployment are achievable by a combination
ofdense deployment of small cells, using largebandwidths at higher
frequency bands andemploying massive MIMO techniques at smallcells.
Nevertheless, some of the componentshighlighted in the system
concept have mutualconflicts when details are considered. Hence,how
to balance the pros and cons of each aspectneeds to be carefully
studied. Further investiga-tions are necessary, particularly in the
followingareas: suitable techniques for use in small cellsin
different frequency regimes; how to incorpo-rate small cells with
NFV and SDN in a cost-effective manner; and intelligent algorithms
thatbetter utilize the available network resources toprovide a
consistent end-user QoE.
REFERENCES[1] Qualcomm, The 1000x Mobile Data Challenge,
White
Paper, Nov 2013. [2] NSN, Signaling is Growing 50% Faster than
Data Traf-
fic, White Paper, 2012. [3] METIS, Scenarios, Requirements and
KPIs for 5G
Mobile and Wireless System (Deliverable D1.1), May2013.
[4] Advanced 5G Network Infrastructure for the FutureInternet
Public Private Partnership in Horizon 2020,2013.
[5] Y. Kishiyama et al., Future Steps of LTE-A: Evolutiontoward
Integration of Local Area and Wide Area Sys-tems, IEEE Wireless
Commun., vol. 20, no. 1, 2013,pp. 1218.
[6] E. G. Larsson et al., Massive MIMO for Next Genera-tion
Wireless Systems, May 2013.
[7] S. Gollakota, S. Perli, and D. Katabi, Interference
Align-ment and Cancellation, ACM SIGCOMM Comp. Com-mun. Rev.,
2009.
[8] A. Benjebbour et al., System-Level Performance ofDownlink
NOMA for Future LTE Enhancements, IEEEGLOBECOM, 2013.
[9] H. Ishii, Y. Kishiyama, and H. Takahashi, A Novel
Archi-tecture for LTE-B: C-plane/U-Plane Split and PhantomCell
Concept, IEEE GLOBECOM Wksps., 2012.
[10] G. Fodor et al., Design Aspects of Network
AssistedDevice-to-Device Communications, IEEE Commun.Mag., vol. 50,
no. 3, 2012, pp. 17077.
[11] G. P. Fettweis, A 5G Wireless CommunicationsVision,
Microwave J., Dec 2012.
[12] B. Farhang-Boroujeny, OFDM Versus Filter Bank
Multi-carrier, IEEE Signal Proc. Mag., vol. 28, no. 3, May2011, pp.
92112.
[13] EARTH Project Work Package 2, Deliverable D2.1: Eco-nomic
and Ecological Impact of ICT,
https://www.ict-earth.eu/publications/deliverables/deliverables.html,2011.
[14] Network Functions Virtualisation - Introductory WhitePaper,
SDN and OpenFlow World Congress, Darm-stadt, Germany, Oct 2012.
[15] EARTH Project Work Package 4, Deliverable D4.3:Final Report
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[16] E. Ternon et al., Database-Aided Energy Savings inNext
Generation Dual Connectivity Heterogeneous Net-works, IEEE WCNC,
Istanbul, Turkey, Apr 2014.
BIOGRAPHIESPATRICK AGYAPONG is a researcher with the wireless
researchgroup at DOCOMO Communications Laboratories Europe.His
research focuses on designing incentive-compatiblealgorithms,
protocols, and architectures to support nextgeneration mobile
communication needs, spanning theareas of resource management,
content distribution, net-work architecture, and business strategy.
He holds a Ph.D.in engineering and public policy from Carnegie
Mellon Uni-versity, Pittsburgh, Pennsylvania. He also holds an
M.Sc. inelectrical engineering and a B.Sc. in electrical
engineeringand computer science, both from Jacobs University
Bre-men, Germany.
MIKIO IWAMURA is a director of the wireless research group
atDOCOMO Communications Laboratories Europe. He receivedhis Ph.D.
and M.Sc. degrees from Kings College London in2006 and the Science
University of Tokyo in 1998, respec-tively. Before his current
role, he was deeply engaged in LTEstandardization, with over 300
contributions to 3GPP. Hehas over 100 patents internationally, and
has published over20 technical journals and conference papers.
DIRK STAEHLE is a manager at DOCOMO CommunicationsLaboratories
Europe. He is responsible for the standardiza-tion team
contributing to the 3GPP SA2 and ETSI NFV stan-dardization groups.
He received his Ph.D. from theUniversity of Wrzburg in 2004 and
continued as an assis-tant professor, leading the wireless network
researchgroup before joining DOCOMO in 2011. His research
activi-ties include NFV, machine-type communication, applicationand
QoE-aware traffic and resource management, andradio network
planning.
WOLFGANG KIESS studied at the Universities of Mannheimand Nice,
and holds a diploma in business informationsystems from the
University of Mannheim and a Ph.D. incomputer science from the
University of Dsseldorf. He isthe leader of the virtualization
research team at DOCO-MO Communications Laboratories Europe,
focusing oncellular core network virtualization, cloud
computing,and 5G.
ANASS BENJEBBOUR [SM] obtained his Ph.D. and M.Sc.
intelecommunications in 2004 and 2001, respectively, andhis Diploma
in electrical engineering in 1999, all fromKyoto University, Japan.
He is currently an assistant man-ager of the 5G team within NTT
DOCOMO, Inc. He servedas 3GPP RAN1 standardization delegate during
LTE Release11, as secretary of the IEICE RCS conference, and
Editorfor IEICE Communications Magazine. He is a Senior Mem-ber of
IEICE.
Initial proof of con-
cept investigations
suggest more than
1000 times through-
put gains compared
to a macro-only
3GPP Release 8 LTE
deployment are
achievable by a com-
bination of dense
deployment of small
cells, using large
bandwidths at higher
frequency bands and
employing massive
MIMO techniques at
small cells.
AGYAPONG_LAYOUT.qxp_Layout 10/29/14 3:33 PM Page 75
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