A 25 Gbps(Km2) Urban Wireless Network

INTRODUCTION

With the emergence of numerous smart mobiledevices such as handheld smart phones and net-books, the data usage on mobile networks isgrowing exponentially. As shown in Fig. 1, it isexpected that the mobile data traffic generatedwill grow more than 50 times compared to theend of 2009 by 2015 and even 500 times by 2020corresponding to about 57 Gbytes per month peraverage subscriber. Sixty Gbytes per month isgenerated by an asymmetric digital subscriberline (ADSL) subscriber at a constant bit rate of2 Mb/s based on the average online time of 136min in 2009. Thus, this expectation means thatmobile users in 2020 will be waiting for a similaruser experience as that of current wireline users.It is anticipated that all Internet applicationsused via fixed Internet access should also be sup-ported on the mobile access platform. Actually,some applications (e.g., social networks) mayeven be accessed more frequently via mobileaccess than fixed access. At the same time, it ispredicted that by 2020 more than 50 billion

devices (compared to 4.9 billion devices at theend of 2009) will be connected with mobilebroadband connections, and every one of us willbe surrounded by an average of 10 devices. Suchexplosive growth of wireless devices also drives anew shift of applications from current man-to-machine communications to future machine-to-machine (M2M) communications. This newparadigm of M2M applications also contributesto the increasing demand for mobile data appli-cations in the future.

The rapidly increasing mobile traffic puts ahuge demand on network capacity and quality ofservice (QoS) in mobile networks. The wirelessnetwork is evolving from the current third-gener-ation (3G) technologies to various fourth-gener-ation (4G) systems such as International MobileTelecommunications (IMT)-Advanced systems.Although IMT-Advanced technologies (e.g.,Long Term Evolution Advanced [LTE-A] in theThird Generation Partnership Project [3GPP])have achieved remarkable capacity increase incomparison with current 3G systems, they stillcannot satisfy the explosive increase of mobiledata traffic projected for 2020. For instance,assuming an average bit rate of 1 Mb/s duringthe busy hour (BH) per user, this implies ademand of average area capacity of 25 Gb/s/km2

in dense urban regions with typical user densityof 25,000 users/km2. To achieve this capacity,about 230 MHz bandwidth is required even ifthe cell average throughput of 3.7 b/s/Hz/cell isachieved as in LTE-A with 200 m intersite dis-tance (ISD) [1]. It is clear that we need a morefundamental breakthrough to increase the wire-less capacity in urban areas beyond LTE-A tech-nology. In this article we discuss various technicalchallenges involved as well as potential advancedtechnologies to achieve the aggressive target of25 Gb/s/km2 area throughput: interference miti-gation techniques, cooperative multiple-inputmultiple-output (MIMO) techniques, and cross-layer self-organizing networks (SONs). In thefollowing we discuss the advantages and techni-cal issues associated with urban small cell deploy-ment and the associated key performance target.Specifically, the urban small cell environmenthas the potential to provide more spatial degrees

IEEE Communications Magazine • February 2011122 0163-6804/11/$25.00 © 2011 IEEE

ABSTRACT

In this article we present a survey on thetechnical challenges of future radio access net-works beyond LTE-Advanced, which could offervery high average area throughput to support ahuge demand for data traffic and high user den-sity with energy-efficient operation. We highlightvarious potential enabling technologies andarchitectures to support the aggressive goal ofaverage area throughput 25 Gb/s/km2 in beyondIMT-Advanced systems. Specifically, we discussthe challenges and solutions from the control-ling/processing perspective, the radio resourcemanagement perspective, and the physical layerperspective for dense urban cell deployment.Using various advanced technologies such asinterference mitigation techniques, MIMO, andcooperative communications as well as cross-layer self-organizing networks, we show thatfuture urban wireless networks could potentiallyoffer high-quality mobile services and offer anexperience similar to the wired Internet.

IMT-ADVANCED AND NEXT-GENERATIONMOBILE NETWORKS

Sheng Liu and Jianjun Wu, Huawei Technologies

Chung Ha Koh and Vincent K. N. Lau, Hong Kong University of Science and Technology

A 25 Gb/s(/km2) Urban Wireless NetworkBeyond IMT-Advanced

KOH LAYOUT 1/19/11 3:35 PM 22

of freedom in the network, which facilitates effi-cient resource reuse, user plane/control planeseparation, and dynamic spatial path selections.We propose two radio access network (RAN)architectures, the cloud RAN and self-organiza-tion RAN, for reducing operation and controlcosts. We also elaborate on various advancedphysical layer techniques such as interferencemitigation techniques and cooperative MIMOcommunications, which contribute to fulfillingthe high demand in future wireless networks.

URBAN DENSE SMALL CELL DEPLOYMENTThere are various advantages associated withsmall cell urban deployment from the networkcapacity and energy efficiency perspectives. Forinstance, small cell deployment allows more effi-cient spatial reuse, which contributes toincreased network capacity. In addition, thedeployment of small cells also has benefits interms of energy efficiency. Energy-efficientdesign for green radio has become a trend forboth handsets and network infrastructure tolower total cost of ownership (TCO) for mobileoperators and CO2 emissions [2]. One exampleis the Green Radio project in Mobile VCE [3],which plans to achieve the goal of 100-foldreduction in power consumption over currentwireless communication networks. Moving theaccess network closer to the user is a keyapproach to reduce the transmit power required.

Although urban small cell deployment hasthe potential to achieve higher system capacity,there are various crucial technical challengesthat must be overcome.

Backhaul and Installation Cost — When thecell size shrinks, the number of radio accesspoints increases tremendously, which leads tohuge increases in backhaul cost and real estatecost of installing the access points. Therefore,cost-efficient backhauling schemes and easy-to-install access points must be considered.

Interference Management — For dense smallcell deployment, intercell interference willbecome more severe than in conventional macro-cellular systems since the cell edge areas and thenumber of interference sources might becomelarger. In macrocell networks MIMO-basedintercell interference mitigation techniques, suchas coordinated multipoint transmission (CoMP),have been proven to be very effective for han-dling interference [4]. However, such schemesmay not be practically feasible in dense smallcell networks due to the increased number ofinterference sources and radio access points.

Mobility Management — A small cell networkimplies frequent handover, which gives rise toheavy signal loading in mobility managementand an increase in the probability of droppedcalls.

Resource Scheduling — The radio propaga-tion environment in a small cell network is verydifferent from that in a macrocell network. Forexample, in a macrocell network the base stationlocations are usually carefully planned, and usersare partitioned into cell center and cell edge

users. However, in a dense small cell networkthe locations of the access points may not be ascarefully planned as in the macrocell scenario,and there is no distinct user partitioning sincethe cell radius is very small, and therefore thedifferences in signal strengths among users maynot be very large. Additionally, in a traditionalmacrocell network there are usually two to fouradjacent cells that dominate the intercell inter-ference, and the impact of remote cells can beignored due to signal attenuation over longpropagation distances. However, in small cellnetworks, interference may be coming not onlyfrom the first tier neighboring cells but also fromthe other cells. As a result, radio resources(power, frequency, time, and space) schedulingand optimization become much more complicat-ed, and conventional centralized solutions maynot be viable in such complex networks.

High Operational Expenditures — The num-ber of nodes in dense small cell networks is sig-nificantly larger than in conventional macrocellnetworks. As a result, the cost of site mainte-nance will be very high if each node does notsupport self-organizing operations such as self-configuration, self-optimization, and self-healing.

Figure 2 illustrates a hierarchical architectureof future dense small cell network, consisting ofdense small cells and macrocells. Specifically,network equipment within the service range of amacrocell base station includes a large numberof small cell access points (SAPs), which arecontrolled by the AP managers. The SAPs mightform a wireless backhaul by connecting withneighboring SAPs via the wireless medium andrelaying traffic for each other. On the otherhand, the SAPs might have centralized controlsignaling according to the control managementpolicy of the capacity enhancement technologies.While the small cell concept has been around fora long time for 2G, 3G, or LTE-A systems, thenotion of small cells in the current systems isquite different from the dense small cell networkwe discussed above. For instance, the notion ofsmall cells in current systems is expected to playa complementary role rather than a major role

Figure 1. Growth of transferred data in Western Europe.

IEEE Communications Magazine • February 2011 123

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IEEE Communications Magazine • February 2011124

in providing capacity and coverage. Hence, thephysical layer signal processing, resource man-agement algorithms, and architecture of currentsystems are not optimized for dense small celldeployment, and these existing technologies can-not deal with the above challenges. In this articlewe elaborate on various advanced enabling tech-nologies to support the vision of dense small cellnetworks.

TARGET PERFORMANCETable 1 summarizes the major target perfor-mances of dense small cell networks. We aim tosupport an average user data rate of up to 1Mb/s, which is similar to the current ADSL-likeuser experience. For a typical user density of25,000 user/km2, this implies an average areacapacity of 25 Gb/s/km2. In addition, we considervarious per-cell performance targets.1 For exam-ple, the target average and peak downlink spec-trum efficiency are about 5.5 b/s/Hz and 45b/s/Hz, respectively. These per-cell targets repre-sent about 50 percent improvement over theLTE-A systems in micro- or picocellular envi-ronments. While these target performances arerather challenging, they could potentially beachievable via various advanced enabling tech-nologies on network architecture, cross-layer

self-organizing networks, as well as advancedinterference mitigation techniques in the physi-cal layer.

RADIO ACCESSNETWORK ARCHITECTURE

The future mobile network is a heterogeneousnetwork that supports macros, picos, femtos,relays, and distributed antenna system (DAS)in the same spectrum. Such a network enableshuge system capacity by allowing vertical cov-erage and optimal usage of local and wide areacells. For indoor applications, femto is an effi-cient scheme to enhance indoor coverage andthroughput. For urban and dense small cellenvironments, it is envisioned that dense smallcells will play a major role in supporting highsystem capacity. The dense small cells shouldbe supported by the control management andmaintenance operations in the RAN architec-ture. A key factor is the availability of abun-dant physical f iber, wavelength-divisionmultiplexed passive optical network (WDM-PON), or ultra-wide band microwave, based onwhich the following two RAN architectures areconsidered.

Centralized processing: For smaller-scalenetworks or when low-cost backhaul transmis-sion resources are widely available, the radiofrequency (RF) processing can be distributed atremote radio units (RRUs) such as SAPs, whilethe baseband and radio resource management(RRM) processing can be centralized at thenetwork controller. Based on this centralizedarchitecture, the system capacity can be maxi-mized with network MIMO signal processingand global RRM. Performance-wise, the cen-tralized RAN architecture is one of the mostefficient ways to overcome the interference andresource management issues in small cell

Figure 2. Small cell network architecture.

AP manager AP manager

User density: 25000 user/km2

Macro-BTS

Smallcell APs SAPs

Network manager

Table 1. System performance targets.

Metric Potential target

Average spectrum efficiency (b/s/Hz) DL: 5.5 b/s/Hz, UL: 3.7 b/s/Hz

Peak spectral efficiency (b/s/Hz) DL: 45 b/s/Hz, UL: 25 b/s/Hz

Peak data rate (b/s/cell) DL: 4.5 Gb/s/cell, UL: 2.5 Gb/s/cell

Average areal capacity (b/s/km2) 25 Gb/s/km2

1 The per-cell metrics(average cell throughput,average spectrum efficien-cy, peak spectral efficien-cy, etc.) are widely usedfor characterizing the sys-tem performance ofmacrocell systems. How-ever, these metrics focusmore on the performancegain achieved by the phys-ical layer signal processingand radio resource controltechniques, and less onthe evolution of networkarchitecture.



deployment. However, the issue is scalabilitydue to the computation complexity and signal-ing overhead involved. Depending on the scaleof the network, cost reduction and load sharingmay be accomplished by cloud computing tech-nology [5].

Distributed processing: For large-scale net-works or when backhaul transmission is expen-sive, distributing some RAN computation andprocessing at the SAPs is preferred. In this casethe RAN consists of dense small cells with com-pact micro/pico base stations, which take care ofthe baseband processing and maybe part of theRRM control, whereas the network controllertakes care of registrations and maintenance ofthe network. The advantage of this architectureis reduced computational and signaling loadingin the network controller, and as a result, thisarchitecture is more scalable. However, the dis-tributive processing requirement poses greatchallenges on the robustness and effectiveness ofthe control algorithms.

In the following we illustrate two implemen-tation examples of centralized and distributiveRANs, the cloud RAN and distributed SON net-works, respectively.

CENTRALIZED CLOUD RANFigure 3a illustrates a typical implementation ofthe cloud RAN [6]. A cloud RAN is a radioaccess network installing many small RRUs andcentralized processing units (CPUs) based onthe software defined radio (SDR) multiprotocolplatform. It uses virtualized baseband processingby combining all radios and computing resourcescheduling. As Fig. 3a shows, the CPUs are con-nected to the RRUs (which correspond to SAPsin urban wireless network) via WDM-PON andcontrol the RRUs based on all the knowledge ofcentralized processing.

The digitalized I/Q radio signal has very highspeed up to several gigabits per second or high-er when MIMO RRUs are used and the band-width is more than 20 MHz. Thus, opticaltransmission is usually necessary for this archi-tecture. In the near future, it is in generalagreed that a WDM-based access network willenable next-generation optical broadbandaccess. In contrast to time-division multiplexing(TDM)-based passive optical network (PON)that offers only tens of megabits per second,WDM-PON will enable the delivery of muchhigher capacity services to subscribers sinceeach optical network unit (ONU) will be servedby a dedicated wavelength channel to communi-cate with the central office or optical line termi-nal (OLT). As a result, the deployment ofcentralized architecture for outdoor applicationswill become popular.

When the signals of distributed antennas areconnected together, joint signal processing andcooperative RRM become easier and more flex-ible. With the rapid development of multicoreprocessors, a cloud-computing-based platformwill be feasible to carry out all physical layerand medium access control (MAC) layer pro-cessing. Because the cell size is small, signalsfrom hundreds or even thousands of cells can becentralized without long-distance optical trans-mission.

DISTRIBUTED SELF-ORGANIZATION RAN

Figure 3b illustrates an example implementationof a distributed SON [5]. The SON is a solutionfor simpler operations and better maintenanceof networks, which gives the network elementsself-organizing functions to allow the system tooperate and configure with less human interven-tion. Distributed SON allows the relative lowernetwork entities to have SON functions, whileon the other hand centralized SON allows onlyupper-level network entities to operate SONfunctions. In urban wireless networks establishedby distributed SON, each SAP operating SONfunctions collects the information about environ-ment changes (e.g., the installation of a newSAP and neighboring SAP actions) and makesself-optimization decisions for mobility robust-ness, energy savings, coverage optimization, andso on. Moreover, the SAPs exchange informa-tion through the interface to help reconfigura-tion of neighboring SAPs.

It can be a key enabler to manage and oper-ate several layers for interworking and givingfaultless service with lower cost. This servicewould be useful in particular for a dense smallcell network comprising a large number of SAPs.The main SON functions include self-configura-tion, self-optimization, and self-healing. Follow-

Figure 3. RAN architectures: a) centralized cloud; b) distributed self-organized.

Small-cellcluster

Small-cellcluster

RRU RRU

RS

WDM-PONCentralizedprocessing unit

(CPU)

(a)

(b)

SON

SON SON

SON

SAPs

Interfacebetween SAPs

CentralizedOAM

Network manager

BTS manager

OAM(Operation andmanagement)

OAM


ing are detailed descriptions of SON functionsand requirements with considerations of urbansmall cells.

Self-Configuration — SAPs should be auto-matically configured to provide wireless servicewhen connecting with a core network. After theinstallation of a new SAP, the automatic config-uration process contains the connection to acore network entity, authentication, and recogni-tion of neighbor SAPs. The initial parameter set-ting for reliable initial service without interferingwith other SAPs and macro-base transceiver sta-tion (BTS) is also an important feature of self-configuration.

Self-Optimization — In order to maximize net-work performance and keep the system’s relia-bility, optimization considers urban environmentcharacteristics of scattered channel, user mobili-ty, and high density. Self-optimization encapsu-lates the procedures of the monitoring mode fordetecting variance, updating the neighbor list,reconfiguring system parameters, and exchang-ing configured information; thus, it should becarefully operated to handle a small load ofwork.

Interaction between Entities — Self-opti-mization of SAPs needs sensing and detectionprocedures of neighbor environments. Thismight lead to establishing interactions betweenSAPs and between an SAP and upper networkequipment, especially when end equipment hasits own SON function. The interface and controlsignaling are significantly necessary to supportthe SON function.

Fault Management — Failure detection andlocalization belongs to self-healing procedures. Ifa failure happens in SON procedures such asnegative effects and false setting of parameters,

SAP and network entities should contain self-disabling capability and self-healing procedures.

RADIO RESOURCE MANAGEMENT INDENSE SMALL CELL NETWORKS

Efficient radio resource management (RRM)schemes for urban wireless networks play animportant role in utilizing the limited radio spec-trum resources. General RRM involves strate-gies and algorithms for controlling parameterssuch as transmit power, data/control channelallocation, and load balancing. In this section wefocus on RRM approaches for controlling adense population environment. In our urbanmodel, typical user density is 25,000 user/km2.For a dense urban environment, it is envisionedthat an efficient distributed approach to resourceallocation will have significant influence onachieving high system capacity.

In this section we describe the advanced fea-tures of RRM adapted to the urban wirelessenvironment. It deals with a plane separationscheme using the hierarchical cell structure ofurban wireless networks. We then introduce traf-fic distribution and caching approaches for effi-cient resource usage that work with high userdensity.

USER PLANE/CONTROL PLANESEPARATED HIERARCHICAL CELL STRUCTURE

Figure 4 shows the proposed separate userplane/control plane hierarchical cell structure,where a macro-BTS and a number of SAPs sharethe same spectrum to form two-tier coverage.Signaling channels as well as traffic channels fordelay-sensitive services of small data volume(e.g., voice over IP [VoIP] and gaming) areoffered by the macro-BTS; meanwhile, otherdata traffic is transmitted by the channels estab-lished by the SAP.

As illustrated in Fig. 4, UE1 has traffic chan-nels offered by SAP1 and SAP2, and a signalingchannel with the macro-BTS. When UE1 moveswithin the coverage of the macro-BTS, only traf-fic channels set up with the SAP may bechanged, but the signaling channel with themacrocell is always connected. Since the signal-ing link is free of frequent handover, signalingload for mobility management can be alleviatedand call drop of real-time services avoided.Thanks to orthogonal frequency-division multi-ple access (OFDMA), if orthogonal time-fre-quency resource blocks are allocated to themacro-BTS andrespectively, the macro and SAPscan share the same spectrum almost withoutinterference.

Moreover, this two-tiered structure has anotheradvantage, i.e., the macro-BTS can provide wire-less backhaul for the small cells. If in-band relay-ing is employed, the SAP actually acts as a relaynode. Since the antenna height of the macro-BTSis high enough, there may be a line-of-sight (LOS)path between the macro and an SAP, which makesthe use of point-to-point microwave links possible.Together with smart traffic distribution and localcontent caching, this structure will greatly reducethe cost of backhauling.


Figure 4. Illustration of user plane/control plane separated hierarchical cellstructure.

SAP 2

SAP 3

UE2

UE1

SAP 1

Microwave or self-backhaul



SMART TRAFFIC DISTRIBUTION ANDLOCAL CONTENT CACHING

Note that mobile data traffic is differentiable. Itis a fact that a lot of traffic is low value-addeddata for Internet services that occupies mostmobile network resources but treats the networkmerely as a pipeline, while only high value-added data such as IP Multimedia System (IMS)services, VoIP, mobile gaming, and mobileTV/music require QoS guarantees and core net-work services.

In addition, most areas where massive mobiledata occurs are convenient to Internet access viaa fixed access network. Usually the backhaul linkto connect RANs and mobile core networks ismore expensive than the links offered bymetropolitan area networks because the back-haul link conveys not only user traffic but alsothe control signaling, so it must be low-latencyand high-security, and even provide special func-tions like timing and frequency reference. Addi-tionally, the backhaul link conveys video service,and web browsing accounts for a big portion ofmobile data traffic. The contents of such servicescan be prefetched or selectively stored close tothe access point so that it can be accessed with-out being redirected from the application serversthroughout the core network.

Hence, we can integrate the deep packetinspection (DPI) function in the base station, sosmart traffic distribution and local contentscaching can be implemented at the RAN side.By offloading the low value-added traffic tofixed access networks, the bandwidth for back-hauling can be effectively reduced. Only a smallpart of data traffic relying on QoS guaranteeand core network services and control signalingpass through the backhaul link to the core net-work. Moreover, contents including web objects,downloadable objects (media files, software, anddocuments), real-time media streams, and so onare fetched from local caches, which implies thatthe load of backhaul can be further alleviated. Infact, smart traffic distribution and local contentcaching also has the benefit of improving QoSand user experience due to reduced delay.

INTERFERENCE MITIGATIONTECHNIQUES IN

DENSE SMALL CELL NETWORKS

In outdoor environments, interference manage-ment and effective transmission schemes are sig-nificant to enhance the capacity, and they shouldbe adapted to the characteristics of urban chan-nels and deployment environments. The desiredpolicies and algorithms for urban outdoor wire-less networks, which are different from indoorand macrocellular environments, should considerthe following aspects.

Usage of a complex propagation environ-ment: In contrast to a macro base station whoseantenna is tower-mounted, the antenna height ofa pico base station is usually 5~10 m. In urbanregions such an antenna height implies numer-ous complicated scatters and shades in the prop-agation paths, so the coverage area of each

picocell becomes very irregular, and they overlapeach other. This results in a complex propaga-tion environment but also brings rich spatialdegrees of freedom, which implies potentialcapacity that can be exploited.

Necessity of global optimization: Due to thecomplex propagation environment as explainedabove, the system model actually becomes a par-tial interference channel for most users. Thus,interference management cannot be handled ineach cell separately, but should be consideredfrom the viewpoint of global optimization.

Interference characteristics: In a dense smallcell network, the separation between outer celland inner cell is not clear due to the small cellradius. Moreover, intercell interference is gener-ated in universe to/from not only a one-tierneighboring cell, but also two- or three-tier cells.

In this section we represent an advancedapproach of dynamic spatial selection adaptingto the urban wireless environment. We thenintroduce various cooperative transmissionapproaches that can overcome limitations ofLTE-A technologies in urban small cell deploy-ment.

DYNAMIC SPATIAL PATH/BEAM SELECTION IN ADENSE SMALL CELL NETWORK

A simple scheme is to avoid interference throughdynamic spatial path selection. As illustrated inFig. 5, UE1 will select path 1 because interfer-ence signals from SAP1 to UE2 are weak enoughthat the interference of SAP1 to UE2 can beignored. Similarly, UE2 selects path 2 under thesame principle. It is actually an altruistic algo-rithm and only feasible when there are manycandidate paths to be selected. In order to applythis algorithm, each UE just reports its worstpath (i.e., from which it hears almost nothing).

In fact, physical-layer-based interference pro-cessing, which highly depends on the accurateestimation of real-time channel parameters vary-ing every frame, usually needs interlinks amongSAPs. However, MAC layer processing onlyrelies on long-term channel average, and thusthe intersite links for cooperation are not neces-sary. Since beamforming is an efficient way toenrich spatial degrees of freedom, coordinatedbeamforming, already adopted in LTE-A [1],can be further used together with the selectionof spatial path (i.e., joint spatial path and beamselection) for interference mitigation.

COOPERATIVE TRANSMISSION INDENSE SMALL CELL NETWORKS

Small cell size brings more spatial degrees offreedom, which facilitates cooperative transmis-sion of multiple network nodes. When the desti-nation is far from the source node, cooperativetransmission has the benefit of capacity by therelaying function of some other nodes, so infor-mation flow is transferred via them instead ofover the direct air link.

One novel technique in LTE-Advanced forcooperation is CoMP, which directly improvescell-edge user throughput by avoiding or elimi-nating intercell interference; however, due tocomplexity it is not a cost-efficient approach to

In a dense small cell

network, the

separation between

outer cell and inner

cell is not clear due

to the small cell

radius. Moreover,

intercell interference

is generated in

universe to/from not

only a one-tier

neighboring cell,

but also two- or

three-tier cells.



combat interference in a dense small cell net-work. Cooperative techniques have the followingadvanced approaches for enhancing capacity andreducing cost overhead in urban wireless net-works.

Cooperative Relaying — Although relayingalso consumes radio resources, it can sti l limprove system capacity due to interferencelocalization. Based on cooperation among mul-tiple relay nodes or relay nodes and the sourcenode, cooperative relaying can further improvesystem throughput and reliabil ity due toexploiting spatial degrees of freedom. Further-more, relay also helps reduce terminal powerconsumption, which is significant to greenradio.

Mobile Terminal Cooperation — At the sametime, the number of subscriptions is increasingdramatically with the rapid development ofmobile computing and the advent of M2M ser-vice. As the mobile terminal becomes more pow-erful, sophisticated cooperative communicationcan be carried out by the terminals. The mobileterminal either acts as a relay node just like arelay station or establishes a direct air link withother terminals to enable device-to-device (D2D)transmission. Thus, we can achieve performanceimprovement in terms of throughput and relia-bility without increased infrastructure cost.Despite the fact that the capacity gain of mobilecooperation diminishes fast with increased dis-tance, mobile cooperation still plays a big role inenabling M2M services in future mobile net-works since sensors must have a very low trans-mit power limit, but handsets or cellular modemsare distributed everywhere and close to thesesensors.

Other Approaches — MAC layer scheduling,including distributed frequency reuse (DFR) anddistributed power control, is also essential forcooperative radio resource management for thedistributed architecture. In a small cell networkthere is no distinct partition of cell center andcell edge users, which means that fractional fre-quency reuse (FFR), succeeding in the macrocellnetwork, may not appropriate for the densesmall cell network. Hence, radio resourcesincluding time, frequency, beam, power, and spa-tial path cannot be scheduled independently byeach cell but preferably in a cooperative way.

PHYSICAL LAYER ENHANCEMENTThere are various advanced physical layer tech-niques introduced in LTE-A systems to enhancethe physical layer data rate for macrocell scenar-ios, such as carrier aggregation and enhancedMIMO. Carrier aggregation supports both con-tiguous and non-contiguous spectra and asym-metric bandwidth for frequency-division duplex(FDD) with maximum 100 MHz aggregatedspectrum in LTE-A systems. However, the effi-ciency of bandwidth aggregation is limited by theavailable spectrum and maximum site powerconstraint. On the other hand, with a maximumof eight antennas equipped, enhanced MIMO inLTE-A systems supports up to 8-stream spatialmultiplexing in both the uplink and downlink.However, in the small cell network, the antennaheight of a pico base station is usually 5~10 m,and there is very limited space for antennamounting, which implies that the number ofantennas for each pico base station is difficult toincrease. In addition, there is a diminishingreturn on spatial multiplexing gain in high orderMIMO when the overhead of pilot preamblesand the associated signaling are taken intoaccount. As a result, we cannot merely rely onthe existing techniques in LTE-A; careful designof physical layer techniques to exploit the uniquecharacteristics of a dense small cell network isneeded to meet the aggressive area capacity infuture networks. In addition to MIMO and carri-er aggregation, the following physical layerenhancement techniques can be effective toboost the spectral efficiency in dense small cellnetworks.

High-order modulation: Exploiting the inter-ference mitigation techniques and small propa-gation loss in a small cell network, mobile usersmay be able to operate at very high signal-to-interference-plus-noise ratio (SINR); hence, wecould exploit very high-order modulation (128-quadrature amplitude modulation [QAM] and256-QAM) to achieve high peak rate and aver-age throughput.

Filter-bank-based multicarrier: In addition,filter-bank-based multicarrier (FBMC) [7] maybe a better choice of multiple access techniquein dense small cell networks instead of OFDMAor SC-FDMA in LTE. The main advantages ofFBMC include very low out-of-band frequencyleakage and cyclic prefix (CP)-free operation.Note that in the user plane/control plane sepa-rated hierarchical cell structure, the macro- andsmall cells share the same spectrum, and thedelay spreading of signals from the small cell is

Figure 5. Illustration of dynamic spatial path selection.

SAP 1

SAP 2

Path 1

Path 2

UE1

UE2



usually small, but that from the macro is rela-tively large. Clearly, the CP length should meetthe delay spreading of signals from the macro-cell, which is a redundancy for the small cell andthus causes a loss in spectral efficiency. Adopt-ing FBMC can overcome this problem at a costof somewhat increased processing complexity.

CONCLUSIONSBased on the above discussions, it is seen thatthe future radio access network should offer veryhigh average areal throughput to support a vastamount of mobile data traffic and user density,and should be energy-efficient. Although wire-less technologies seem to encounter a bottleneckin fundamental limitations, we can still move for-ward with advanced radio access network archi-tecture and novel techniques for small cellnetworks.

REFERENCES[1] T. Abe, “3GPP Self-Evaluation Methodology and Results

— Assumptions” 3GPP TSG-RAN1 tech. rep., NTT DoCo-Mo, 2009.

[2] R. Irmer and S. Chia, “Signal Processing Challenges forFuture Wireless Communications,” Proc. ICASSP, 2009,pp. 3625–28.

[3] Mobile VCE, “CORE-5 Research Area: Green Radio”;http://www.mobilevce.com/infosheets/GreenRadio.pdf.

[4] T. Nakamura, “Proposal for Candidate Radio InterfaceTechnologies for IMT-Advanced Based on LTE Release10 and Beyond (LTE-Advanced),” ITU-R WP 5D 3rdWksp. IMT-Advanced, Oct. 15, 2009.

[5] 3GPP TR 32.821, “Study of Self-Organizing Networks(SON) Related Operations, Administration and Mainte-nance (OAM) for Home Node B (HNB) version 9.0.02009-06,” 3GPP TSG-RAN, 2009.

[6] China Mobile Research Institute, “C-RAN — The RoadTowards Green RAN,” White Paper, v. 1. 0. 0, Apr. 2010.

[7] T. Ihalainen et al., “Filter Bank Based Multi-Mode Multi-ple Access Scheme for Wireless Uplink,” EUSIPCO ‘09,Aug. 2009, pp. 1354–58.

BIOGRAPHIESSHENG LIU ([email protected]) received B.S., M.S., andPh.D. degrees in electrical engineering from the Universityof Electronic Science and Technology of China (UESTC, in1992, 1995, and 1998, respectively. From 2001 to 2005 heserved as a senior architect in UTStarcom Shenzhen R&D

Center, where he worked on RAN architecture and RRMalgorithms. Since 2005 he has been with Huawei Technolo-gies Corporation, where he has led several standardresearch projects including HSPA+, UMB, and 802.16m.Currently he is the system architect of the NG-Wireless Pro-gram in Huawei. His research interests include MIMO,interference alignment, cloud RAN, heterogeneous net-work, and cooperative communications. He is the inventorof 16 U.S. and 50+ China granted patents or patent appli-cations.

JIANJUN WU ([email protected]) graduated fromSouthwest Jiaotong University in April 2001. He joinedHuawei as a wireless engineer in 2001. From 2001 till 2003he was engaged in the development of NodeB for WCDMAsystems, and during this time he also designed and devel-oped smart antenna for WCDMA and CDMA2000 systems.From 2003 to 2004 he worked on the Huawei B3G projectas a system engineer, responsible for system design. SinceJuly 2005 he has led Huawei’s WIMAX Research project,and was responsible for the standard research within theIEEE and WiMAX Forum. Since May 2007 he has beenresponsible for system and architecture evolution research.

CHUNG HA KOH ([email protected]) received her B.S., M.S.,and Ph.D. degrees from the Department of Electrical andElectronic Engineering of Yonsei Univerisy, Seoul, Korea, in2004, 2006, and 2010, respectively. Since May 2010 shehas been with Hong Kong University of Science and Tech-nology (HKUST) as a research associate. Her currentresearch interests are resource allocation, self-organizingnetworks, cross layer optimization, delay optimal systems,and femtocell protocols.

VINCENT K. N. LAU ([email protected]) received his B.Eng.(Distinction 1st Hons — ranked 2nd) from the Departmentof Electrical and Electronics Engineering, University ofHong Kong in 1992. He joined the Hong Kong Telecom(PCCW) after graduation for three years as system engi-neer, responsible for transmission systems design. Heobtained the Sir Edward Youde Memorial Fellowship,Rotoract Scholarship, and Croucher Foundation in 1995and went to the University of Cambridge for a Ph.D. inmobile communications. He completed his Ph.D. degree intwo years and joined Bell Labs — Lucent Technologies,New Jersey, in 1997 as a member of technical staff. He hasbeen working on various advanced wireless technologiessuch as IS95, 3G1X, and UMTS, as well as wideband CDMAbase station ASIC design and p 3G technologies, such asMIMO and HSDPA. He joined the Department of ECE,HKUST as an associate professor in August 2004 and waspromoted to professor in July 2010. He has also been thetechnology advisor and consultant for a number of compa-nies such as ZTE and Huawei, ASTRI, leading several R&Dprojects on B3G, WiMAX, and cognitive radio. He is thefounder and co-director of the Huawei–HKUST InnovationLab.

Although wireless

technologies seem to

encounter a

bottleneck in

fundamental limita-

tions, we can still

move forward with

advanced radio

access network

architecture and

novel techniques for

small cell networks.


A 25 Gbps(Km2) Urban Wireless Network

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