communicating in the real world: 3d mimo

IEEE Wireless Communications • August 2014136 1536-1284/14/$25.00 © 2014 IEEE

Xiang Cheng is withPeking University.

Bo Yu and Liuqing Yangis with Colorado StateUniversity.

Jianhua Zhang is withBeijing University of Postsand Telecommunications.

Guangyi Liu is withChina Mobile Communi-cations Corporation.

Yong Wu and Lei Wanare with Huawei Tech-nologies Company Ltd.

AC C E P T E D F R O M OP E N CALL

ABSTRACTSpectrum efficiency has long been at the cen-

ter of mobile communication research, develop-ment, and operation. Today it is even more sowith the explosive popularity of the mobile Inter-net, social networks, and smart phones that aremore powerful than our desktops used to be notlong ago. The discovery of spatial multiplexingvia multiple antennas in the mid-1990s hasbrought new hope to boosting data rates regard-less of the limited bandwidth. To further realizethe potential of spatial multiplexing, the nextleap will be accounting for the three-dimensionalreal world in which electromagnetic waves prop-agate. In this article we discuss fundamentalsand key technical issues in developing and realiz-ing 3D multi-input multi-output technology fornext generation mobile communications.

INTRODUCTION

“When you have a scarce resource, an industry runas an oligopoly and a population that can’t getenough, you have all the ingredients for the firstnew resource crisis of the millennium.”

No, not oil. This excerpt from a 2010 TimeMagazine article is all about the wireless spec-trum. With the explosive increase of data-hungryservices including Facebook and Twitter, as wellas always-connected smart mobile devices, thisstatement is edging ever closer to reality.

In the 1990s Foschini and Telatar firstrevealed that the channel capacity of multi-antenna systems increases linearly with the num-ber of transmit/receive antennas, thus givinghope to meeting the unlimited data demand ofthe real world with the limited wireless spectrum(e.g. [1, 2] and references therein). Today it hasbecome standard to equip base stations (BSs)with antenna arrays that facilitate various multi-input multi-output (MIMO) functionalitiesincluding beamforming, multiplexing, diversity,and interference coordination, just to name afew. In these existing cellular systems, however,the BS antenna array has remained passive andcan only adjust the beam in the horizontaldimension with fixed downtilt angle, based onthe horizontal channel information.

On the one hand the real-world channel fea-tures three-dimensional (3D) characteristics,rendering two-dimensional (2D) MIMO tech-

niques suboptimum. On the other hand, thecapability of tilting the transmit beam angle inthe full 3D space will intuitively improve theoverall system throughput and interference man-agement, especially for scenarios where mobileusers are distributed in a 3D space with distin-guishable elevation such as modern urban envi-ronments. The latter case is becoming increasinglyimportant with the prevalence of the small cellconcept, in which the horizontal scale becomesmore comparable with the vertical scale.

Not surprisingly the 3D MIMO concept isembraced by various mainstream communica-tions systems (long term evolution (LTE), LTE-advanced and beyond) thanks to its potential toboost system capacity and alleviate interference,both of which consist of the most important sys-tem development objectives. One core enablingtechnology for 3D MIMO is the so called activeantenna system (AAS). The employment of AASat BSs was recently approved by the 3rd Genera-tion Partnership Project (3GPP) at TSG RAN#53 in September 2011. AAS technology inte-grates radio frequency components (poweramplifiers and transceivers) with the antennaelements. In this manner the phase and ampli-tude of the signals from each antenna elementcan be electronically controlled, thus facilitatingmore flexible and intelligent beamforming,resulting in increased capacity and coverage.With a 2D or 3D AAS array at the BS, theantenna radiation pattern can be dynamicallycontrolled in both horizontal and vertical dimen-sions, thus enabling 3D MIMO as opposed tothe conventional 2D MIMO. The AAS-enabled3D MIMO is attracting significant attentionfrom academic researchers as well as industrialdevelopers and operators. In this article we pro-vide the overview and perspective of 3D MIMOtechnology. We will start from fundamentals andapplication scenarios, followed by discussions on3D MIMO channels and an overview of keytechnological issues. We conclude the articlewith opportunities and challenges in 3D MIMOresearch and development.

FUNDAMENTALS ANDAPPLICATION SCENARIOS

At conventional BSs linear (1D) antenna arrayswith fixed radiation patterns in the verticaldomain are used. The transmitted beamwidth in

XIANG CHENG, BO YU, LIUQING YANG, JIANHUA ZHANG, GUANGYI LIU, YONG WU, AND LEI WAN

COMMUNICATING IN THE REAL WORLD: 3D MIMO

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IEEE Wireless Communications • August 2014 137

the vertical dimension and the antenna downtiltare usually fixed. Occasional adjustment of thedowntilt angle can be achieved physically by, forexample, Remote Electrical Tilt (RET) devicesto direct the main lobe of the antenna responsetoward the ground. As a result the spatial free-dom only lies in the azimuth dimension essen-tially in terms of the horizontal radiation beampattern and width. With the deployment of 2Dor 3D AAS at the BS, the elevation and azimuthsteering angles, the beamwidth, and the radia-tion pattern can all be dynamically controlled infull dimensions by adaptively weighting the ele-ments in the antenna array, potentially for eachindividual user equipment (UE), thus facilitatingthe so called 3D MIMO technology.

Fully utilizing both the horizontal and verti-cal dimensions, 3D MIMO is particularly suit-able for scenarios with vertical user locationdistributions. One example is the dense urbanarea (including both residential areas and cen-tral business districts) with many high-rise build-ings. There is usually a huge demand for mobiledata capacity in these areas. Furthermore, theindoor users in these areas are usually locatedon different floors of high-rise buildings and thevertical user distribution is evident. Transmis-sions from outdoor BSs to users located on dif-ferent floors can be better separated in theirelevation angles. Hence, significant gain in sys-tem performance can be expected with 3DMIMO. Another important scenario is the densepopulation area where a huge number of usersare closely located with each other and connect-ed to one BS in a limited area. Examples includetransportation stations, shopping malls, stadi-ums, and so on. In this scenario a great amountof traffic will be generated simultaneously andthe data rate of each user will be degraded. 3DMIMO will provide a good solution for thisproblem. For suburban and rural scenarios, ele-vation beamforming can also be beneficial interms of the cell range expansion achieved byvertical sectorization.

To date 3D beamforming is by far the moststudied 3D MIMO technique, thanks to its rela-tive simplicity, flexibility, and effectiveness. Anexample is shown in Fig. 1, where a single widebeam covering the entire cell with a fixed down-tilt angle is replaced by multiple simultaneousnarrower beams sectoring the cell with dynamicdowntilt angles. Essentially a kind of physicalbeamforming technique without any channelstate information (CSI), 3D beamforming comesat low complexity. The granularity of the verticalsectorization enabled by 3D beamforming can betheoretically adjusted to different levels fromvery coarse (two to three sectors in the radialdirection) to very fine UE-specific (e.g. [3]),leading to maximized UE signal strength as wellas highly flexible interference management.

Another potential technology is the extensionof spatial multiplexing from 2D MIMO to the3D MIMO regime. Different from 3D beam-forming, spatial multiplexing requires instanta-neous or statistical CSI at the Tx (CSIT), and isinherently UE-specific. Based on the CSIT, thespatial multiplexing gain is exploited to improvethe system performance by optimizing each UE’sspecific precoding coefficients. There are two

main categories of such designs, namely single-user (SU-)MIMO and multi-user (MU-)MIMO.For SU-MIMO, the optimal beam directions aresimply the channel eigen-directions. However,MU-MIMO inherently accounts for multi-userinterference and is thus more suitable for cellu-lar applications.

In addition to these multiplexing-orientedtechniques, 3D MIMO also has the potential tofacilitate enhanced spatial diversity via moresophisticated antenna deployment. However, ithas been long understood that exploiting diversi-ty gains implies sacrificing multiplexing gains.We will focus on multiplexing-oriented tech-niques in this article.

Interference coordination has been tradition-ally one main concern since the invention of thecellular concept in the last century. These vari-ous 3D MIMO techniques pose unprecedentedopportunities as well as unique challenges to thislong-standing problem, hence stimulatingresearch and development innovations. Suchefforts inevitably call for 3D MIMO channelmeasurements and modeling. These measure-ment-based channel models are not only indis-pensable for the development and verification of3D MIMO technology, but also critical in justify-ing the benefit of 3D MIMO with respect to its2D counterpart, because the propagation envi-ronment in the real world is inherently 3D,hence comparisons can only be fair using suchchannel models.

CHANNEL MEASUREMENTS AND MODELINGThe complete process of channel measurementsand modeling includes the following steps:1) Channel measurement.2) Raw data post-processing.3) Channel modeling and simulation.

These steps are intimately interlaced. Forexample, the last step consisting of data analysisand channel modeling may gain useful informa-tion in revising measurement campaigns in thefirst step. For raw data post-processing, spatialalternating generalized expectation-maximization(SAGE) has been widely adopted as one of themost popular channel estimation algorithms,thanks to its high accuracy, capability of estimat-ing channel parameters, and applicability toalmost every type of antenna array. In this sec-tion we will summarize and report some recent3D MIMO channel measurement and modelingefforts.

Figure 1. 2D vs 3D BF.

(a) 2D BF with fixed downtilt (b) 3D BF with dynamic downtilt

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IEEE Wireless Communications • August 2014138

CHANNEL MEASUREMENTS

Unlike the 2D MIMO, 3D MIMO channel mea-surement is still at a v ery early stage due to theelevated requirement on the measurementequipment. Currently only a few qualified chan-nel sounders are available, for example, thePropSound channel sounder by Elektrobit andthe RUSK MIMO channel sounder by Medav.More recently one of the leading mobile devicemanufacturers, Huawei, plans to design and pro-duce their own channel sounder for 3D MIMOmeasurements. In the meantime several 3DMIMO measurement campaigns have beenrecently conducted and others are on the way toinvestigate the 3D MIMO propagation channelsfor various application scenarios (e.g. [4, 5] andthe references therein). These mostly focus onthe elevation related channel parameters, forexample, elevation angles of departure (EAOD)and elevation angles of arrival (EAOA), whereastheir impact on other important parameters, forexample, polarization, Doppler, and power delayprofile, have not yet received much attention.

These recent measurement campaigns use 2,3.5, and 5 GHz as carrier frequencies, near-staticscenarios including outdoor and outdoor-to-indoor (O2I) environments with both line-of-sight (LoS) and non-LoS conditions, and aretypically wideband from 30 to 100 MHz. Recent-ly, led by China Mobile Communications Corpo-ration (CMCC) and Beijing University of Postsand Telecommunications (BUPT), a new 3DMIMO measurement campaign based on Prop-Sound channel sounder at 3.5 GHz with 100MHz bandwidth has been conducted for typicalUrban Macro (UMa), Urban Micro (UMi), andO2I scenarios in Beijing, China [4]. To collectdata with accurate 3D spatial information, spe-cially designed Tx and Rx antennas are needed.In this measurement a uniform planner array(UPA) with 32 elements was used at the BS Tx,while a three dimensional omni-directional array

(ODA) with 56 antennas was installed at themobile terminals as the Rx, as depicted in Fig. 2.This measurement revealed that both EAODand EAOA distributions can be well fitted bythe Laplacian distribution [5].

CHANNEL MODELINGBased on the understanding of 3D MIMO prop-agation characteristics via either theoreticalanalyses and/or channel measurements, one candevelop accurate yet easy-to-use cannel models.We classify existing 3D MIMO channel modelsin terms of their respective modeling approach-es. As summarized in Fig. 3 the two basic cate-gories are deterministic versus stochastic models.

Deterministic channel models characterize 3DMIMO channel parameters in a purely determin-istic manner. This category can be further classi-fied into the geometry-based deterministic model(GBDM) and the finite-difference time-domain(FDTD) model. Both methods need an accuratedatabase, high computation time, and use approx-imations of Maxwell’s equation for their solution,while the former is in general based on ray-trac-ing, which exploits the high-frequency approxi-mation of Maxwell’s equation, and thus needs adetailed and time-consuming description of thesite-specific propagation scenarios. Therefore thedeterministic channel model typically has highcomplexity and cannot be easily generalized.

Stochastic channel models determine thephysical parameters in a stochastic manner withor without presuming any underlying geometryand thus can be easily used to deal with variousscenarios. These models can be further classifiedinto the correlation-based model (CBM), thegeometry-based stochastic model (GBSM), andthe measurement-based pseudo-geometric model(MBPGM). The CBM characterizes the 3DMIMO channel matrix statistically in terms of thecorrelation among the matrix elements. There-fore the CBM aims at obtaining the spatial chan-nel correlation function and then generates thechannel response or some statistical properties ofthe channel based on such correlation function.Popular correlation-based analytical channelmodels are the Kronecker model and the Weich-selberger model. It is an analytical model andthus comes with low complexity and could bereadily used in theoretical analysis as well as sys-tematic design of the 3D MIMO technology.

The GBSM is derived from some predefinedstochastic distribution of the scatterers/clusters byapplying the fundamental laws of wave propagation[6]. Such models can be easily adapted to diversescenarios by modifying the stochastic distributionand properties of scatterers/clusters and the shapeof the scattering region. GBSMs can be furtherclassified into regular-shaped GBSMs (RS-GBSMs)and irregular-shaped GBSMs (IS-GBSMs) depend-ing on whether scatterers/clusters are placed onregular shapes, for example, two-sphere and two-cylinder, or irregular shapes, as illustrated in Fig. 3.Its direct involvement of scatterers/clusters rendersGBSM one of the most promising candidates for3D MIMO channel modeling.

The MBPGM is entirely based on channelmeasurements. Examples of MBPGM includethe widely used SCM and WINNER models [7],though both are often mistakenly referred to as

Figure 2. Details of (a) UPA antenna and (b) ODA antenna.

Item

Antenna type

Element number

Polarization

Spacing

Arrangement of elements

Angle rangeAzimuth

Elevation

UPA

32

+–45 degree

0.5 wavelength

Planar

–70°~70°–70°~70°

Value

ODA

56

+–45 degree

0.5 wavelength

Cylinder

–180°~180°–55°~90°

(a) (b)(b)



GBSM. Such misunderstanding is largely due tothe fact that the main channel parameters are allrelated to scatterers/clusters, for example, angleof arrival/departure and angle spread, as depict-ed in Fig. 3. However, notice that all these scat-terer/cluster-related parameters are actuallyobtained solely based on measurements, insteadof predefined stochastic distributions of the scat-terers/clusters. Therefore, both SCM and WINNERmodels are more properly classified as MBPGM,and should be distinguished from GBSM.

So far MBPGMs, for example, SCM and WIN-NER models, are the most popular models for3D MIMO channels. After the elevation-relatedparameters are obtained from measurements, forexample, the Laplacian distribution parameters ofthe elevation angle, 2D SCM and WINNER mod-els can be readily extended to 3D models as dis-cussed in [7]. The extended 3D SCM model isused for beamforming simulations in the follow-ing section using Huawei’s system-level simulator.Two-cylinder RS-GBSM is used to show theimpact of some important parameters on 3DMIMO channel capacity. In these simulations weuse a 5.25 GHz carrier frequency, 91 Hz maxi-mum Doppler frequency, 104 Hz sampling rate.We consider two receive and two transmit anten-na elements with antenna element spacing of halfwavelength, SNR = 3 dB, and uniformly dis-tributed azimuth angle of arrival (AAOA) andazimuth angle of departure (AAOD). BothEAOA and EAOD follow Laplacian distributionswith variance 1. As shown in Fig. 4 this modelinvolves an LoS component with Ricean factor K,single- and double-bounce components with ener-gy-related parameters ηS and ηD that specifyhow much the single- and double-bounced rayscontribute to the total scattered power and thusηS + ηD = 1. From Fig. 4 it is clear that theincrease of elevation angle spread results in anincrease of the channel capacity, which agreeswith our intuition. Figure 4 also demonstratesthat the increase of ηD results in an increase of3D MIMO channel capacity. This is because a

larger ηD value implies richer scatterers in the3D environment, and thus smaller spatial correla-tion. Moreover, the channel capacity decreaseswith the increase of K, which is because larger Kvalues also lead to smaller spatial correlation. Webelieve these results will provide guidance forfuture channel measurement campaigns.

KEY 3D MIMO TECHNOLOGIES

In this section we give an overview of the key 3DMIMO technologies as briefly mentioned above,namely the CSI-independent 3D beamforming,CSI-dependent spatial multiplexing includingSU-MIMO and MU-MIMO techniques, as wellas interference coordination techniques.

3D BEAMFORMINGTo demonstrate the benefit of dynamic 3D beam-forming let us start with a simple simulation. Weconsider a snapshot in the LTE small cell net-work scenario, in which the small cells are inde-pendently deployed following a uniformdistribution, while the UEs are deployed non-uniformly in different hotspots. We considerthree different antenna tilting schemes: 2Dbeamforming with fixed tilting at 6°, cell-specific3D beamforming, and UE-specific 3D beam-forming. For cell-specific beamforming the anten-na tilt angle for each BS is adjusted adaptivelyaccording to the specific cell coverage area, whilefor UE-specific beamforming the main lobe ofthe antenna beam is steered directly to the sched-uled user location for each BS in each sub-frame.Note that in the majority of literature on thissubject the weighting vectors used to achieve thedesirable beamforming angles are obtained basedon only the location of the UEs and no CSI isused (e.g. [3, 8].) The BS can determine the UElocation by estimating the direction of arrival(DoA) of the received uplink signal with somespecialized algorithms. The signal-to-interfer-ence-and-noise ratio (SINR) and user throughputcomparisons are shown in Figs. 5a and 5b, respec-

Figure 3. Classification of 3D MIMO channel models.

Ray-tracing method

high complexity

3D MIMO channel model

Deterministicmodel

Two-cylinder

RS-GBSM

Two-sphere

RS-GBSM

GBDM

High complexity

FDTD

Kronecker,

Weichselberger

Stochasticmodel

CBM

SCM, winner

MBPGM

Directly deal withscatterers/clusters

GBSM RS-GBSM

IS-GBSM



tively. It is observed that UE-specific 3D beam-forming tilting provides the best performance interms of both metrics.

With the capability of dynamic vertical beamadaptation, different kinds of 3D beamformingcan be realized to exploit the added degree offreedom, in contrast to the conventional 2Dcounterpart where the beamforming is done onlyin the horizontal plane. Generally the verticalbeam adaptation schemes can be classified intotwo categories: one is cell splitting with verticalsectorization, the other is UE-specific antennadowntilt adaptation. An example of vertical sec-torization is that a single cell can be split intotwo cells by generating two separated verticalbeams with different downtilts and beam width.Each cell will be served by a vertical beam sepa-rately. The simulation results in [9] show that3 × 2 sectorization (three horizontal sectors, twovertical sectors per horizontal sector) can pro-vide significant capacity gain.

For UE-specific antenna downtilt adaptation,more complicated processing is required sincethe antenna downtilt is adjusted for each UEsuch that the main lobe of the vertical beam issteered directly to the specific UE. In this casethe received signal power at the scheduled UE ismaximized. However the interference generateddepends on the location of the UE and thebeamwidth. The field trial in [10] considers asimple scenario with two BSs and five UEs. Thedowntilts are adjusted according to the locationof each UE, while the vertical half-powerbeamwidth (HPBW) for the two BSs are 6.2° and7.5°, respectively. The measurement results showthat the UE-specific downtilt adaptation canincrease the signal-to-interference ratio (SIR) atdifferent UE locations by about 5-10 dB com-

pared to a system with fixed downtilt at the BSs.A thorough comparison among various verticalsectorization schemes and the UE-specific down-tilt adaptation scheme has been conducted in [3].The results show that cell edge throughput bene-fits from vertical sectorization due to reducedinterference at the cell edge, whereas UE-specificdowntilt adaptation with limitation of minimumdowntilt boosts spectral efficiency since the signalpower at the scheduled UE is always maximized.

It is worth noting that for the added vertical-dimension beamforming the adjustment range ofthe vertical beam pattern is usually much lessthan in the horizontal direction, and the verticalbeamwidth is also much narrower than the hori-zontal beamwidth. For example, in a typicalmacro cell scenario (according to 3GPP case 1)the antenna downtilt is 15° and the vertical half-power beamwidth (HPBW) is 10°, while the hor-izontal HPBW is 70°. This means that muchfiner and more accurate control of the verticalbeam pattern is highly preferred in order toimprove vertical beam separation and achievefine granularity UE-specific vertical beam adap-tation. The fine granularity beamforming can berealized by employing an active 2D or 3D antennaarray with a large number of radiation elements.

SPATIAL MULTIPLEXINGWith the degrees of freedom provided in thespatial dimension of the MIMO channel, multi-ple data streams can be spatially multiplexedonto the MIMO channel for simultaneous trans-mission, giving rise to the so called spatial multi-plexing gain. MIMO precoding is a transmitprocessing technique that utilizes CSIT to exploitthe spatial multiplexing gain for improved sys-tem spectral efficiency. It can be regarded as a

Figure 4. Two-cylinder RS-GBSM and simulation results of the impact of key channel parameters on 3D MIMO channel capacity.

Elevation spread2010

2.555

Ergo

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city

2.56

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30 40 50 60 70 80

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K=0, Elevation distribution: LaplacianAzimuth distribution at Tx: LaplacianElevation spread=30°, mean elevation=0°

ηS=0, ηD=1, Elevation distribution: LaplacianAzimuth distribution at Tx: LaplacianElevation spread=30°, mean elevation=0°

ηS=0, ηD=1, K=0Azimuth distribution at Tx: LaplacianElevation distribution: LaplacianMean elevation=0

0.4 0.5 0.6 0.7

LoSTXm RXp

RXqTXnOi Or

Double-bounce

Single-bounce

Single-bounce



high resolution channel eigen-space beamform-ing to each UE, in contrast to the 3D physicalbeamforming discussed in the last section.

A proof-of-concept dynamic system-level sim-ulation is conducted and the evaluation resultsare shown in Fig. 6. The scenario follows 3GPPcase 1, where 19 cell sites (three sectors per cellsite) are deployed in a hexagon grid with anaverage of 10 UEs per sector on the ground.The fast fading channel is generated using the3GPP 2D SCM model, with the elevation dimen-sion taken into consideration. The BS side verti-cal angle spread is 6°. A two-column ±45°linearly cross-polarized rectangular array is con-figured at the BSs with mechanical rotation of15°. A cross-polarized two-element array with0°/90° polarization is configured at the UEs. Allantenna elements are separated by half wave-length. 2D MIMO and 3D MIMO are comparedin both SU and MU scenarios. For the MUtransmission, the maximum paired UE numberis 4. For 2D MIMO only the linear four horizon-tal ports are active and the antenna downtilt isthus fixed and equal to the mechanical tilt. For3D MIMO the 4 × 8 rectangular array is activeand the antenna downtilt (as well as the shape ofthe array pattern) is adjusted for each UEaccording to the user channel condition. Thecomparison results for cell average spectral effi-ciency (SE) and cell edge SE are depicted inFigs. 6a and 6b, respectively. We can observe thatfor SU-MIMO a 12 percent gain is achieved forcell average SE, while the gain for cell edge SEis very limited. For MU-MIMO remarkablegains are achieved for both cell average SE(43 percent) and cell edge SE (41 percent).

Conventional 2D MIMO precoding has attract-ed much attention during the last decade and bothlinear and nonlinear precoding schemes based ondifferent criteria have been proposed in the litera-ture (e.g. [1, 11] and the references therein). Inparticular, linear precoding schemes provide a sim-ple and efficient way to utilize CSIT and achieve

desirable trade-offs between performance andcomplexity. Theoretically, for SU-MIMO it is wellknown that the optimal beam directions are thechannel eigen-directions given the perfect CSIT.For MU-MIMO a number of precoding schemeshave also been proposed in the literature (e.g. [11]and the references therein), including zero-forcing,block diagonalization, and so on. Most precodingschemes can achieve considerable spatial multi-plexing gain even with imperfect CSIT, especiallyfor MU-MIMO. A detailed description of themain features of MU-MIMO techniques adoptedin LTE and LTE-A standards is provided in [12].In the latest enhancement to MU-MIMO in LTE-A a maximum of eight transmit antennas areallowed to support simultaneous transmission ofup to eight layers, and no more than four UEs canbe co-scheduled considering the trade-off betweenperformance and signaling overhead.

These conventional 2D MIMO precodingschemes may be readily applicable to the 3DMIMO scenario, at least in theory. However,unique features of 3D MIMO need to be takeninto account when designing 3D MIMO precodingschemes for both optimality and complexity con-cerns. First, with an added dimension in verticaldomain the 3D channel model is inherently differ-ent from the conventional 2D channel model,which may result in different precoding design prin-ciples. Second, the AAS-enabled 3D MIMO canpotentially have very large size 2D or 3D antennaarrays at BSs, which will render signal processingon both Tx and Rx sides prohibitively complex.

An additional concern for frequency divisionduplex (FDD) systems is the excess feedbackoverhead in obtaining the CSIT for such large-scale antenna arrays. One approach to avoid thefeedback overhead is to design a codebook con-taining all possible quantized beamforming vec-tors. The codebook can be designed off-line andis known to both the Tx and Rx. The Rx can thenchoose the best beamforming vector from thecodebook with different selection criteria based

Figure 5. SINR and user throughput comparison between 2D vs 3D beamforming.

SINR (dB)

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2D with fixed tilting at 6 degrees3D cell-specific tilting3D UE-specific tilting

2D with fixed tilting at 6 degrees3D cell-specific tilting3D UE-specific tilting



on the instantaneous channel knowledge and sendthe index back to the BS via a limited feedbackchannel. In this case the system performancedepends heavily on the codebook quantizationerror, which is caused by the mismatch betweenthe codebook space and the actual channel space.To a large extent the actual channel spacedepends on the antenna response of the antennaarrays. In the conventional 2D scenario the code-book that is designed based on linear antennaarrays thus cannot be directly adopted in the 3Dscenario with 2D or 3D antenna arrays. In addi-tion the large-scale antenna arrays may result inhigh dimensionality of the channel eigen-space,which in turn results in a large codebook size andfurther increases the feedback overhead.

On the other hand, for time division duplex(TDD) systems no feedback is needed since theCSIT can be obtained by channel reciprocity.However, the pilot contamination problememerges and the effect is especially significantfor 3D MIMO with the incorporation of large-scale 2D or 3D antenna arrays. Some suggestthat pilot contamination is the limiting factor inthe asymptotic sense as the number of antennasapproaches infinity. The common understandingis that some level of coordination accounting forthis problem is a necessity to ensure reasonablegain as the antenna array grows in size. Forexample, the optimal allocation and coordina-tion of pilot sequences at the UE would be high-ly desirable. An insightful survey on severalworks proposed to deal with the pilot contami-nation problem can be found in [13].

INTERFERENCE COORDINATIONInterference is always a major obstacle in achiev-ing higher spectral efficiency. With 3D beam-forming the situation becomes more complicatedin comparison with 2D beamforming. With thelatter the vertical beam direction cannot be

dynamically adjusted to maximize the receivedsignal power for each UE. However, this alsoconfines most of the interference from leakingto the neighboring cells. On the other hand, withthe capability of vertical beamsteering 3D beam-forming can dynamically adjust the beam direc-tion according to the location of each UE suchthat the received signal power for each UE canbe maximized. However, the generated inter-cellinterference can be very complicated. Thereforeit is not straightforward whether the overall sys-tem performance (spectral efficiency as well ascell edge user throughput) can be improved.

In the existing literature there is a lack ofsolid analytical study on this problem. The simu-lated study in [3] shows that 3D beamformingwith direct steering of the vertical beam towardthe UE without any downtilt limitation performsbetter than fixed downtilt beamforming in termsof cell edge user throughput, but worse in termsof the overall spectral efficiency. Hence a morethorough investigation is needed to exploit theadditional degree of freedom provided by thevertical dimension in order to manage the inter-ference more flexibly and effectively. For exam-ple, advanced beam coordination methods maybe a good option. It is also possible to combine3D beamforming with existing interference man-agement schemes in the literature, such as vari-ous coordinated multipoint transmission/reception (CoMP) techniques [14] and inter-cellinterference coordination (ICIC) methods [15].For 3D MU-MIMO, coordination can even beachieved at the precoding stage across BSs.

OPPORTUNITIES AND CHALLENGES

Still at the launching stage, 3D MIMO researchand development are not short of challenges andopportunities. Next we will discuss some of theseopen issues from various perspectives.

Figure 6. Cell average and cell edge SE comparison between 2D and 3D MIMO.

(a)SU-MIMO

1

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Cel

l ave

rage

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/Hz)

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MEASUREMENTS AND CHANNEL MODELING

So far 3D MIMO channel measurements aremainly conducted for UMa, UMi, and O2I sce-narios; more measurement campaigns are need-ed for indoor hotspot scenarios. Furthermore,more comprehensive 3D MIMO channel mea-surement campaigns are needed to investigatethe impact of 3D spatial environments on a widerange of key channel characteristics, for exampletime variation, stationarity, polarization, and soon, for various scenarios.

Currently MBPGM has been widely used foranalyzing and evaluating 3D MIMO technolo-gies both in academia and industry. For exam-ple, the 3D SCM model extended from thestandardized 2D SCM model. However, currentMBPGMs only take the elevation-related param-eters into consideration. While the impacts ofelevation angle on other important channelparameters, for example Doppler, non-stationar-ity, polarization, and so on, have not been con-sidered. Based on further channel measure-ments, these impacts should be modeled forfuture MBPGMs.

On the other hand, considering that 3DMIMO technology will fully explore the 3D spa-tial environments that consist of scatterers,GBSMs should be used for accurate modelingand thus facilitating improved designs of 3DMIMO technology. Based on dedicated channelmeasurements and analysis, placing scatterers/clusters with certain distributions around the Txand Rx in the 3D environments, setting theirproperties, and collecting all the received raysfrom greatly-simplified ray tracing to build real3D MIMO GBSMs will be a future researchdirection for accurate 3D MIMO channel mod-eling. In addition, CBMs will be useful to bringus insight on how the different spatial correla-tion characteristics between 3D MIMO and 2DMIMO channels can be used for optimum designof 3D MIMO precoding schemes.

KEY 3D MIMO TECHNOLOGIESSo far the effects of the 3D beamforming tech-niques have been studied mainly by system-levelsimulations and field trial evaluations in verysimple scenarios. There is an urgent need foranalytical study of the 3D beamforming design,in terms of how to optimally determine the cellborder for cell splitting, what would be the opti-mal beam pattern and downtilt for each cell, andso on. More rigorous formulation of the opti-mization problem, together with careful analysisof the complexity-granularity trade-off, will sig-nificantly assist the massive deployment of 3DMIMO technology.

For 3D beamforming, in order to realizemore fine granularity beamforming in the verti-cal domain, the number of antenna elementsrequired can be very large, which will pose greatchallenges. First, the number of active antennaarray elements that can be equipped at a BS isusually limited by the BS form factor. As a result,small-scale and more cost-efficient hardwaredesign becomes challenging, especially for theelectromagnetic elements, such as duplexers andfilters, which are very difficult to be miniaturizeddue to the physical constraints, including wave-

length, current densities, conductivity, and so on.In addition, with the potential large size antennaarrays, signal processing complexity becomes veryhigh. The BS power consumption could also be amajor concern taking into account the cost ofheavy baseband signal processing. Thereforeoptimized (real-time and low-complexity) dis-tributed signal processing algorithms need to becarefully designed and implemented. Furthermore,the synchronization of different transceiver andantenna element chains becomes complicatedand is of great importance for the overall systemperformance. Note that the number of antennasat the UE does not need to be increased beyondwhat is defined in LTE/LTE-Advanced.

For cooperative 3D MIMO operations, wheremultiple BSs are clustered together to form adistributed antenna array and perform MU-MIMO operations, the overhead can be pro-hibitive. First, the CSI acquisition overhead isvery high, considering the large size of the anten-na arrays. The effect of errors in CSI estimation,quantization, and feedback to the system perfor-mance also need to be investigated thoroughly.In addition, in order to facilitate cooperationamong multiple BSs the backhaul is also a non-negligible challenge.

At the same time, as the granularity of 3Dbeamforming continues to be refined it is expect-ed that the number of BS antenna elements willapproach the level that is considered massiveMIMO (e.g. [13] and the references therein).Though 3D MIMO with finite antenna elementscan be considered as an intermediate stagetoward massive MIMO, the latter boasts drasti-cally different (and likely simplified) signal pro-cessing from conventional precoding. On boththe theoretical side and the operational side, it isworth investigating when and whether such atransition will take place and how the 3D MIMOcommunity should prepare for that.

CONCLUSIONS

In this article we introduced the fundamentalsand application scenarios of the emerging 3DMIMO technology and identified several keyissues critical to the research and development ofthis technology. In terms of the channel measure-ments and modeling, we reported the recent mea-surement campaign conducted by CMCC andBUPT. In addition we are the first to comprehen-sively classify prevalent 3D MIMO channel mod-els. In terms of the key 3D MIMO technologies,we used proof-of-concept simulations based onthe system-level simulator provided by Huawei.We then summarized existing work in terms of3D beamforming, SU- and MU-MIMO, as well asinterference coordination. Last but not least, wedelineated the opportunities and challenges on allthese aspects, and laid out research issues togeth-er with possible directions of the next-phase 3DMIMO research and development.

ACKNOWLEDGMENTThis work was jointly supported by the NationalNatural Science Foundation of China (Grant No.61101079, 61172105, and 61322110), the NationalScience Foundation (Grant No. 5388740), theNational 863 Project (Grant No. 2014AA01A706),

the synchronization ofdifferent transceiver andantenna element chains

becomes complicatedand is of great impor-

tance for the overall sys-tem performance. Note

that the number ofantennas at UE does not

need to be increasedbeyond what is defined

in LTE/LTE-Advanced.



the Important National Science & TechnologySpecific Project of China (Grant No.2013ZX03003006), the Science Foundation forthe Youth Scholar of Ministry of Education ofChina (Grant No. 20110001120129), and the StateKey Laboratory of Rail Traffic Control and Safe-ty (RCS2014K008), Beijing Jiaotong University.

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[4] 3GPP R1-132543, “UMa Channel Measurements Resultson Elevation Related Parameters,” China Mobile, April2013.

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BIOGRAPHIESXIANG CHENG [S’05-M’10-SM’13] received a Ph.D. degreefrom Heriot-Watt University and the University of Edin-burgh, Edinburgh, U.K., in 2009, where he received thePostgraduate Research Thesis Prize. He has been withPeking University, Beijing, China, since 2010, first as a Lec-turer, and then as an Associate Professor since 2012. He isalso with the State Key Laboratory of Rail Traffic Controland Safety, Beijing Jiaotong University. His current researchinterests include mobile propagation channel modeling andsimulation, next generation mobile cellular systems, intelli-gent transportation systems, and hardware prototypedevelopment. He has published more than 80 researchpapers in journals and conference proceedings. He receivedthe best paper award from the IEEE International Confer-ence on ITS Telecommunications (ITST 2012) and the IEEEInternational Conference on Communications in China(ICCC 2013). He received the “2009 Chinese NationalAward for Outstanding Overseas Ph.D. Student” for his

academic excellence and outstanding performance. He hasserved as Symposium Leading-Chair, Co-Chair, and a Mem-ber of the Technical Program Committee for several inter-national conferences. He is now an Associate Editor forIEEE Transactions on Intelligent Transportation Systems.

BO YU [S’08] received a B.S. degree in electrical engineeringfrom Huazhong University of Science and Technology,Wuhan, China, in 2008, and an M.S. degree in electricalengineering from the University of Florida, Gainesville, in2010. He is currently pursuing a Ph.D. degree with theDepartment of Electrical and Computer Engineering, Col-orado State University, Fort Collins. His research interestsinclude spectrum and energy efficiency in cellular networks.

LIUQING YANG [S’02-M’04-SM’06] received her Ph.D. degreefrom the University of Minnesota, Minneapolis, in 2004.She then joined the University of Florida, Gainesville, andbecame an Associate Professor in 2009. She is currently anAssociate Professor with the Department of Electrical andComputer Engineering at Colorado State University. Hergeneral interests are in the areas of communications andsignal processing. She was the recipient of the ONR YoungInvestigator Program (YIP) award in 2007, and the NSF Fac-ulty Early Career Development (CAREER) award in 2009.She is serving as an active reviewer for more than 10 jour-nals, as TPC chair/member for a number of conferences,and has served on the editorial board for IEEE Transactionson Communications, IEEE Transactions on Wireless Commu-nications, IEEE Transactions on Intelligent TransportationSystems, and Phycom: Physical Communication.

JIANHUA ZHANG received her Ph.D. degree in circuit and systemfrom Beijing University of Posts and Telecommunication (BUPT)in 2003. She was an associate professor from 2005 and is nowprofessor of BUPT. She has published more than 100 articles inrefereed journals and conferences. She was awarded “2008Best Paper” of the Journal of Communication and Network. In2007 and 2013 she received two national novelty awards forher contribution to the research and development of Beyond3G TDD demo system with 100Mb/s@20MHz and1Gb/s@100MHz respectively. In 2009 she received the “secondprize for science novelty” from the Chinese CommunicationStandards Association for her contributions to ITU-R and 3GPPin IMT-Advanced channel model. Her current research interestsinclude channel measurement and modeling for broadbandmultiple antenna system, transmission techniques for 4G and5G systems, channel emulator, OTA test, and and so on.

GUANGYI LIU [M] received his B.S. in physics from ChineseOcean University in 1997. He received his M.S. and Ph.D.degrees in circuits and systems from Beijing University ofPosts and Telecommunications in 2000 and 2006, respec-tively. Since 2006 he has been working for the ResearchInstitute of China Mobile Communication Corporations. Heis now chief technical officer of the Wireless Department ofthe same institute. His research interests include LTE/LTE-Advanced/5G standardization and trial, system perfor-mance modeling and evaluation, channel measurementand modeling, and flexible spectrum usage.

YONG WU received the B.S. degree from Dalian University ofTechnology, Dalian, China in 2003, and a Ph.D. degreefrom Tsinghua University, Beijing, China in 2008, both inelectronics engineering. He is currently working at HuaweiTechnologies, Beijing. His research interests include multi-ple antenna processing, interference suppression, and sys-tem-level modeling and evaluation of wirelesscommunications.

LEI WAN is a senior expert of Wireless Research and Standardsat Huawei, currently responsible of LTE-Advanced researchand 3GPP RAN standardization work in Huawei. Her mainfocus areas are network topology and air-interface evolution.She is known as the expert on TDD and the system-level eval-uation methodology in the industry, as the main driver of LTETDD/FDD frame structure merging, CoMP, LTE-Hi (3GPP R12small cell enhancement) and U-LTE (unlicensed LTE) researchand standardization, and the inventor of the physical layer MIquality model that is widely used in system-level simulation.She has long experience in the wireless communicationindustry, with the background of WCDMA, TD-SCDMA, LTEand LTE-Advanced. Her research interests cover FDD and TDDharmonization, network topology evolution as CoMP andHetNet, traffic adaptive system design, interference coordina-tion and the evaluation methodology.

Though 3D MIMO withfinite antenna elementscan be considered as anintermediate stagetoward massive MIMO,the latter boasts drasti-cally different (and like-ly simplified) signalprocessing from conven-tional precoding. Onboth the theoretical sideand the operationalside, it is worth investi-gating when andwhether such a transi-tion will take place andhow the 3D MIMO community should prepare for that.


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