IEEE Wireless Communications • April 201426 1536-1284/14/$25.00 © 2014 IEEE

Younsun Kim, HyoungjuJi, and Juho Lee are withSamsung Electronics Co.,Ltd.

Young-Han Nam, BoonLoong Ng, Ioannis Tzani-dis, Yang Li, andJianzhong (Charlie)Zhang are with SamsungResearch America.

EN H A N C I N G SP E C T R A L EF F I C I E N C Y F O R LTE-AD VA N C E D

A N D BE Y O N D CELLULAR NE T W O R K S

INTRODUCTIONIn recent years, the wireless industry has seen adrastic increase of wireless data traffic on a glob-al scale [1]. This increase is fueled by the devel-opment of new mobile smart devices andapplications that consume significantly largervolumes of data compared to traditional voicecalls. In response to the increase in wireless datatraffic, the Third Generation Partnership Project(3GPP) has focused much of its standardizationefforts in providing cutting edge techniques toimprove spectral efficiency and user experience.Among such techniques are multiple-input mul-tiple-output (MIMO), coordinated multipoint(CoMP) transmission/reception, and carrieraggregation (CA). CoMP relies on coordinationbetween multiple transmission and receptionpoints to enhance user equipment (UE) perfor-mance at cell edges, but requires a very capablebackhaul connection for inter-site coordination.Carrier aggregation simultaneously utilizes mul-tiple frequency bands to enhance peak data rate

and a network’s load balancing capability, butrequires the use of large frequency resources.Although each of these techniques represents amajor step forward in improving system perfor-mance, further developments of new technolo-gies are required to meet the exponentiallygrowing demand for wireless data traffic.

Full dimension MIMO (FD-MIMO) [2] isone of the key technologies currently studied inthe 3GPP for the next generation Long-TermEvolution (LTE) systems. As a first step, a studyitem [3] has been initiated to study a new chan-nel model under which future evaluation of theantenna technologies will be performed. Follow-up, 3GPP study and work items on FD-MIMOare expected as early as this year. The focus ofthe study and work items is to identify key areasin the LTE standards that need to be enhancedto support up to 64 antenna ports placed in a 2Darray. By incorporating FD-MIMO into LTEsystems, it is expected that system throughputwill be drastically improved beyond what is pos-sible in conventional LTE systems. Compared toCoMP and CA, FD-MIMO is capable of enhanc-ing system performance without requiring a verycapable backhaul or large frequency resources.This article builds on the discussions in [2] toprovide details of FD-MIMO in terms of deploy-ment scenarios, 2D antenna array implementa-tion, possible enhancements to the current LTEstandards, and system-level evaluation results.

FD-MIMO utilizes multiple antennas placedin a 2D antenna array panel to realize high-order multi-user MIMO (MU-MIMO) transmis-sions. High-order MU-MIMO refers to the useof a large number of antennas at the base sta-tion to transmit or receive spatially multiplexedsignals to or from a large number of terminals.Figure 1 depicts an enhanced NodeBs (eNB,3GPP terminology for base station) with FD-MIMO capability transmitting simultaneously tomultiple UEs (here, UE is 3GPP terminology formobile station). The antennas at the eNB areplaced on a 2D antenna array panel where everyantenna is an active element. These active anten-na elements allow dynamic and adaptive precod-ing to be performed jointly across all antennas.As a result of such precoding, eNBs can realize

YOUNSUN KIM, HYOUNGJU JI, JUHO LEE, YOUNG-HAN NAM, BOON LOONG NG, IOANNIS TZANIDIS, YANG LI, AND JIANZHONG (CHARLIE) ZHANG

ABSTRACTFull dimension MIMO has attracted signifi-

cant attention in the wireless industry andacademia in the past few years as a candidatetechnology for the next generation evolutiontoward beyond fourth generation and fifth gen-eration cellular systems. FD-MIMO utilizes alarge number of antennas placed in a 2D anten-na array panel for realizing spatially separatedtransmission links to a large number of mobilestations. The arrangement of these antennas ona 2D panel allows the extension of spatial sepa-ration to the elevation domain as well as the tra-ditional azimuth domain. This article discussesfeatures and performance benefits of FD-MIMOalong with the ongoing standardization efforts in3GPP to incorporate FD-MIMO features in thenext evolution of LTE. Furthermore, a design ofa 2D antenna array, which plays a key role in theimplementation of FD-MIMO, is also discussed.Finally, in order to demonstrate the perfor-mance benefits of FD-MIMO, system-level eval-uation results are provided.

FULL DIMENSION MIMO (FD-MIMO): THE NEXT EVOLUTION OF MIMO IN LTE SYSTEMS

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more directed transmissions in the azimuth andelevation domain simultaneously to a largernumber of UEs.

Compared to MIMO transmissions of con-ventional LTE systems, FD-MIMO has twoimportant differentiating factors. First, the num-ber of antennas has been increased beyond whatis supported in conventional LTE systems (up toeight antennas). As a result, beamforming andspatial user multiplexing capability can be signifi-cantly improved. Second, antennas are no longerplaced in a 1D linear array but in a 2D planararray. The main motivation for the planar place-ment is to reduce the form factor of the anten-nas to be more practical. For example,supporting 64 antennas at 2.5 GHz in an 8 × 8planar array with 0.5 l spacing would result in aform factor of 50 cm × 50 cm. However, if theantennas are placed in a linear array, the arraywould be 4 m wide, making it unpractical. Whilea planar array does reduce the effective spacingbetween different antenna elements comparedto a linear array, it provides the benefit of beingable to extend spatial separation to the elevationdomain as well as the traditional azimuthdomain. More details on the design of the 2Dantenna array and its impact on system perfor-mance are presented in the following sections.

The purpose of this article is to introduce thetechnical aspects of FD-MIMO. Discussions onsystem deployment, implementation of a 2Dantenna array, modeling of a 3D channel, andpossible enhancements to the current LTE stan-dards are presented. Additionally, system evalua-tion results are provided to demonstrate theperformance benefits of FD-MIMO comparedto legacy MIMO systems.

The rest of this article is organized as follows.First, the next section discusses the overall con-cept of FD-MIMO, possible specification sup-ports in order to realize FD-MIMO in LTEsystems, and deployment scenarios where FD-MIMO-capable eNBs are likely to be deployed.We next discuss the design of a 2D antennaarray that enables high-order MU-MIMO opera-tion while considering both performance andoverall form factor. Then we discuss the 3Dchannel model that takes into account wirelesschannel propagation effects in the elevationdomain in addition to the azimuth domain. Sim-ulation scenarios and parameters along with theresults and the relevant analysis are presented.Finally, conclusions are drawn.

FULL DIMENSION MIMO SYSTEM CONCEPT

THEORETICAL PERFORMANCE

The key source of performance enhancement byutilizing FD-MIMO is its ability to handle high-order MU-MIMO. Compared to conventionalLTE systems where the maximum number ofMU-MIMO co-scheduled UEs is limited to four,FD-MIMO is capable of supporting a signifi-cantly larger number of MU-MIMO UEs with alarger number of antennas. Consider an FD-MIMO system with NT transmit antennas ateNB, K co-scheduled UEs, and downlink trans-mission power of P. With channel conjugate pre-coding, the received signal for the kth UE can

be derived as

where xk is the transmitted signals for the kthUE, hk is the downlink channel for the kth UE,and nk is the Gaussian noise at the kth UE’sreceiver. Theoretically, as the number of anten-nas increases, the cross-correlation of two ran-dom channel realizations converges to zero asshown in [4],

(2)

where dkl = 1 if k = l and dkl = 0 otherwise. As aresult, assuming a large NT and K (K < NT), theaverage signal-to-interference-plus-noise ratio(SINR) for each UE can be approximated as

(3)

for the case where inter-user interference is sig-nificantly larger than the noise variance. Theabove analysis is for the downlink, but a similaranalysis can be applied for the uplink multipleaccess channel [4]. Although the simple analysisabove is based on an ideal signal model, impor-tant insights can be obtained. From Eq. 3, it canbe observed that the SINR at each UE linearlyincreases as a function of the number of anten-nas. Additionally, if the number of antennasincreases at the same rate as the number of co-scheduled UEs, the same SINR can be main-tained. In other words, if the number of transmitantennas increases by a factor of G, the numberof UEs that can be co-scheduled using the samewireless resource can also increase by a factor ofG without any sacrifice in SINR. For example, ifthe number of antennas increases from 10 to 100while the number of UE’s increases from 2 to20, a 10-fold system capacity increase can beachieved. Note that the above analysis assumes

∑= + +⎛

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Figure 1. Conceptual diagram of an FD-MIMO system realizing high-orderMU-MIMO by utilizing a 2D antenna array.

FD-MIMO 2Dantenna array

Patch antenna

Feednetwork

Connector CPRI

FD-MIMO basebandLTE

infrastructure

IP

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an uncorrelated channel at the transmitter andvery large number of transmit antennas. Howev-er, actual wireless channels have some degree ofcorrelation depending on the environment orantenna implementation and an eNB has a finitenumber of antennas.

DEPLOYMENT SCENARIOS

In order to fully exploit the enhanced beamform-ing and spatial user multiplexing of FD-MIMO,eNBs with FD-MIMO capability should bedeployed in scenarios where such characteristicscan provide system performance enhancement.Figure 2 shows examples of such FD-MIMOdeployment in urban micro, urban macro, highrise, and high population density scenarios.

In practical situations, most UEs in urbanlocations are indoors on different floors. Havingthe capability to control the beam direction inthe elevation domain as well as the azimuthdomain presents new opportunities to enhancesystem performance for FD-MIMO in such sce-narios. One important scenario is the urban out-door to indoor scenario between an outdooreNB and indoor UEs on different floors. Trans-missions originating from the outside of thebuilding to UEs located on different floors canbe better separated using beamforming in theelevation direction.

Another scenario of importance is the highpopulation density scenario, where a large num-ber of UEs are closely located with one anotherin a hot zone. Examples of such high populationdensity scenarios are:• Shopping malls• Stadiums or concert halls• Transportation hubs such as major airports or

train stationsA key characteristic of the high population

area scenario is that a large number of peopleare located in a limited area, generating hightraffic demand simultaneously. Typically, in such

scenarios, hundreds or even thousands of UEs ina hot zone can simultaneously try to access thecellular system, leading to severe quality of ser-vice (QoS) instability. MU-MIMO transmissionfrom the 2D antenna array can be made simulta-neously for multiple UEs in such scenarios, tak-ing advantage of the additional beam directivity.For example, in a shopping mall with a high ceil-ing, the 2D antenna array can be positioned onthe ceiling facing downward to provide high-order MU-MIMO transmission.

SPECIFICATION SUPPORT FOR LTE SYSTEMSIn order to support FD-MIMO in LTE systems,a number of areas in the LTE standards need tobe enhanced to accommodate additional anten-nas in a 2D antenna array.

One such area is downlink reference signals.In the LTE standards, two types of reference sig-nals are provided to support multi-antennatransmissions: channel state information refer-ence signal (CSI-RS) and demodulation refer-ence signal (DM-RS). CSI-RS is a low overheaddownlink reference signal with a periodicity of5n ms (n = 1, 2, 4, 8, or 16) that a UE measuresto derive downlink CSI. On the other hand,DM-RS is a UE-specific downlink reference sig-nal with the same precoding as the data signaland is transmitted on the same frequency/timeresource as the data signal. DM-RS provides aUE with a reference for data demodulation. IfCSI-RS and DM-RS overhead increases propor-tionally to the number of orthogonal antennaports as in current LTE standards, the largenumber of antenna ports to support FD-MIMOwould lead to an excessively large overhead ofdownlink reference signals. Specification supportin this area should focus on how to reduce theoverhead of the reference signals while ensuringreasonable channel estimation performance.

To cope with a potentially large downlinkoverhead due to the reference signals, one alter-native for supporting FD-MIMO in the LTEstandards is to support it in time-division duplex(TDD) systems only where the eNB can exploitchannel reciprocity in determining the downlinkprecoders. However, it should be noted thateven if channel reciprocity holds, the eNB can-not effectively determine the highest downlinkdata rate at which a UE can reliably receive sig-nals. The downlink data rate can only be deter-mined at the UE, which has knowledge of itsown receiver performance and the level of noiseand interference. For this reason, even for aTDD LTE system, it is expected that enhance-ment of CSI-RS will be required for FD-MIMO.

Another such area is CSI reports, comprisingthe following three indicators:• Rank indicator (RI): Indication of preferred

number of layers on the downlink• Precoder matrix indicator (PMI): Indication of

preferred precoding on the downlink• Channel quality indicator (CQI): Indication of

the maximum supportable data rate on thedownlinkIn order to fully realize the benefits of FD-

MIMO, RI, PMI, and CQI would have to beenhanced to take into account the large numberof antennas and the 2D antenna array. PMIshould be enhanced to pinpoint a preferred

Figure 2. FD-MIMO deployment scenarios.

25m

10m

6~8 floors6~8 floors

20 floors

UMa (urban macro)

Stadium, mall, airport

UMi (urban macro)

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downlink precoding on the azimuth and eleva-tion domains while maintaining the uplink over-head to a reasonable level. Additionally, CQIshould be redefined such that interference fromthe transmissions to co-scheduled UEs can effec-tively be taken into account.

TWO-DIMENSIONAL ACTIVEANTENNA ARRAY

In order to realize the benefit of FD-MIMO, anefficient implementation of a 2D antenna arrayis a key requirement. A 2D antenna array shouldbe designed such that active antenna elements inhorizontal and vertical placement can provideadaptive precoding in both the azimuth and ele-vation domains. In doing so, the 2D array shouldhave a form factor that is adequate for actualdeployment while providing sufficiently efficientradio frequency characteristics.

An actual functioning example of an FD-MIMO array configuration is shown in Fig. 3. Thearray comprises four stacked panels, each havingeight sub-arrays arranged in an 8 × 1 (8 horizontalby 1 vertical) configuration. The spacing betweentwo adjacent sub-arrays is dH = 0.5 l in the hori-zontal direction and dV = 2 l in the vertical direc-tion (between centers of adjacent sub-arrays).Each sub-array is composed of four patch antennaelements arranged in a 1 × 4 configuration, andfed with equal magnitude and phase by a singlefeed port. Thus, the FD-MIMO array has a totalof 32 feed ports (32 channels) and a form factorof approximately 1 m height by 50 cm width.

One of the key features of this FD-MIMOarray configuration is that the patch antenna ele-ments are disposed in the = ±45° directions,which results in dual-linear polarization on thetwo diagonal planes ( = ±45° with reference tothe coordinate system shown in Fig. 3). Due tothis configuration, the +45° and –45° polarizedsub-arrays have the same beamwidths in the ele-vation ( = 0°) and azimuth ( = 90°) planes,and are affected more similarly by the channelcharacteristics than would a 0° and 90° dual-polarized array version. Notice also that the+45° and –45° sub-arrays are interlaced alongboth the horizontal and vertical directions toincrease isolation between adjacent sub-arrays(since they are orthogonally polarized). Thus,scanning the array beam in the azimuthal plane,for example, could involve sub-arrays from twoadjacent panels (e.g., the four +45° from the toppanel and the four +45° from the panel belowit) if the dH = 0.5 l spacing is to be maintained.

The patch elements of each sub-array are fedthrough a corporate microstrip line feed networkprinted on the bottom layer of the feed board.Energy is coupled to the patches through rectan-gular slot cutouts on the ground plane on theother side of the feed board. This feeding tech-nique provides better bandwidth, higher isola-tion between adjacent patch elements, and alsomore flexibility in adjusting the air-gap betweenthe antenna and feed board (see the board stack-up detail in Fig. 3) than conventional probefeeding. The air gap between the antenna boardand the feed board (ground plane) is tuned tomaximize the bandwidth and achieve the speci-

fied gain. Finally, a low-loss radome covers theantenna and system enclosure, and protects itfrom the elements.

For consistent radiation, it is important that allsub-arrays are phase matched; that is, the differ-ence of electrical lengths from the feed ports tothe patch antennas should not exceed more than1° ~ 2°. To ensure phase matching, all sub-arrayshave the same feed network adjusted to fit both±45° rotated patches by merely mirroring themicrostrip sections, as seen in Fig. 3 (mirroringsections in our case does not change the electricallength). Furthermore, within each four-elementpatch sub-array, all microstrip sections werephase matched to better than 1.4° from the com-mon feed port to the patch antenna.

The antenna panel is designed for one candi-date band to support FD-MIMO: LTE TDD#41(2.496–2.69 GHz) and to achieve a gain of about10 dBi per sub-array with beamwidth of 25° and65° in the azimuth and elevation domains,respectively. Figure 4 shows a picture of a fabri-cated FD-MIMO array panel and measurementresults in an anechoic chamber environment.Each sub-array has lower than –15 dB reflectioncoefficient in the target band, while the mutualcoupling between adjacent sub-arrays isbelow –20 dB. Measured co-phase and cross-polradiation patterns for one of the eight sub-arrayson the azimuth and elevation planes at 2.6 GHzare also shown in Fig. 4. The sub-array co-polgain is about 10 dBi at q = 0°, and the cross-polgain about 10 dB below that. The beamwidths atthe azimuth and elevation planes are about 65°and 24°, respectively.

THREE-DIMENSIONAL CHANNEL MODEL

Geometry-based stochastic channel models havebeen developed and refined over time by a num-ber of research groups such as 3GPP, 3GPP2,

Figure 3. Right: top-to-bottom view of an FD-MIMO antenna array and feednetwork; left: bottom view of one sub-array with detailed PCB stackup.

Board stack-up

2λ

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the International Telecommunication Union(ITU), and the WINNER initiative [5, 6]. Thespatial channel model (SCM) [6], an example ofa geometry-based stochastic channel model [7],is widely used in the 3GPP community to evalu-ate performance of different wireless technolo-gies. Traditional SCM used in the 3GPPcommunity is a 2D channel model, where an ele-vation angle of each signal path is alwaysassumed to be zero. While such an approach isacceptable for evaluating performance of sys-tems with horizontally placed linear antennaarrays, modeling of elevation angles is necessarywhen evaluating an FD-MIMO technology utiliz-ing a 2D antenna array.

In a 3D SCM, each signal path has to be mod-eled with an elevation angle as well as an azimuthangle. A 3D spatial channel model takes intoaccount the wireless channel propagation in theelevation direction as well as the azimuth direc-tion. One of the main challenges is to model the

correlation of large-scale parameters as well asthe statistical distribution of elevation angles.Large-scale channel parameters, such as azimuthspread at departure (ASD), azimuth spread atarrival (ASA), elevation spread at departure(ESD), elevation spread at arrival (ESA), shadowfading, Rician K-factor, and delay spread, havebeen shown to be correlated. As a result, thecross-correlations of these parameters must bemeasured and modeled at the terminal side. Inaddition, if the terminals are closely located, thelarge-scale parameters are correlated for differ-ent terminals as well. These cross-correlationshave not been extensively measured and reportedin the literature. Nevertheless, in some refer-ences (e.g., WINNER+), the elevation spread isassumed to have the same spatial correlation asthe azimuth spread; such approximation is con-sidered reasonable, since the azimuth and eleva-tion spreads originate from the same clusters,and their autocorrelations may behave in a simi-

Figure 4. FD-MIMO antenna panel measurements: a) photo of a fabricated antenna array panel comprising eight 0.5 l spaced sub-arrays, taken during measurements in an anechoic chamber; b) magnitude of reflection coefficients (self S-parameters) ofeight sub-arrays within FD-MIMO antenna panel; c) magnitude of mutual coupling coefficients (Si+1, i parameters) between adjacent sub-arrays; d) co-pol and cross-pol radiation patterns of one sub-array on azimuth ( = 0°) and elevation ( = 90°)planes at 2.6 GHz.

Frequency (GHz)2.22

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lar manner. Further study and measurements areneeded to confirm this assumption and deter-mine whether or not the same approach could betaken for the other elevation parameters.

In some references (e.g., WINNER II), thedistribution of the elevation spread is assumed tobe wrapped Gaussian, which is symmetric aroundthe mean. However, in other measurements, it isobserved that the distribution of elevation anglesis asymmetric, so the distributions that reflect theasymmetric nature of elevation angles are consid-ered. This aspect is reflected in WINNER+. Adouble exponential (or Laplace) distribution isproposed in WINNER+, which models suchskewness by using two standard deviations (leftand right). A proper modeling of the mean andvariance is another key challenge of extending a2D channel model to a 3D channel model.

Currently, in 3GPP, a study item [3] has beeninitiated to finalize the details of the 3D channelmodel. The following key topics are under dis-cussion; generic steps of channel generation,height dependent path loss generation, correla-tion of large-scale parameters, and statistical dis-tribution of elevation components. Partnercompanies are undertaking extensive measure-ment campaigns to complete these topics. Theoutcome of the study item will be used in futureevaluations for the standardization of not onlyFD-MIMO but also other advanced antennatechnologies that utilize 2D antenna arrays.

DOWNLINK SYSTEM PERFORMANCEEVALUATION

In order to verify the performance benefits ofdeploying FD-MIMO, system-level simulationswere performed for a multi-cell system environ-ment. A homogeneous cellular network consistingof 57 cells in a 2-tier deployment with a minimuminter-site distance of 500 m was assumed. Addi-tionally, UE dropping was assumed to be uni-

formly distributed. Two performance metrics areconsidered: cell average throughput, representingoverall system capacity, and 5 percentile userthroughput, representing cell edge UE perfor-mance. The 5 percentile user throughput is theuser throughput at the 5 percentile obtained fromthe cumulative distribution of the individual userthroughputs in the system. Both performancemetrics are in units of bits per second per Hertz.

All the evaluation results are obtained by tak-ing into account the 3D wireless channel environ-ment described in [9]. Additionally, all evaluationresults were obtained based on the assumptionthat the receiver and transmitter have full knowl-edge of the CSI on their respective downlinkchannel. Other details of the evaluation parame-ters can be found in [10]. Note that all the evalu-ation results have been obtained for a case wherethe UEs are located on the ground level (i.e., at1.5 m height) due to the lack of a widely recog-nized channel model for UEs distributed in dif-ferent elevation levels. Higher FD-MIMOperformance gains are expected in future evalua-tions of channel models that take into accountUE distribution in the elevation domain.

Figure 5 shows the system performance ofFD-MIMO for different antenna configurations(left subfigure) and different numbers of UEsper cell (right subfigure). The left subfigure inFig. 5 shows evaluation results for NT = 32 andNT = 64 antenna ports when the number of UEsper cell is 10. Furthermore, the horizontal anten-na spacing is 0.5 l , and the vertical antennaspacing is either 0.5 l or 2 l. An antenna arrayconfiguration of NH × NV corresponds to theplacement of NH antennas in the horizontal axisand NV antennas in the vertical axis. Right sub-figure in Fig. 5 shows the evaluation results forNT = 64 antenna ports where the number ofUEs per cell is 10, 20, and 30. It was assumedthat downlink transmission is scheduled to allserving UEs in a cell using full bandwidth(denoted full scheduling).

Figure 5. Evaluation results on FD-MIMO with full scheduling for: left: different antenna configurations; right: different number ofUEs per cell.

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Conventional LTE MIMO with a linearantenna array of 8 × 1 achieves cell average per-formance of 2.8 b/s/Hz and cell edge perfor-mance of 0.1 bps/Hz for 10 UEs per cell.Compared to these results, Figure 6 shows thatFD-MIMO achieves significant performancegains by utilizing the larger number of antennasfor enhancing MU-MIMO with a large numberof co-scheduled UEs. For example, FD-MIMOwith 16 × 4 provides about 400 percent gain.

On the left subfigure in Fig. 5, it can beobserved for 0.5 l vertical antenna spacing, anantenna configuration that has more antennas onthe vertical axis achieves significantly lowerthroughput than the one that has more antennason the horizontal axis. For example, with NT = 32and 0.5 l vertical antenna spacing, the 8 × 4 con-figuration achieves larger cell average and celledge throughput than the 4 × 8 configuration. Thisperformance attribute is because the elevationangle spread is small among the UEs in the cur-rent simulation assumption where all the UEs areassumed to be dropped on the ground level. In thiscase, the precoding on the elevation domain can-not be effectively applied to separate beams target-ed for different UEs. When 2 l spacing is used forthe vertical antennas, a better spatial separationcan be achieved in the elevation domain than with0.5 l spacing; given the same number of antennas,the 2 l spacing configuration achieves significantlybetter performance than the 0.5 l spacing configu-ration. For example, cell average throughput for 8× 8 with 0.5 l spacing and 2 l spacing are 8.1b/s/Hz and 12.9 b/s/Hz, respectively.

On the right subfigure in Fig. 5, it can beobserved that as the number of UEs per cell increas-es, the cell throughput of the FD-MIMO systemincreases, taking advantage of higher-order MU-MIMO. Since full scheduling is used, if the numberof UEs per cell increases, so does the number of co-scheduled UEs. The performance improvement islarger with more antennas on the horizontal axis.For example, the cell average throughput for 64 × 1

with 10 UEs per cell is 14 b/s/Hz, while with 30 UEsper cell, it is 19 b/s/Hz. The performance improve-ment with 30 UEs per cell is not observed for thecase of 8 × 8 FD-MIMO due to the ineffectivebeam separation in the elevation direction.

Figure 6 shows the system performance of FD-MIMO for different antenna configurations anddifferent numbers of UEs per cell when propor-tional fair scheduling (denoted PF scheduling) [10]is used. Other than using PF scheduling instead offull scheduling, the other evaluation parametersare identical. Since PF scheduling is used, not allthe UEs are co-scheduled. Instead, the PF sched-uler chooses a subset of the UEs that maximizesthe proportional fairness metric. Theoretically, PFscheduling should always be able to outperformfull scheduling since PF scheduling relies on theCSI to choose a set of co-scheduled UEs that bal-ances performance and fairness. Contrary to PFscheduling, there is no consideration for CSI in thefull scheduling since all the UEs in each cell areco-scheduled. In general, observations similar tothose made for Fig. 5 can be made for Fig. 6. Ver-tical antenna spacing of 2 l results in better perfor-mance than 0.5 l, and higher cell throughput canbe achieved with more UEs per cell.

By comparing Fig. 5 to Fig. 6, an interestingobservation can be made on performance of fullscheduling and PF scheduling. For some antennaconfigurations (e.g., 64 × 1) and numbers ofUEs, relatively higher performance can beachieved with full scheduling than with PFscheduling. One possible reason for this perfor-mance attribute is fluctuation of MU-MIMOinterference between the spatially multiplexedstreams for different UEs in the case of PFscheduling. In full scheduling, the scheduler hasno control on the selection of co-scheduled UEs,but in return, the MU-MIMO interferenceobserved by each UE is quite stable since all theUEs in a cell are scheduled on all time and fre-quency resources. Stable MU-MIMO interfer-ence allows the outer loop for individual UEs to

Figure 6. Evaluation results on FD-MIMO with PF scheduling for: left: different antenna configurations; right: different numbersof UEs in a cell.

5%-tile user throughput (b/s/Hz)0.2

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K = 30K = 20K = 10

(32 1)

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(64 1)

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(16 4)

(16 4)

(1 64)(2 32)

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(32 2)

(64 1)

(16 4)

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(32 2)

(64 1)

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IEEE Wireless Communications • April 2014 33

self-correct any mismatch in link adaptation. Onthe other hand, in PF scheduling, the schedulertries to optimize the set of co-scheduled UEs. Asa result of the optimization, the set of co-sched-uled UEs changes in both time and frequencydomains, causing a fluctuation in MU-MIMOinterference observed by each UE. With suchfluctuation in MU-MIMO interference, itbecomes difficult for the outer loop to correctthe mismatch in link adaptation. The perfor-mance degradation due to mismatch in link adap-tation is aggravated by the fact that in LTE thereis no support for handling MU-MIMO interfer-ence in channel state reports. In other words, PFscheduling is done based on UEs’ channel statereports, which are optimized not for MU-MIMObut for single-user MIMO. The evaluation resultsin Fig. 6 suggest that in order to utilize PFscheduling, specification support for CSI thattakes into account MU-MIMO is necessary.

CONCLUSIONS

In this article, the characteristics and performanceof FD-MIMO technology for evolution towardsB4G and 5G cellular systems were discussed.Fundamental characteristics of FD-MIMO interms of performance, future deployment scenar-ios, and potential enhancements to the LTE stan-dards were presented. Additionally, details on thedesign of a 2D antenna array for the support ofFD-MIMO were discussed. In order to evaluatethe performance of FD-MIMO, a 3D channelmodel that captures the wireless channel charac-teristics of the azimuth and elevation directionswas introduced. System level evaluation resultsshowed that FD-MIMO systems can potentiallyprovide substantial system performance enhance-ment over legacy MIMO systems.

REFERENCES[1] Cisco, “Cisco Visual Networking Index: Global Mobile

Data Traffic Forecast Update, 2012–2017,” 2013.[2] Y.-H. Nam et al., “Full-Dimension MIMO for Next Gener-

ation Cellular Technology,” IEEE Commun. Mag., June2013.

[3] 3GPP TSG RAN Plenary #58, R1-122034, “Study on 3D-Channel Model for Elevation Beamforming and FD-MIMO Studies for LTE,” Barcelona, Spain, 4–11 Dec.2012.

[4] T. L. Marzetta, “Noncooperative Cellular Wireless withUnlimited Numbers of Base Station Antennas,” IEEETrans. Wireless Commun., 2010.

[5] C. Schneider et al., “Large Scale Parameter for the WIN-NER II Channel Model at 2.53 GHz in Urban MacroCell,” IEEE VTC-Spring, 2010.

[6] 3GPP TR 25.996 V10.0.0, “Spatial Channel Model forMultiple Input Multiple Output (MIMO) Simulations.”

[7] K. Baltzis et al., “A Simple 3D Geometric Channel Modelfor Macrocell Mobile Communications,” Wireless Per-sonal Commun., 2009.

[8] IST-4-027756 WINNRER II D1.1.2 V1.2, “WINNER IIChannel Models,” Sept. 2007.

[9] B. L. Ng et al., “Fulfilling the Promise of Massive MIMOwith 2D Active Antenna Array,” IEEE GLOBECOM, Dec.2012.

[10] 3GPP TR 36.819 V10.0.0, “Technical Report on Coordi-nated Multi-Point Operation for LTE Physical LayerAspects.”

[11] J. M. Holtzman, “Asymptotic Analysis of ProportionalFair Algorithm,” IEEE PIMRC, Sept. 2001.

BIOGRAPHIESYOUNSUN KIM (younsun@samsung.com) is currently a princi-pal engineer with Samsung Electronics where he coordinatesthe physical layer standardization of LTE/LTE-Advanced. Hereceived B.S. and M.S. degrees in electronic engineering

from Yonsei University, Korea, and his Ph.D. degree in elec-trical engineering from the University of Washington in1996, 1999, and 2009, respectively. He joined Samsung in1999 and has since been working on the standardizationof wireless communication systems such as cdma2000,HRPD, and LTE/LTE-Advanced. His research interests includemultiple access schemes, coordination schemes, multiple-antenna techniques, and advanced receivers for next gen-eration systems.

JUHO LEE (juho95.lee@samsung.com) is currently a Masterwith Samsung Electronics and is the lead executive incharge of research on standardization of wireless commu-nications. He received his B.S., M.S., and Ph.D. degrees inelectrical engineering from Korea Advanced Institute of Sci-ence and Technology in 1993, 1995, and 2000, respective-ly. He joined Samsung Electronics in 2000 and has beenworking on standardization of mobile communicationssuch as WCDMA, HSDPA, HUSPA, LTE, and LTE-Advanced.He was a vice chairman of TSG RAN WG1 during February2003 through August 2009 and served as the rapporteurfor the 3GPP LTE Rel-11 CoMP work item.

HYOUNGJU JI (hyoungju.ji@samsung.com) received his B.S inelectrical and electronic engineering and M.S. in communi-cations engineering from Sogang University, Korea, in 2005and 2007, respectively. He joined Samsung Electronics in2007, and has been involved in 3GPP RAN1 LTE and LTE-Advanced technology developments and standardization.His current interests include heterogeneous networks, carri-er aggregation, multi-antenna techniques, and relay andmachine type communications.

YOUNG-HAN NAM (younghan.n@samsung.com) received hisB.S. and M.S from Seoul National University, Korea, in1998 and 2002, respectively. He received a Ph.D. in electri-cal engineering from the Ohio State University, Columbus,in 2008. Since February 2008 he has been working at Sam-sung Research America, Dallas, Texas. He has been engagedin standardization, design, and analysis of the 3GPP LTEand LTE-Advanced, Releases 8–12. His research interestsinclude MIMO/multi-user/cooperative wireless communica-tions, cross-layer design, and information theory.

BOON LOONG NG (b.ng@samsung.com) received his Bachelorof Engineering (electrical and electronic) degree and hisPh.D. degree in engineering from the University of Mel-bourne, Australia, in 2001 and 2007, respectively. He isnow a staff standards engineer with Samsung ResearchAmerica, working on the standardization of LTE and LTE-Advanced. His research interests include wireless communi-cation systems, multi-antenna techniques, and coordinatedmultipoint transmission and reception techniques.

IOANNIS TZANIDIS (i.tzanidis@samsung.com) joined SamsungResearch America as an antenna engineer in 2012. Hereceived his Ph.D. in 2011 in the area of electromagneticsand antenna design from the ElectroScience Laboratory,Ohio State University. His expertise includes UWB antennasand antenna arrays for MIMO communications, wirelesscharging systems, and high-accuracy GPS systems.

YANG LI (yang.l@samsung.com) received his B.S. and M.S.degrees in electronic engineering in 2005 and 2008 fromShanghai Jiao Tong University, China, and a Ph.D. degreein 2012 in electrical engineering from the University ofTexas at Dallas. He works at Samsung Research America asa senior standards engineer. His research interests includeMIMO, interference management, cooperative communica-tion, and cognitive radio.

JIANZHONG (CHARLIE) ZHANG [S’96, M’02, SM’09](jianzhong.z@samsung.com) is currently senior directorand head of tje Wireless Communications Lab with Sam-sung Research America, where he leads technology devel-opment, prototyping, and standardization for beyond 4Gand 5G wireless systems. From August 2009 to August2013, he served as Vice Chairman of the 3GPP RAN1 Work-ing Group, and led development of LTE and LTE-Advancedtechnologies such as 3D channel modeling, UL-MIMO andCoMP, and carrier aggregation for TD-LTE. Before joiningSamsung, he was with Motorola from 2006 to 2007 work-ing on 3GPP HSPA standards, and with Nokia ResearchCenter from 2001 to 2006 working on the IEEE 802.16e(WiMAX) standard and EDGE/CDMA receiver algorithms. Hereceived his Ph.D. degree from the University of Wisconsin,Madison.

Partner companies areundertaking extensive

measurement campaignsto complete these

topics. The outcome ofthe study item will be

used in the future evaluations for the

standardization of notonly FD-MIMO but other

advanced antenna technologies that utilize

2-D antenna arrays.

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