5g role of small cells

IEEE Communications Magazine • May 201444 0163-6804/14/$25.00 © 2014 IEEE

Volker Jungnickel andKonstantinos Manolakisare with the FraunhoferHeinrich Hertz Institute.

Wolfgang Zirwas andBerthold Panzner are withNokia Solutions and Net-works.

Volker Braun is withAlcatel Lucent Bell Labs.

Moritz Lossow is withDeutsche Telekom AG,Innovation Labs. Thework described here wasdone when he was withFraunhofer HHI.

Mikael Sternad and RikkeApelfröjd are with Upp-sala University.

Tommy Svensson is withChalmers University ofTechnology.

INTRODUCTION

From the data traffic evolution over the lastyears, high capacity demands can be expectedfor the future evolution of mobile radio. It iscommonly assumed today that around 2020, anew fifth generation (5G) of mobile networkswill be deployed. Recent research has identifiedmajor challenges such as a massive growth in thenumber of connected devices and traffic, as wellas a wider and more diverse set of requirementsand characteristics, starting from low latency andlow rate for control messages between devices tomulti-gigabits per second for interactive multi-media [1]. Researchers currently investigate howto serve 10 to 100 times more devices, deliver1000 times the traffic, and to reduce the latencyby a factor of 5 for mobile users compared to

Long Term Evolution (LTE). In focus arepromising techniques to meet these challenges atreasonable costs.

A combination of more spectrum being usedmore flexibly (i.e., by spectrum sharing), higherspectral efficiency, and additional cells — so-called small cells — seems to be a reasonablebreakdown of the main challenge (i.e., 1000times more traffic) into more manageable sub-terms. Here, the focus is on small cells embeddedinto a macrocell network, sometimes referred toas a heterogeneous network (HetNet).

Our reference cases are Third GenerationPartnership Project (3GPP) LTE Release 8 to11, which are highly sophisticated systems com-prising the results of a vast amount of researchover many years. Reference values of the spec-tral efficiency are in the range of 2–3 b/s/Hz permacrocell. Outperforming the well designedLTE system by a factor of 10 to achieve 20b/s/Hz constitutes a true challenge.

From a high-level perspective, there are threemain contributors to reaching higher spectralefficiency: advanced interference mitigation,small cells for densification of the networknodes, and massive multiple-input multiple-out-put (MIMO), that is, a significant increase in thenumber of antennas at the base station andpotentially also at the terminal.

ADVANCED INTERFERENCE MITIGATIONIntercell interference is a well-known challengefrom the beginning of mobile radio. In 2G sys-tems, simple frequency reuse schemes proved tobe powerful and sufficient. However, it is intu-itive that higher spectral efficiency requires allcells to be active in the whole spectrum at alltimes. In LTE, sophisticated schemes denoted asintercell interference coordination (ICIC) havebeen introduced. As the name indicates, theseschemes coordinate intercell interference but donot eliminate it. Therefore, the gains are limitedto small and medium load conditions.

Comparing the spectral efficiency of a singleisolated cell with that of a cellular network, a biggap is observed. For interference-limited scenar-ios, theoretical gains of 300 percent and morehave been promised using network-wide base

ABSTRACT

5G will have to support a multitude of newapplications with a wide variety of requirements,including higher peak and user data rates,reduced latency, enhanced indoor coverage,increased number of devices, and so on. Theexpected traffic growth in 10 or more years fromnow can be satisfied by the combined use ofmore spectrum, higher spectral efficiency, anddensification of cells. The focus of the presentarticle is on advanced techniques for higherspectral efficiency and improved coverage forcell edge users. We propose a smart combina-tion of small cells, joint transmission coordinatedmultipoint (JT CoMP), and massive MIMO toenhance the spectral efficiency with affordablecomplexity. We review recent achievements inthe transition from theoretical to practical con-cepts and note future research directions. Weshow in measurements with macro-plus-small-cell scenarios that spectral efficiency can beimproved by flexible clustering and efficient userselection, and that adaptive feedback compres-sion is beneficial to reduce the overhead signifi-cantly. Moreover, we show in measurements thatfast feedback reporting combined with advancedchannel prediction are able to mitigate theimpairment effects of JT CoMP.

5G WIRELESS COMMUNICATIONS SYSTEMS:PROSPECTS AND CHALLENGES

Volker Jungnickel, Konstantinos Manolakis, Wolfgang Zirwas, Berthold Panzner, Volker Braun,

Moritz Lossow, Mikael Sternad, Rikke Apelfröjd, and Tommy Svensson

The Role of Small Cells, CoordinatedMultipoint, and Massive MIMO in 5G

JUNGNICKEL_LAYOUT_Layout 5/7/14 11:59 AM Page 44

IEEE Communications Magazine • May 2014 45

station cooperation to eliminate intercell inter-ference [2]. A comprehensive description ofpractical techniques for joint transmission coor-dinated multipoint (JT CoMP) can be found, forexample, in [5, references therein].

These results have motivated 3GPP to com-plete several study and work items. But achiev-ing even a fraction of the predicted gains atsystem level and for real impairments turned outto be difficult. Limitations are caused by delayedfeedback and synchronization errors. Furtherlimitations are due to clustering, user selection,and increased overhead.

Performance gains on the order of 50–100 per-cent were found in [3] taking practical constraintsinto account. Our analysis in this article leads to amore advanced interference mitigation framework.It includes new approaches for clustering, usergrouping, interference-floor shaping, feedbackcompression, channel prediction and scheduling.

SMALL CELLSThe interference mitigation framework describedin [3] has been designed for macrocell networks.It is a new task to integrate a large number ofsmall cells into this framework. For advancedtechniques like JT CoMP, small cells have spe-cific characteristics. They are placed as add-onsinto the macrocell, and have lower transmitterpower and different radio propagation charac-teristics (e.g., due to below-rooftop deployment).Furthermore, they do not always have a directlink to the macro base station enabling coordina-tion on a short timescale.

An intuitive solution is to set up different andindependent frequency layers for small and macro-cells, or to do time- or frequency-domain ICIC.While this solution is very robust and enables par-allel macro-plus-small-cell transmission withoutinterference, it sacrifices spectral efficiency. Ourresults reported below indicate that full frequencyreuse based on JT CoMP enables high spectralefficiency in both small and macrocells, taking theextra complexity into account.

MASSIVE MIMOThe term massive MIMO has been introducedfor using a much larger number of antennas persite than today. In particular, the number ofantennas is assumed to be, say, 10 times largerthan the total number of streams served to allterminals in a cell. In this way, significant beam-forming gains become possible, and more userscan be served in parallel [4].

For large numbers of antennas, however, theso-called pilot contamination sets an upperbound on spectral efficiency. More antennasneed more orthogonal pilots for channel estima-tion. The overhead consumes the radio resourcesquickly. Nonlinear (i.e., decision-aided) estima-tors can relax this situation at high signal-to-interference-plus-noise ratio (SINR) where errorpropagation plays a minor role. Note also thatincreasing the numbers of antennas is alwaysrelated to higher complexity and costs; it is intu-itive to deploy them gradually. More insights areneeded at the system level on how more anten-nas lead to higher spectral efficiency.

In the following, we propose an enhancedversion of the interference mitigation framework

developed in [3] in which JT CoMP is combinedwith small cells and massive — or ratherenlarged — MIMO. We review recent achieve-ments in the transition from theoretical to prac-tical concepts and outline synergies whencombining these techniques.

ADVANCED INTERFERENCEMITIGATION: RECENT INSIGHTS ON

COMP

JT CoMP aims at converting a mobile networkthat is limited by intercell interference into onethat exploits the potential interference for datatransmission. In reality, however, there are severallimitations and impairments making JT CoMP achallenging technology. Fundamental require-ments are proper synchronization between coop-erating sites, which can be satisfied by GPS or theIEEE 1588 precision time protocol. Either JTCoMP can be implemented in a central unit forall cooperating cells, or the processing is distribut-ed among the sites. While centralized processingrequires that sampled waveforms are transmittedfrom the baseband unit to remote radio heads, inthe distributed implementation, fast exchange ofuser data over the backhaul interconnectsbetween the cooperating cells is needed [5]. Bothare best implemented using fiber optics.

Multi-site demo systems (e.g. at the FraunhoferHeinrich Hertz Institute in Berlin and the Techni-cal University of Dresden) validated that the prin-cipal physical layer concept of JT CoMP (i.e.,synchronization of distant sites, multi-cell channelestimation, feedback of the channel state informa-tion [CSI], synchronous exchange of user data,and joint processing) can be implemented withsome extra effort [5]. But there is still room forimprovement using more sophisticated algorithms.This includes interference-floor shaping, cluster-ing, and user selection, a frequency-selectivescheduler, and more accurate CSI for robust pre-coding based on predicted channels, simultaneous-ly limiting the reporting overhead. While this wasclaimed as future research in [5], in the following,we describe our recent progress in these areas.

CLUSTERING AND USER SELECTION INSMALL CELLS

It is well accepted that cooperation has to belimited to a few cells because otherwise, thenumber of pilots, the feedback overhead for theCSI reporting and the backhaul traffic willexplode. Several measurements in real networksindicate that interference can be limited to avariable but small number of cells by using theantenna down-tilt. This feature suggests the for-mation of cell clusters in the network denoted ascooperation areas (CAs). CAs include thestrongest interferers for all jointly served termi-nals. Moreover, a high percentage of the servedusers are expected to gain from cooperation.

While network-wide cooperation would com-pletely eliminate the interference, a clusterednetwork leads inherently to residual inter-clusterinterference. It has been identified in earlyresearch that clustering and user selection is a

Our analysis in this

article leads to a

more advanced

interference

mitigation

framework. It

includes new

approaches for

clustering, user

grouping, interfer-

ence-floor shaping,

feedback compres-

sion, channel predic-

tion, and scheduling.


IEEE Communications Magazine • May 201446

NP-hard problem, where the optimum among avery large number of possible cluster configura-tions is searched. Efficient heuristics are neededfor real-time implementation. There are severalapproaches on different time-scales.

Cover-Shifts — On a longer timescale, a staticapproach is proposed to reduce overall interfer-ence. The CA is enlarged, for example, over threeadjacent sites with three sectors per site. In thisway, it becomes more likely that users have theirstrongest interfering cells inside the CA. But still,there are many users at the edge of the CA whereout-of-cluster interference is harmful. For thatreason, we introduced overlapping CAs, so-calledcover-shifts, on different radio resources. A termi-nal at the CA edge can then be scheduled intoanother cover-shift, where it is in the center ofthe CA. The cover-shift concept can be supportedby active antennas where a smaller tilt is appliedfor CA inbound and a stronger tilt for outboundbeams. With appropriate power allocation, theinterference floor is significantly reduced [6].

On a shorter timescale, flexible clustering anduser selection is useful. It is well known [7] thatthe performance can be improved by using flexibleuser-centric clustering. Clusters are thereby assem-bled from the most relevant interferers, beside theserving cell, according to the local interferencescenario at the terminal. Note that optimal userselection can be very complex. First approacheswith reduced complexity are proposed in [8].

Successive User Selection — In the following, anew heuristics for efficient user selection is pro-posed. It is evaluated in interference-limited macro-plus-small-cell scenarios measured in Berlin [10].The scenario is shown in Fig. 1, left. The downtilt atthe macrocells was set so that the vertical beamstouch the ground after 0.33 times the inter-site dis-tance. Channels to all cells were measured coher-

ently. Small cells with disjoint coverage were placedat five locations having high intercell interferencedue to macrocells (magenta dots in Fig. 1, left).

The main new idea is denoted as successiveuser grouping. Note that the classical approachto search for orthogonal channel vectors [9]requires extensive CSI reporting. In order toreduce the feedback, we propose to select a firstuser in the small cell, typically at the cell edge.This user is grouped with other users selectedrandomly in the other cells included in the clus-ter. If all users experience performance gainscompared to interference-limited transmission,user grouping is considered successful.

Otherwise, we replace users with reducedperformance by other users selected randomly aswell. Interestingly, in more than 80 percent ofcases, all users in the cluster profit from JTCoMP after the first trial (red circle, Fig. 1, cen-ter). Suitable clusters, in which all users gainfrom cooperation, can often be formed afteronly a few iterations. While successive searchhas a minor effect in the small cell (Fig. 1, cen-ter top), it improves the clustering success rateand thus the spectral efficiency in the macrocellsalso involved in the cluster (bottom).

By selecting the best resource blocks and thebest rank in each cell, the spectral efficiency evenexceeds a well-known bound for cluster-basedcooperation, where the spectral efficiency is lim-ited only by out-of-cluster interference. Note thatoccasional beamforming gains when using a lowrank were ignored when computing the bound(Fig. 1, right top). By forming the clusters andselecting the users as described above, obviously,the cluster size and all overheads due to pilots,feedback, and backhaul, and also the complexityof JT CoMP can be minimized, while the spectralefficiency approaches a theoretical limit [10].Sophisticated algorithms are obviously importantto achieve high JT CoMP gains.

Figure 1. Left: interference-limited macro-plus-small-cell scenario measured in Berlin [10]; center: user throughput statistics forall small cells (top) and all surrounding macrocells (bottom) using the same clustering and user selection and the full rank ineach cell over the full 20 MHz bandwidth; right: optimized performance after selecting the 25 percent best resource blocks (RBs)and the best rank in each cell, single- or multi-stream.

Interference limitedJT CoMP random clusterJT CoMP successive

Interference limitedJT CoMP same clusterJT CoMP random clusterJT CoMP successiveBound

Interference limitedJT CoMP successive + best RBJT CoMP succ + best RB + best rankBound

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FEEDBACK COMPRESSION ANDCHANNEL PREDICTION

In system-level simulations, for a 2 × 2 MIMOHetNet deployment with JT CoMP at 500 m dis-tance between macro sites, spectral efficienciesof 6–7 b/s/Hz and 4–5 b/s/Hz are achieved in thesmall cell and in surrounding macrocells, respec-tively, assuming ideal CSI. This is more than 100percent gain over multiuser MIMO, yielding lessthan 3 b/s/Hz. In reality, however, JT CoMP isvery sensitive to channel estimation errors dueto quantization and outdating of the CSI formobile terminals. In the following, we considerthe potential of both adaptive compression forthe CSI feedback with controlled performancedegradation and mitigation of the feedbackdelay by means of advanced channel prediction.

Feedback Compression — The overhead dueto the feedback of the CSI for JT CoMP is com-monly considered huge. As non-overlappingclusters are formed on different frequency sub-bands, the feedback can be limited to a fractionof the overall bandwidth [5]. Flexible clusteringand successive user selection lead to a situationwhere the feedback requested from the userscan be significantly reduced [10]. There is fur-ther potential by transforming the channel fromthe frequency domain, where the pilots aredefined, to the time domain and selecting themost relevant paths [11]. This can be achievedby estimating the noise and out-of-cluster inter-ference floor. The required quantization dependson this floor as well [12]. From these arguments,a combination of clustering, tap selection, andquantization is useful to compress the feedback.

We evaluated the combined potential of theseapproaches in [12]. Our reference case is similarto the JT CoMP link tested in [5]. A static feed-back scheme is used where a terminal provides fullCSI from its nearest 7 cells with 16 bits per realand imaginary values and per pilot. The numberof pilots is equal to the cyclic prefix length. Tapselection yields a feedback reduction by a factor of6, flexible clustering yields a factor of 3 due to themean cluster size, and adaptive quantization yieldsa factor of 2. Note that these contributions are notmultiplicative. Overall, the CSI feedback can bereduced by a factor of 15 compared to our refer-ence case. Note that feedback compression can beimplemented in real time [11, 12].

Channel Prediction — State-of-the-art predic-tion techniques like Kalman and Wiener filteringhave the potential to make JT CoMP links morerobust for CSI delays of a few milliseconds andat moderate mobility. Note that the precoder canbe adapted to different reliabilities of the pre-dicted channels. Kalman filters provide reliabilityinformation intrinsically, which can be reportedsemi-statically from the terminals [13, 14].

Doppler-delay-based prediction is a recentapproach. The channel for each link between atransmitter and a receiver antenna can be mod-elled by a number of multi-paths with their indi-vidual complex amplitude, delay and Dopplerfrequency. From the recent channel history, theseparameters can be estimated for each path. Next,the multi-path parameters are assumed to remain

static over short periods of time. Then the chan-nel can be predicted into the future by insertingthe estimated parameters into the channel model.This approach has been studied using the stan-dard spatial channel model (SCM) in [15] (Fig. 2,left). A significant improvement of the meansquare error (MSE) for the CSI can be achieved,even at 30 km/h and 2.6 GHz, by using a fewDoppler frequencies for each path.

In a further step, Doppler delay and Kalmanprediction were both tested over measured chan-nel data. Channels were measured coherently ata velocity up to 30 km/h at 2.66 GHz in 20 MHzbandwidth from three base stations in an urbanmacrocell scenario in Stockholm [13, referencestherein]. Thermal noise was set to –120 dBm persubcarrier, and 5-bit quantization was used forreal and imaginary parts of the channel coeffi-cient. For computing the Kalman filters, perfectCSI was assumed. For both methods, an order of10 (numbers of filter poles or Doppler frequen-cies) was found to be sufficient.

Results shown in Fig. 2, center, are based onthese measurements, using a channel history of50 ms, CSI updates every 2 ms, and a predictionhorizon up to 20 ms. For typical feedback delaysbetween 5 and 10 ms, significant improvementsof the MSE can be observed. The two methodsfollow different concepts. While the feedbackoverhead of the Doppler delay method is small-er, the sparse inverse discrete Fourier transform(DFT) used to extract the taps degrades the per-formance slightly. The Kalman filter is based ondamped sinusoids instead of perfect ones, limit-ing the achievable prediction accuracy [14].

The improved CSI translates to better perfor-mance. Without prediction in moving scenarios,JT CoMP gains can even be negative. Usingappropriate feedback intervals together with pre-diction, substantial gain can be realized by JTCoMP (Fig. 2, right). Note that irregularly, pre-diction fails. If prediction is also performed atthe terminal, it can be compared with the latestmeasurements. The network can be informedabout unusual prediction errors via a low-ratelow-latency feedback channel.

Feedback compression, shorter feedbackintervals, and a powerful predictor are regardedas important enablers for JT CoMP. A welldesigned feedback scheme reduces the overheadand counteracts the delay accumulated over theair and while the CSI is transported over thebackhaul. Ultimately, such a scheme allows forhigher mobility.

EVOLUTION TO 5G: AN INTEGRATED APPROACH

In the following, we propose a combination ofsmall cells, JT CoMP, and massive MIMO toincrease the spectral efficiency in 5G.

SMALL CELLS ANDENHANCED INDOOR COVERAGE

A natural way to improve network capacity is toplace further transmitters with moderate numbersof antennas at typical outdoor hotspot locations. Asoutlined above, more cells yield higher capacity if

A well designed

feedback scheme

reduces the over-

head and counter-

acts the delay

accumulated over

the air and while the

CSI is transported

over the backhaul.

Ultimately, such a

scheme allows for

higher mobility.

JUNGNICKEL_LAYOUT_Layout 5/7/14 11:59 AM 7


the frequencies are reused. High-speed data cover-age is improved, and the network diversity isincreased. Shorter distances between base stationsand terminals and a higher line-of-sight probabilityare further benefits. Deployment of small cells willprobably start at hotspots leading to inhomoge-neous cell layouts. More complex network planningis then required. Also adding small cells indoors isrelated to the use of more powerful enhanced wire-less local area networks to overcome the strongoutdoor-to-indoor penetration loss, which is oftenin the order of 10 to 20 dB and even more insidelarge buildings. On the other hand, less cooperationbetween indoor and macrocells is needed.

MASSIVE MIMO AND JT COMPUsing many more antennas (i.e., massive MIMO)is commonly regarded as an alternative to JTCoMP. For very large numbers of antennas, itwas shown recently that beamforming gains areso large that both intercell and inter-streaminterference can be very low. Spectral efficiencycan reach high values, like 100 b/s/Hz [4].

In combination with JT CoMP, macro-diversitygains can be exploited with more antennas thanstreams. This is useful to make the link morerobust. The deployment of more antennas is appro-priate to serve the increasing amount of users thatcan be expected if new services like device-to-deviceand machine-type communications are supported inthe future. Note the fundamental trade-off betweenspatial multiplexing and diversity gains.

Active antennas and massive MIMO arerelated topics. However, the increased numberof antennas is a big challenge for deployment athigh-rise sites because the wind load and visibili-ty are both increased. Today, macrocell panelantennas contain a stack of co-phased antennaelements that, all together, realize a fixed verti-cal beam with a certain down-tilt. Operatingeach antenna element in the panel independent-ly enables more flexible vertical beamforming.

The synergy between massive MIMO and JTCoMP is intuitive if we assume a simple fixedgrid of beams (GoB), shown in Fig. 3, left. Avertical GoB subdivides the cell into radial sub-sectors. The GoB can also be applied in theazimuth direction by increasing the number ofcolumns in the array. Obviously, interference canbe more localized using massive MIMO. Withmore antennas, interference is more likelybetween adjacent subsectors of the same sitethan between overlapping subsectors at differentsites. Thus, less cooperation between the sites isneeded, so the backhaul overhead associatedwith JT CoMP can be reduced.

In practice, more antennas need extra RFchains, extra analog-to-digital converters (ADCs,DACs), and more signal processing, which arenot free. While antenna elements can be verycheap, extra costs for RF frontends and signalprocessing are more relevant. More research isneeded to combine low-cost massive MIMOarrays with sufficient beamforming flexibility.

For 5G, we propose to combine massiveMIMO with the interference mitigation frame-work as laid out above, and to increase the num-ber of antenna elements only gradually so that theeffort for a required performance target is mini-mized. In this way, massive MIMO will be used to

Figure 2. a) average precision of the CSI vs. the feedback delay with andwithout Doppler-delay-based prediction, assuming perfect CSI at the ter-minal, for SCM with 30 km/h. The oscillation with no prediction is due totemporal correlation; b) the same for measured channel data. An autore-gressive Kalman filter without quantization for the CSI is used as a refer-ence; c) performance with and without predicted CSI in a scenario withseven cells and three users.

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localize the interference and improve the rank ofthe overall channel matrix, while the use of JTCoMP overcomes residual intercell interference.

Altogether, adding more antennas at macrocellsmay be viable in a future 5G system. Benefits ofmassive MIMO are that there will be no extra sitecosts, it supports cell-wide or even intercell loadbalancing, extra backhaul costs are small, and moresignal processing can be done at a single site.

INTEGRATED APPROACHOur envisioned 5G system concept is a sophisti-cated combination of these techniques, shown inFig. 4. We expect that 5G will natively supportsmall cells, JT CoMP, and massive MIMO toimprove the spectral efficiency, besides enablingother techniques such as infrastructure and spec-trum sharing between mobile operators.

The keys to operate the proposed system arecentralized or distributed joint signal processingto coordinate the entire network. Moreover, ahigh-speed backhaul is needed to interconnectan increasing number of base stations and deliv-er the increasing amounts of data. Note thatboth high capacity and low latency are requiredin the backhaul to achieve high spectral efficien-cy in the proposed architecture [16].

INITIAL EVALUATION RESULTSAs illustrated in Fig.1, the spectral efficiency ofthe proposed JT CoMP scheme is limited by out-of-cluster interference. When introducing smallcells, frequency can be fully reused, and the sameJT CoMP scheme can be used between macro-and small cells as between macrocells [10, 16].Synchronization and sufficient backhaul capacityare obviously needed. Compared to multi-userMIMO in LTE, the spectral efficiency per anten-na is increased by roughly 100 percent. Note thatour current results do not include massiveMIMO, which is for further research. For now,we show initial results to illustrate the enhancedrobustness. The potential of the combined 5Gapproach is quantified using extrapolation.

By adding more distributed transmitters(small cells) or co-located antenna elements(massive MIMO), the most important limitations

of efficient interference mitigation can be over-come. These are cases where the channel matrixhas low rank (e.g., for parallel beams formed ata single site in the same direction toward co-located terminal antennas) and where the powerof indoor terminals is low due to the high out-door-to-indoor penetration loss.

With a low-rank channel matrix, more poweris needed to separate the users. In Fig. 3, right,the normalization loss of a zero-forcing precoderis shown as a function of the CSI reference sig-nal index (i.e., frequency). Results are obtainedfrom ray-tracing simulations in the Munich areanear the NSN campus. Three users spaced lessthan 50 cm from each other are jointly servedover a distance of nearly 250 m. With 4 antennaelements, there is significant loss ranging from10 to 20 dB. With 16 antenna elements, theaverage loss is 0 dB despite the closely spacedusers. Simultaneously, there is an array gain of 6dB. Spatial diversity makes JT CoMP obviously

Figure 3. Left: Interference is more localized when using massive MIMO, which is beneficial for JT CoMP; right: moreover, inter-ference suppression becomes more robust, as explained in the section “Initial Evaluation Results.”

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Figure 4. Integrated 5G mobile network concept including small cells, JTCoMP, and massive MIMO.

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more robust in critical user constellations. More-over, it reduces the impact of residual impair-ments such as prediction errors.

Finally, we extrapolate our current system-level results, and quantify the achievablethroughput over the air and the required back-haul traffic in Fig. 5, right. We compare a triple-sectored 20 MHz 2 × 2 MIMO LTE macro-sitescenario with a visionary 5G HetNet using 100MHz spectrum, 10 small cells per sector, JTCoMP and a 16-element MIMO antenna arrayassuming an equivalent number of terminalantennas in each cell. In the HetNet, the small-cell traffic is aggregated at the nearest macro-cell. Recommendations from the NextGeneration Mobile Networks (NGMN) Allianceare used for backhaul estimation. Note that theinter-site ratio in [16] is smaller if more subsec-tors are formed using a GoB. With 16 × 16enlarged MIMO, the backhaul overhead for JTCoMP is approximately halved.

We assume that over-the-air traffic can bescaled multiplicatively by using small cells (10×),JT CoMP (3×), more antennas (8×), and morespectrum (5×) compared to LTE. Using thecombination of small cells, JT CoMP and mas-sive MIMO as proposed here, 1000 times moretraffic compared to a macrocell LTE deploymentmay be reached (circles, Fig. 5, right).

CONCLUSIONSOur vision of a future 5G system includes acombination of existing and novel technologieslike the deployment of small cells and enhancedlocal area networks and the joint use of both,and advanced interference mitigation based onJT CoMP and massive MIMO. We have quanti-fied the benefits of integrating small cells intothe JT CoMP concept as applied between macro-cells, assuming that synchronization and back-haul are feasible. Using sophisticated clusteringand user selection schemes, spectral efficiency islimited only by out-of-cluster interference. Whilechannel aging tends to ruin the gains of JT

CoMP for moving terminals, we have demon-strated that shorter reporting intervals combinedwith channel prediction enable high spectral effi-ciency. The combination of JT CoMP with mas-sive MIMO is beneficial because more antennasmake the link more robust, interference can bemore localized, and the backhaul overhead canbe reduced. Finally, we illustrate that 1000 timesincrease of mobile traffic is possible using asmart combination of all these concepts withreasonable effort. Also, we have outlined chal-lenges for future research.

ACKNOWLEDGMENTSThe authors acknowledge financial support bythe European Union for the projects ARTIST4G,METIS, and SODALES, the German FederalMinistry of Education and Research for the pro-ject CONDOR, the German Research Councilfor the project CoMPImpairments , and theSwedish Research Council for the frameworkproject Dynamic Multipoint Wireless Transmis-sion. All colleagues in these projects areacknowledged for the inspiring work atmosphereand their contributions. Special thanks go to D.Aronsson, M. Grieger, S. Jaeckel, W. Menner-ich, C. Oberli, T. Pfeiffer, F. Rosas, M Schlosser,and L. Schulz. The authors thank Alcatel LucentBell Labs and Ericsson Research for the mea-surement data used in this article.

REFERENCES[1] A. Osseiran et al., “Scenarios for the 5G Mobile and

Wireless Communications: The Vision of the METIS Pro-ject,” IEEE Commun. Mag., current issue.

[2] G.J. Foschini, K. Karakayali, and R. A. Valenzuela,“Coordinating Multiple Antenna Cellular Networks toAchieve Enormous Spectral Efficiency,” IEE Proc. Com-mun., vol. 153, no. 4, 2006, pp. 548–55.

[3] D. Gesbert and T. Svensson, Ed., Deliverable D1.4 inARTIST4G, “Interference Avoidance Techniques and Sys-tem Design,” https://ict-artist4g.eu/projet/deliverables.

[4] E. G. Larsson et al., “Massive MIMO for Next Genera-tion Wireless Systems”, IEEE Commun. Mag., vol. 52,no. 2, 2014, pp. 186–95.

[5] R. Irmer et al., “Coordinated Multipoint: Concepts, Per-formance, and Field Trial Results,“ IEEE Commun. Mag.,vol. 49, no. 2, 2011, pp. 102–11.

Figure 5. Left: Model of a heterogeneous mobile network using triple-sectored macro-sites. At two sites, small cells and a massiveMIMO array are deployed yielding subsectors due to the GoB. Backhaul is organized as a physical tree. Information exchangebetween cells for JT CoMP is passed over direct logical links using the next common aggregation point (AGP); right: over-the-air and backhaul traffic vs. the number of small cells.

Number of small cells per macrocell21

1G

100M

Ove

r-th

e-ai

r/ba

ckha

ul t

raff

ic (

b/s)

10G

100G

4 8 10

5G backhaul per macro-site downstream5G backhaul per macro-site upstream5G 16x16 MIMO, 100MHz, over-the-airLTE backhaul per macro-siteLTE 2x2 MIMO, 20 MHz, over-the-air

AGP

AGP AGP

AGP

AGP


https://ict-artist4g.eu/projet/deliverables


[6] W. Zirwas, W. Mennerich, and A. Khan, “Main Enablersfor Advanced Interference Mitigation,” Wiley OnlineLibrary, Trans. Emerging Tel. Tech., special issue LTE-A,vol. 24, 2013, pp. 18–31, http://onlinelibrary.wiley.com/doi/10.1002/ett.2567/pdf.

[7] A. Papadogiannis, D. Gesbert, and E. Hardouin, “ADynamic Clustering Approach in Wireless Networkswith Multi-Cell Cooperative Processing,” Proc. IEEE ICC,2008, pp. 4033–37.

[8] P. Baracca, F. Boccardi, and V. Braun, “A Dynamic JointClustering Scheduling Algorithm for Downlink CoMPSystems with Limited CSI,” Proc. IEEE Int’l. Symp. Wire-less Commun. Systems, Paris, France, 2012.

[9] A. Goldsmith et al., “Capacity Limits of MIMO Chan-nels,“ IEEE JSAC, vol. 21, no. 5, 2003, pp. 684–702.

[10] M. Lossow et al., “Efficient MAC Protocol for JTCoMP in Small Cells,” Proc. SmallNets Wksp., IEEE ICC,2013, pp. 1181–86.

[11] T. Wild “A Rake-Finger Based Efficient Channel StateInformation Feedback Compression Scheme for theMIMO OFDM FDD Downlink,” Proc. VTC-Spring, Taipeh,Taiwan, 2010.

[12] L. Schulz, K. Manolakis, and V. Jungnickel, “AdaptiveFeedback Compression for Joint Transmission Coordi-nated Multipoint,” Proc. IEEE VTC-Spring, Dresden, Ger-many, 2013.

[13] R. Apelfröjd, M. Sternad, and D. Aronsson, “Mea-surement-Based Evaluation of Robust Linear Precodingfor Downlink CoMP,” Proc. IEEE ICC, 2012.

[14] D. Aronsson, Channel Estimation and Prediction forMIMO OFDM Systems: Key Design and PerformanceAspects of Kalman-based Algorithms, Ph.D. thesis, Upp-sala Univ., 2011, http://www.signal.uu.se/Publications/pdf/a112.pdf.

[15] K. Manolakis et al., “Channel Prediction by Doppler-Delay Analysis and Benefits for Base Station Coopera-tion,” Proc. IEEE VTC-Spring, 2013.

[16]V. Jungnickel et al., “Backhaul Requirements for Inter-SiteCooperation in Heterogeneous LTE-Advanced Networks,”Proc. OWITSN Wksp., IEEE ICC, 2013, pp. 915–20.

BIOGRAPHIESVOLKER JUNGNICKEL [M’99] ([email protected]) received diploma and doctorate degrees in physicsfrom Humboldt University, Berlin, in 1992 and 1995,respectively. He worked on semiconductor quantum dotsand laser medicine before he joined the Fraunhofer HHI in1997. Since 2003, he has been an adjunct lecturer at theTechnical University of Berlin (TU Berlin), Germany, and pro-ject leader at HHI. Since 2013, he has been head of themetro, access, and in-house systems group. In his research,he has contributed to high-speed indoor optical wirelesslinks, the first 1 Gb/s MIMO-OFDM mobile radio transmis-sion experiments, the first real-time implementation andfield trial for LTE, and the first coordinated multipointexperiments. He has authored and co-authored 160 confer-ence and journal papers and 10 book chapters, and holds24 patents. He won best paper awards at PIRC 2004, VTC2010, and VTC 2013, and also the patent award of HHI in2006. He has served on numerous TPCs, as Track Chair forVTC 2007 and 2013, as Chair of WSA 2009, and as anAssociate Editor for IEEE Photonics Technologies Letters.

KONSTANTINOS MANOLAKIS ([email protected]) obtained a diploma degree from the Aristotle Uni-versity of Thessaloniki, Greece, in 2004 and an M.Sc. degreefrom TU Berlin in 2007, both in electrical engineering with afocus on telecommunications. From 2006 to 2012, he waswith Fraunhofer HHI. In 2012 he also joined the Telecommu-nication Systems Department of TU Berlin, where he is cur-rently working toward his doctoral degree. His researchinterest is signal processing for communications in cellularnetworks with a focus on the impairment effects of coordi-nated multi-point transmission. He has authored and co-authored more than 25 conference and journal papers, andreceived a best paper award at IEEE VTC 2013.

WOLFGANG ZIRWAS ([email protected]) obtained adiploma degree in communication technologies in 1987from TU Munich, Germany. He joined Siemens centralresearch laboratory for communications in Munich in 1987.Since 1996 he has contributed to and also partially lednational and EU projects like ATMmobil, COVERAGE, 3GeT,ScaleNet, WINNER, ARTIST4G, and METIS. His research inter-ests include multihop, MIMO, cooperative antennas, andOFDM. At the end of 2004, he led the project that realized

the 1 Gb/s MIMO-OFDM world record. During 2006–2007,he demonstrated LTE and virtual MIMO, and attended 3GPPLTE standardization meetings on MIMO. He has authoredand co-authored 120 conference and journal papers, con-tributed to four books, and filed more than 200 patents. Hewas the Inventor of the Year within SIEMENS and NSN in1997, 2007, 2008, and 2009, and received a best paperaward at FuNeMS in 2012. He served on the TCPs of Euro-pean Wireless, PIMRC, and WDN-CN 2012, and as AssociateEditor of the Scientific World Journal.

BERTHOLD PANZNER ([email protected]) received aM.Sc. degree in telecommunications from Linköping Uni-versity, Sweden, in 2007 and a doctoral degree inmicrowave engineering from Otto-von-Guericke University,Magdeburg, Germany, in 2012. His research was focusedon automotive radar at 80 GHz and synthetic apertureradar techniques. He is currently working as a senior radioresearch engineer at NSN in the radio system researchgroup in Munich, Germany, where he is involved in thedesign of physical layer aspects and massive MIMO for 5G.he received best paper awards at IEEE APS 2008 and theInternational Radar Symposium 2012.

VOLKER BRAUN ([email protected]) has beenwith Alcatel-Lucent Bell Labs (formerly Alcatel Research &Innovation), Stuttgart, Germany, since 1999. He led thedevelopment of the base station software for the HSPAfast radio resource management, and defined and integrat-ed an LTE pre-standard prototype system used for earlycustomer mobility trials. He further pioneered the conceptof outdoor infrastructure small cells. Currently he is work-ing on various 5G research aspects.

MORITZ LOSSOW ([email protected]) received B.Eng.and M.Eng. degrees in communication and informationengineering from the University of Applied Sciences ofBerlin in 2011 and 2012, respectively. Until 2013, he waswith the Wireless Communications and Networks Depart-ment at Fraunhofer HHI, where he investigated cooperativeprocessing in heterogeneous cellular networks. In 2013, hejoined the Wireless Technology and Networks Department,Deutsche Telekom AG, Telekom Innovation Laboratories,where his current research interests include statistical datatraffic modeling and MIMO techniques in cellular networks.

MIKAEL STERNAD ([email protected]) received a doc-toral degree in automatic control in 1987 from the Institute ofTechnology at Uppsala University, Sweden. He is now a pro-fessor for automatic control at Uppsala University in Sweden.His main research interest is signal processing applied tomobile broadband communications, such as channel predic-tion for fast link adaptation, scheduling, and coordinated mul-tipoint transmission, but also overall system aspects such asefficient MAC layer design. He is also active in audio signalprocessing, and co-founder of the company Dirac Research inthis field. He led the national 4G research project Wireless IPand the Swedish Research Council Framework project Dynam-ic Multipoint Transmission. He was also engaged in the EUprojects WINNER and Artist4G, and is active in METIS on 5Gnow. He has published more than 110 conference and 34journal papers, and six book chapters, and holds 10 patents.

RIKKE APELFRÖJD ([email protected]) completedher M.S. degree in engineering physics at Uppsala Universi-ty in 2011. Since then, she has been a Ph.D. student atUppsala University. She has been involved in the EU projectArtist4G and works on the METIS project now. Her mainresearch interests are channel prediction and robust pre-coding for coordinated multipoint transmission.

TOMMY SVENSSON [S’98, M’03, SM’10] ([email protected]) received a doctoral degree in information the-ory in 2003. He is associate professor for communicationsystems at Chalmers University, where he is leading theresearch on air interface for wireless access and wirelessbackhauling networks for future wireless systems. He hasbeen working with Ericsson AB on core networks, radioaccess networks, and microwave transmission products. Hismain research interests are in design and analysis of physi-cal layer algorithms, multiple access, resource allocation,cooperative systems, and moving relays/cells/networks. Hehas co-authored more than 100 conference and journalpapers, two books, and more than 30 EU projects reports.He is responsible for the Master’s program on communica-tion engineering at Chalmers, and serves as Chairman ofthe Swedish joint IEEE Vehicular Technology, Communica-tions, and Information Theory Societies chapter.

Our vision of a

future 5G system

includes a combina-

tion of existing and

novel technologies

like the deployment

of small cells and

enhanced local area

networks and the

joint use of both,

advanced interfer-

ence mitigation

based on JT CoMP,

and massive MIMO.


mailto:[email protected]











http://onlinelibrary.wiley.com/doi/10.1002/ett.2567/pdf

http://www.signal.uu.se/Publications/pdf/a112.pdf