IEEE VEHICULAR TECHNOLOGY MAGAZINE (TO APPEAR) 1

Massive MIMO and Millimeter Wave for 5GWireless HetNet: Potentials and Challenges

Tadilo Endeshaw Bogale, Member, IEEE and Long Bao Le, Senior Member, IEEE

Abstract— There have been active research activities worldwidein developing the next-generation 5G wireless network. The 5Gnetwork is expected to support significantly large amount ofmobile data traffic and huge number of wireless connections,achieve better cost- and energy-efficiency as well as qualityof service (QoS) in terms of communication delay, reliabilityand security. To this end, the 5G wireless network shouldexploit potential gains in different network dimensions includingsuper dense and heterogeneous deployment of cells and massiveantenna arrays (i.e., massive multiple input multiple output(MIMO) technologies) and utilization of higher frequencies, inparticular millimeter wave (mmWave) frequencies. This articlediscusses potentials and challenges of the 5G heterogeneouswireless network (HetNet) which incorporates massive MIMOand mmWave technologies. We will first provide the typicalrequirements of the 5G wireless network. Then, the significanceof massive MIMO and mmWave in engineering the future 5GHetNet is discussed in detail. Potential challenges associated withthe design of such 5G HetNet are discussed. Finally, we providesome case studies, which illustrate the potential benefits of theconsidered technologies.

I. INTRODUCTION

Research on next-generation 5G wireless systems, whichaims to resolve several unprecedented technical requirementsand challenges, has attracted growing interests from bothacademia and industry in the past few years. More than 5billion devices demand wireless connections that runs voice,data, and other applications in today’s wireless networks [1].The study performed by Wireless World Research Forum(WWRF) has predicted that 7 trillion wireless devices willbe served by wireless networks, both for human and machine-type communications, in 2017. Furthermore, the amount ofmobile data traffic has increased dramatically over the years,which is mainly driven by the massive demand of data-hungrydevices such as smart phones, tablets and broadband wirelessapplications such as multimedia, 3D video games, e-Health,Car2X communications [1]–[3]. This trend will continue andit is expected that the 5G wireless network will deliver about1000 times more capacity than that of the current 4G system.

In addition, significant improvement in communicationsquality of service (QoS) is expected in the 5G network. Inparticular, real time support will be the key requirement torealize many emerging wireless applications. In fact, real timeis a highly subjective term and depends on specific use case.According to [4], a service can be defined as real time whenthe communication response time (i.e., round-trip latency) is

The authors are with the Institute National de laRecherche Scientifique (INRS), Montreal, QC, Canada. E-mails:{tadilo.bogale, long.le}@emt.inrs.ca

faster than the time constants of the application. Moreover,different use cases require different round-trip latency ofinteractions. For instance, the latency requirement for audiosignal is around 80ms which can be supported by the currentLong-term evolution (LTE) with typical round-trip latency of25 ms [1], [5]. Although the LTE latency is fairly sufficientfor most current services, it is anticipated that a sheer numberof new use cases which require very small latency will arise inthe future 5G network including two-way gaming, virtual andenhanced reality (e.g., networked wearable computed devices),and touch screen enabled applications (i.e., a tactile internet).Among these use cases, the tactile internet requires a morestringent latency requirement which is in the order of 1ms[4], [6]. In fact, it has been predicted that tactile internet willinfluence our daily lives and will transform different importantsocioeconomic sectors such as health care, education, smartgrid and intelligent transportation.

Moreover, there is a strong need to achieve these technicalrequirements while improving the cost and energy efficiencyof the future wireless network. In fact, the exponential growthof mobile data in recent years contradicts the flattening of therevenue of mobile operators. Thus, it is very desirable that thenext-generation 5G network can realize cost efficient, whichis measured in $/bit, wireless technologies. In fact, energyrelated costs account for significant portion of the overalloperational expenditure (OPEX) of wireless operators [7]. Inparticular, more than 70% of the mobile operator’s electricitybill is due to the radio part of the wireless cellular network[7], [8]. Also, the Carbon dioxide (CO2) contribution of thetelecommunications sector to the global CO2 emission hasincreased rapidly over the last decade where mobile operatorsare among the top energy consumers. Thus, apart from spectralefficiency, energy efficiency is a crucial design objective toreduce the operation cost for mobile operators as well as tominimize the environmental impact of the wireless domain.

To resolve the aforementioned challenges, it becomes es-sential to adopt a network infrastructure that can efficientlyintegrate various disruptive wireless technologies and to enableinter-networking of existing and newly-deployed technologies.Such development should consider the emerging wirelessapplications and services in the short, medium, and long terms.In particular, the 5G network should enable us to realize thetruly networked society with unlimited access of informationfor anyone, anywhere, and anytime. It should also allow tosupport various smart infrastructures and smart cities that aregreen, safe, mobile, connected, and informed [1]. The airinterface spectral efficiency, available spectrum bands and thenumber of deployed base stations (BSs) are the key contrib-

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utors to the performance of any network. Towards this end,it is undoubtedly important to realize network densificationin multiple dimensions including deployment of super-denseHetNets with different types of cells, multiple radio accesstechnologies (RATs), massive multiple input multiple output(MIMO) at BSs and/or user equipments (UEs) as well asexploitation of both microwave and mmWave frequency bands.The realization of these technologies will lead to the full-scale5G HetNet, which poses various challenges at the architectureas well as communications and networking levels.

In this article, we first provide the concise description oftechnical requirements of the 5G wireless network. Compre-hensive discussions on the considered 5G HetNet and itsassociated research challenges are then provided. After thatwe describe how massive MIMO and mmWave technologiescan help address some of the challenges via case studies. Inthe following, we use the terms node and BS interchangeably.

II. REQUIREMENTS OF 5G WIRELESS NETWORK

The 5G wireless network has not been standardized yet.The detailed and exact technical specifications of this networkwould only be available in the near future. However, thefollowing technical requirements are accepted by wirelessindustries and academia [1]–[3].

• Coverage and Data rate: It is believed that the 5Gshould maintain connectivity anytime and anywhere witha minimum user experience data rate of 1Gb/sec [9].In general, as the low mobility UEs channel changesmuch slower than those of high mobility ones, theseUEs require more resources for CSI acquisition (i.e.,reduced effective data rate). Therefore, the peak datarates required by high and low mobility users in the 5Gnetwork can be different. The network must also ensurea certain QoS for users traveling at very high speed(e.g., high speed trains traveling at 500 km/hr) wherethe existing networks cannot satisfactorily support1.

• Latency: The latency requirement is usually more dif-ficult to achieve compared to that of the data rate asit demands that the data be delivered to the destinationwithin a given period of time. For the 5G network, theend-to-end latency requirement will be in the order of1-5ms [2], [3], [6].

• Connected devices: The future 5G network is expectedto incorporate massive amount of connected deviceswhich may reach up to 100 times that of the currentwireless network. The most potential use cases in thisregard are wearable computing, machine type communi-cations, wireless sensors, and internet of things [1], [7].Importantly, these connected devices may have differentrequirements in terms of communication rate, delay andreliability.

• Multiple RATs: The 5G network would not be devel-oped to replace current wireless networks. It is ratherto advance and integrate the existing network infras-tructures with the new one. In the 5G network, theexisting wireless technologies including Global system

1The 4G network can support the mobility of up to 250 km/hr.

for mobile communications (GSM), 3G, High SpeedPacket Access (HSPA), LTE and LTE-advanced as wellas WiFi will continue to evolve and be integrated into aunified system [1], [7].

• Energy and cost efficiency: 5G wireless technologiesmust be designed to achieve significantly better costefficiency measured in bit/$ in order to address therevenue flattening concerns of mobile operators. In par-ticular, the energy efficiency measured in bit/Joule ofthe 5G network may need to be reduced by a factorof 1000 compared to that achieved by current wirelesstechnologies [1].

III. 5G WIRELESS HETNET BASED ON MMWAVE ANDMASSIVE MIMO

Network densification via massive deployment of cellsof different types such as macrocells, microcells, picocells,and femto cells is a key technique to enhance the networkcapacity, coverage performance, and energy efficiency. Thisdensification approach has been adopted in existing wirelesscellular networks in particular 3G and 4G LTE systems,which essentially results in a multi-tier cellular HetNet [8],[10]. Wireless HetNets may also comprise remote radio heads(RRHs) and wireless relays, which can further boost the net-work performance. It is anticipated that relaying and multihopcommunications will be among central elements of the 5Gwireless architecture (in contrast to the existing LTE systemwhere multihop communications have been considered as anadditional feature) [3]. In general, radio resource managementfor HetNets plays a crucial role in achieving the benefits of thisadvanced architecture. Specifically, development of resourceallocation algorithm that efficiently utilizes radio resourcesincluding bandwidth, transmission power and antenna whilemitigating inter-cell and inter-user interference and guaranteesacceptable QoS for active users is one of the most criticalissues. In addition, design and deployment of reliable back-haul networks that enable efficient resource management andcoordination are also very important.

It is believed that massive MIMO and mmWave technolo-gies provide vital means to resolve many technical challengesof the future 5G HetNet and they can seamlessly be integratedwith the current networks and access technologies. The de-ployment of the massive number of antennas at the transmitterand/or receiver (massive MIMO) can significantly enhancethe spectral and energy efficiency of the wireless network[11], [12]. In a rich scattering environment, these performancegains can be achieved with simple beamforming strategiessuch as maximum ratio transmission (MRT) or zero forcing(ZF) [11]. Moreover, most today’s wireless systems operateat microwave frequencies below 6GHz. The sheer capacityrequirement of the next-generation wireless network wouldinevitably demand us to exploit the frequency bands above6GHz where the mmWave frequency ranging 30GHz-300GHzcan offer huge spectrum, which is still under-utilized [1], [2],[13]. Most importantly, as the mmWaves have extremely shortwavelength, it becomes possible to pack a large number ofantenna elements in a small area, which consequently helprealize massive MIMO at both the BSs and UEs.

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Fig. 1. Potential 5G HetNet network architecture incorporating massive MIMO and mmWave.

In particular, mmWave frequencies can be used for outdoorpoint-to-point backhaul links or for supporting indoor high-speed wireless applications (e.g., high-resolution multimediastreaming). In fact, mmWave technologies have already beenstandardized for short-range services in IEEE 802.11ad. How-ever, these frequencies have not been well explored for cellularapplications. Some potential reasons are the high propagationloss, penetration loss, rain fading and these frequencies areeasily absorbed or scattered by gases [14]. The massivedeployment of small cells such as pico and femto cellsin the future 5G HetNet renders the short-range mmWavetechnologies very useful. Therefore, the mmWave frequenciescan be considered as one of the potential technologies to meetthe requirements of the 5G network.

There are many possibilities to enable the 5G wireless Het-Net incorporating massive MIMO and mmWave technologies.One such 5G network architecture is shown in Fig. 1 where wedemonstrate how massive MIMO and mmWave technologiescan be used in different parts and communication purposes.The architecture of Fig. 1 employs both millimeter wave andmicrowave frequencies. To determine the operating frequencybands of different communications in the architecture of Fig.1, several factors may need to be considered such as the regu-latory issues, application, channel, and path loss characteristicsof various frequency bands. In general, path loss increases asthe carrier frequency increases. This observation leads to theutilization of microwave frequencies for long-range outdoorcommunications.

In mmWave frequency bands, different frequencies havedistinct behaviors. For example, naturally oxygen molecule(O2) absorbs electromagnetic energy at 60GHz to a muchhigher degree than in the regions (30 − 160)GHz in general.This absorption weakens (attenuates) 60GHz signals overdistance significantly; thus, the signals cannot reach far awayusers. This makes the 60GHz suitable for high data-rate and

secure indoor communications. Hence, selection of operatingfrequency depends on several factors such as application,different absorptions and blockages. Given these factors, how-ever, there is a general consensus that mmWave frequencybands (30-300)GHz can be useful for backhaul links, indoor,short range and line of sight (LOS) communications.

Generally, the deployment of multiple antennas at the trans-mitter and/or receiver improves the overall performance of awireless system. This performance improvement is achievedwhen the channel coefficients corresponding to differenttransmit-receive antennas experience independent fading. Fora given carrier frequency, such independent fading channel isexhibited when the distance between two antennas is at least0.5λ, where λ is the wavelength [12], [15]. Thus, for fixedspatial dimension, the number of deployed antennas increasesas the carrier frequency increases which consequently allowsto pack a large number of antennas at mmWave frequencies.Moreover, the deployment of massive MIMO can be realizedat different transportation systems such as trains and buseseven at microwave frequency bands since sufficient space isavailable to do so (i.e., as in Fig. 1).

In recent years, three dimensional (3D) and full dimensional(FD) MIMO techniques have been promoted to increase theoverall network efficiency as they allow cellular systemsto support a large number of UEs using multiuser MIMOtechniques. Thus, the massive MIMO and mmWave systemsof the considered architecture can also be designed to beeither 3D or FD. On the other hand, this architecture cansupport coordinated multipoint (CoMP) transmission whereBSs are coordinated using either fiber or wireless backhauls.In addition, Fig. 1 incorporates a cell virtualization conceptwhere the virtual cell can be defined either by the network(network centric) or the users (user-centric), and can also beintegrated as part of the cloud radio access network [16], [17].

IV. RESEARCH CHALLENGES

The 5G network presents significantly enhanced require-ments compared to those in existing wireless networks. Whileit is expected that the wireless HetNet incorporating massiveMIMO and mmWave technologies enables us to meet thetechnical requirements of the 5G network, there are variouschallenges one has to tackle. In the following, some keychallenges and the potential solution approaches are discussed.

A. Network Planning and Traffic Management

Network planning considering the future mobile data trafficdemand for the 5G wireless HetNet is obviously an importantissue to address. In fact, mobile operators would only optimizethe placement and planning of certain network nodes (BSs),namely the numbers and locations of macro, micro, and picoBSs. This optimization must be performed considering thata large number of low-power femtocells are unplanned andinstalled quite arbitrarily by the end users. Moreover, the factthat large amount of mobile data traffic can be offloaded fromone RAT to another tier of the same RAT or a different RATmust be taken into account in the network planning. Thisdesign task can be even more complicated since the networkcapacity offered by the indoor infrastructure may not be easyto estimate and can indeed vary over time.

Hence, existing planning tools for site locations such asusing evenly spaced lattice structure with hexagonal grid isunlikely a good solution for all network tiers even thoughit may be still a reasonable solution for the macro BSs. Inthe current cellular network, the site locations and zoning ofthe BSs are determined from limited but expensive field testwhile the link and system level modelings take into accountthe carrier frequency, propagation environment and antennacharacteristics. However, the usefulness of this site acquisitionapproach may need to be verified for the 5G wireless HetNet.

On the other hand, as the 5G network can be operatedat both microwave and mmWave frequencies, the optimalfrequency management and planning strategy is also not clear.This is especially because the cellular network has asym-metric uplink and downlink traffic demands. Furthermore,the mmWave frequencies have high penetration losses, whichare easily absorbed or scattered by gases [14]. Consequently,the frequency management strategy at mmWave frequencybands may perhaps be location dependent (e.g., more mmWavefrequency bands can be assigned when the surrounding envi-ronment has less buildings). In summary, the network planningand optimization of the 5G network should take into accountseveral unique parameters such as high cell density, extremelytime-varying traffic and the fact that various frequency regionshave quite different propagation characteristics.

B. Radio Resource Management

The capacity of future 5G networks would increase sig-nificantly with considerably higher cell densification, usageof higher mmWave frequencies and the advanced massiveMIMO technologies. However, this heterogeneous networkarchitecture poses several challenges for radio resource man-agement. At mmWave frequency bands, the channel vector

of each UE would have the specular characteristic whichis the result of very few scatterers. Consequently, the beampattern of a given UE is concentrated mainly around thedirection of LOS path. Thus, UEs having different LOSpaths would minimally interfere with each other. However,since the signal power of mmWave communication systemsexperiences high attenuation and the transmission bandwidthof mmWave systems is fairly large, noise will be a limitingfactor for mmWave frequency communications. On the otherhand, microwave channels are formed from several scatterersarising from different directions. Consequently, the beam-forming pattern designed for one UE would create stronginterference to all other UEs. For this reason, interference is amajor limiting factor for microwave communication systems[6]. Thus, interference management is very crucial for mi-crowave frequency bands. In particular, inter-user and inter-cell interference becomes more challenging to manage in thedense environment. This is because the cross-tier interferencebetween macrocells and small-cells can only be mitigatedefficiently if heavy coordination between the network tiers isperformed by using high-speed and reliable backhauls, whichare difficult to realize in practice.

Simple interference management methods based on or-thogonal spectrum allocation approaches can be applied [2],which are, however, not so efficient for the dense 5G HetNetoperating in the limited microwave spectrum. In fact, the Fed-eral Communications Commission (FCC) in USA have foundthat more than 80% of the microwave frequency spectrumbands are not utilized efficiently. Thus, one way of improvingthe spectral efficiency of these bands is to adopt a moreflexible management approach of available spectrum using thecognitive radio (CR) technology. In particular, the spectrumutilization of the 5G network can be improved by dynami-cally detecting the unused spectrum (i.e, white spaces) usingdifferent spectrum sensing algorithms. Then, interference-freetransmissions between UEs and small-cell (macrocell) BSs canbe enabled using the detected white spaces [18]. Differentnon-orthogonal spectrum management approaches have alsobeen recently developed but most of them require cooperationamong BSs of different tiers. Thus, it is preferable that thefuture 5G network relies on autonomous interference man-agement techniques that enable low-complexity, distributedsolutions and possibly CR technologies.

Furthermore, in recent years, 3D and FD MIMO beamform-ing techniques have been promoted to increase the spectral andenergy efficiency of MIMO systems since these techniqueswould allow cellular systems to support a large number ofUEs using multiuser MIMO techniques both for single celland CoMP multicell systems. On the other hand, differentrequirements of the 5G network can be in conflict in such away that improving one will degrade the others. To efficientlyexploit all available resources, the future 5G network designmust handle the existence of multiple objectives and inherenttradeoffs among them [19]. In particular, backhaul constrainedmulti-objective CoMP FD MIMO transceiver design presentsan interesting design example with unique challenges. Tothis end, massive MIMO systems provide a large degrees offreedom, which not only considerably increases the system

spectral and energy efficiency but also fundamentally eases theinterference management. For instance, in a multicell setup,the massive MIMO capacity gains can be achieved with simpleand uncoordinated beamforming schemes such as MRT or ZF(i.e., whenever there is no pilot contamination) [11]. However,since complete removal of pilot contamination is not a trivialtask2, CoMP transmission could be employed to reap thefull benefits of massive MIMO systems. However, enablingCoMP transmission for massive MIMO systems may requiresignificant coordination between different cells which maybe practically infeasible as the number of UEs and antennasdeployed in the system is likely very large. In contrast, certaindegrees of freedom (i.e., antenna elements) offered by themassive MIMO can be leveraged to mitigate or cancel multi-cell and/or cross-tier interference via suitable beamformingtechniques. How to fully exploit massive MIMO to mitigatethe interference among different cells with negligible BS co-ordination, and handle multi-objective designs is a non-trivialchallenge.

The low-power BSs of the 5G HetNet can be installedand managed by both wireless operators (such as pico BSs)and end users (such as femto BSs). Unlike the operator-installed low-power BSs, the low-power BSs which are de-ployed by the end users are installed quite arbitrarily. Inaddition, the unplanned low-power and indoor BSs can sig-nificantly outnumber the planned ones while the low-powerBSs would be the main capacity drivers of the future 5Gnetwork since more than 70% of the traffic arises from theindoor environment. Therefore, these low-power nodes shouldpossess self-configuration, self-optimization, and self-healingfunctionality (i.e., self organized network (SON)) in resourceand interference management so that efficient and scalableultra dense network deployment can be achieved. In thisregard, ensuring cost-effective customer satisfaction will be thecritical challenge of the SONs. On the other hand, the resourceallocation approach of the 5G network, unlike the traditionalapproach (which rely mainly on bit error rate), may needto consider different context information such as application,environment, and required QoS in terms of the latency, biterror rate, and minimum data rate requirements. The contextconsiders the user location, mobility, other proximity devices,resolution, central processing unit (CPU), battery level of thedevice. How to deploy the SON that can enable a cost-effectivecontext-aware resource allocation approach is a challengingproblem.

The 5G network is expected to achieve a minimum rateof 1Gb/s in an energy-efficient manner. One potential wayto achieve these goals is to adopt the virtual cell (soft cell)concept which has been proposed for the dense wirelessHetNet [20]. Specifically, radio resources available to a groupof heterogeneous cells (macro, micro and femto cells) areconsidered a single resource pool to serve each UE, which inturn views the group of cells as a big macrocell. Furthermore,the idea of phantom cell, which was originally proposedby NTT DoCoMo, can be employed to better manage the

2Note that one can efficiently mitigate the effect of pilot contamination (seeSection IV-E).

control and data planes at different frequencies and nodes[21]. Specifically, the control and data planes are split so thatcritical control data is transmitted over reliable microwavelinks between UEs and macro BSs while high-speed datacommunications between UEs and small-cell BSs are realizedover mmWave frequency bands. Consequently, a reliable andstable communications can be maintained while reaping thebenefits of mmWave bands3. Also, macro BSs are utilizedto ensure a wide-area coverage so as to maintain goodconnectivity and mobility. On the other hand, in a massivedeployment of small cells, separating the control and dataplanes will improve the energy efficiency of the network [21].These design approaches advocate the user-centric principlewhere much more research studies are expected to realize itin practice.

C. Cell Association and Mobility Management

In the dense HetNet with multiple tiers and multiple RATssuch as cellular technologies of different generations, WiFi andWorldwide Interoperability for Microwave Access (WiMaX),each UE can have several cell association options duringthe lifetime of its active sessions. Cell association shouldbe designed to efficiently utilize the network resources andprovide acceptable QoS for UEs. In general, small-cells suchas picocells, femtocells, and WiFi are preferable to serve lowmobility UEs whereas, high mobility UEs need to be servedby the macro BS to avoid frequent handover. Furthermore,macro BSs should also fill the coverage holes of the networkto reduce the call drop probability. For instance, in a HetNetnetwork, a typical pico BS has a maximum cell radius ofaround 200m, and if one tries to associate a high-speed trainUE traveling at 250 km/hr to this BS, handover may needto take place in few seconds, which is quite undesirable.Nevertheless, the development of an optimal cell associationmetric taking into account different factors such as signalquality, interference, traffic loads, data offloading capabilityand mobility is one of the key research issues. Moreover, theemployed metric should enable decentralized implementationwith low-signaling overhead. Traditional association metricsbased on signal strength or signal to noise ratio would notbe sufficient for the future 5G HetNet. Obviously, the perfor-mance of the 5G network can be improved further if each UEcan be associated with more than one nodes as in the virtualcell. Moreover, cell association must be jointly designed withthe mobility management so that low speed and high speedUEs can be treated differently. In fact, high speed UEs areusually inside vehicles which should be served by macro BSsto avoid frequent handoffs. Furthermore, since vehicles canenable us to deploy massive MIMO, the reliability of theseUEs can be improved significantly (see Fig. 3 in Section V).

Offloading UEs to another tier of the same RAT or toanother RAT offers an effective strategy to meet the require-ments of the future 5G HetNet. In this respect, a fractionof user traffic can be rerouted either to another tier of thesame RAT or to another RAT. Cell range expansion is one

3Sporadic interruptions of the data plane during the handover processbetween two small cells become less harmful.

practical approach to offload traffic from macro BS to lowpower nodes. To further improve the offloading capability,operators can deploy their own WiFi APs to relieve the trafficcongestion. These approaches may not be an ideal solutionfor the future network. To achieve the requirements of the5G network, the offloading approach for the 5G networkmay need to exploit the advantages of all RATs. In a typicalmetropolitan environment where a number of collocated RATsexists, discovering the best RAT for traffic offloading is nottrivial. This is because different RATs can be administeredby different operators (in the case of WiFi AP for example).On the other hand, as inter-RAT offloading utilizes the re-sources of other RAT, the offloading revenue should be sharedbetween the RAT operators fairly. This offloading approachis particularly useful to disseminate non-private and delaytolerant information such as bulk data files, news, softwareand files generated by laboratory experiments, and sensornetworks which account for 64% of the current world mobiledata traffic. Furthermore, the offloading method may need toconsider location, user, and time-dependent RAT availabilities,incorporate both licensed and unlicensed bands at mmWavefrequencies such as WiGIG, and take into account the seamlessservice delivery switching time constraint between differentRATs which is around 10ms [1]. Designing a cell associationand traffic offloading algorithm by integrating all these issuesis a non-trivial and challenging problem.

D. Backhaul Design

Backhaul links are required for data and signaling ex-changes between BSs and between the core and access net-works (i.e., the system of BSs). In general, high-capacityand reliable backhaul links support different types of trafficand cooperation between different BSs, which consequentlyimproves the user experience and overall throughput of thenetwork. There has been active research on the advancedCoMP transmission and reception techniques for the LTE-based system over the past few years. For the future denseHetNet, deployment of an efficient backhaul network sup-porting coordination and signaling among BSs of differenttypes is undoubtedly required. Studies in 2012 show that thebackhaul of the existing cellular network comprises of 70%cooper, 10% fiber and 15% wireless backhaul. And it wouldbe preferable to deploy more wireless over wired backhaultechnologies although wired backhaul using optical fiber andinternet protocol (IP) cables would still be needed in the5G network. In the existing wireless backhaul, large physicalaperture antennas are used to achieve the required link gainwhich is not economically desirable. Therefore, engineeringof the cost-effective, reliable, and scalable wireless backhaulnetwork will be one of the fundamental challenges of the future5G network.

MmWave and massive MIMO technologies provide greatopportunities to resolve these challenges. Specifically, one canform a large number of beams to establish point-to-multipointbackhaul links by using massive MIMO [11]. In addition,mmWave offers virtually unlimited bandwidth for short-rangebackhaul links, which would be sufficient for the future dense

HetNet (For instance, around 12.9 GHz of bandwidth isavailable in the E-band). This is illustrated in the hierarchical(mesh) backhaul network shown in Fig. 1. The massive MIMObeamforming, if designed appropriately, can realize mmWavenarrow beams which provide a unique opportunity to havescalable backhaul network with minimal (negligible) interfer-ence. One can utilize simple resource allocation approachessuch as static partitioning (usually in frequency domain) forthe access and backhaul networks. A more efficient approachis to allow the backhaul and access networks share the sameresource pool. By doing so, the deployment flexibility of thenetwork can be improved [15]. In this direction, some researchworks are performed including design, resource allocation andscheduling mechanisms [22]. However, the unique challengeof the latter approach is that as a low-power node operates onthe same frequency both for backhaul and access links, eachnode may need to allocate backhaul resources dynamicallyaccording to its load and the meshed low power nodes. For thisreason, re-engineering the backhaul may need to be consideredtaking into account the time varying load characteristics of alllow-power nodes.

E. Low-Cost CSI Acquisition and Beamforming for MassiveMIMO

Beamforming is the key tool to exploit the potentials ofMIMO systems, which typically requires the availability ofchannel state information (CSI) at the transmitter and/orreceiver. For the future 5G network that employs massiveMIMO at the microwave and/or mmWave frequency bands,CSI estimation and beamforming are the key design issueswhich strongly impact the network performance. The existingMIMO systems (such as LTE) are equipped with a few numberof antennas N (from 1 to 10). For such a case, the numberof radio frequency (RF) chains, digital to analog converters(DACs) and analog to digital converters (ADCs), which arethe most expensive parts of a wireless transceiver, can bethe same as that of the number of antennas. However, in amassive MIMO system with 100 to 1000 antennas, deployingN RF chains is not practically feasible4. On the other hand,the energy consumptions of a MIMO transceiver increaseswith the number of active RF chains. Specifically, when thetransmission bandwidth is very large, the energy consumptionof ADCs would be unacceptably high. Thus, the channelestimation and beamforming algorithms should be designedtaking into account the constraints on the number of RF chains(i.e., to be much less than N ) and the finite resolutions ofADCs.

In a conventional channel estimation (i.e., N RF chainscase), the channels between transmitters and receivers areestimated from orthogonal pilot sequences which are limitedin number due to the finite coherence time of the channel. Ina massive MIMO setup, as N � K (i.e., the number of UEs),time division duplex (TDD) based communication would bemore efficient where the pilot symbols are transmitted from

4Note that the number of RF chains is the same as that of DACs at thetransmitter, and ADCs at the receiver. Thus, we simply refer to RF chains asthey implicitly include, ADCs and DACs in the sequel.

UEs [11]. In a multicell setup, however, orthogonal pilotsequences may need to be re-used over the cells whichunfortunately results in the so-called pilot contamination.Pilot contamination imposes the fundamental performancebottleneck for the massive MIMO system5. Therefore, researchon potential techniques to eliminate or mitigate the pilotcontamination effect through efficient CSI estimation and pilotoptimization is important [11]. Different approaches have beenproposed to reduce pilot contamination such as Eigenvaluedecomposition (EVD) based channel estimation, successivepilot transmission, time shifted channel estimation and a linearcombination approach which exploits multipath components[23], [24]. In this regard, it is shown recently in [24] that amaximum of L (i.e., number of multipath components) cellscan reliably estimate the channels of their UEs and performbeamforming while efficiently mitigating the effect of pilotcontamination where L can be increased just by increasingthe transmission bandwidth. Nevertheless, all these approachescan only mitigate the effect of pilot contamination and, how tocompletely eliminate the effect of pilot contamination is stillan open research problem.

When the number of RF chains NRF at the BS is muchless than the number of BS antennas, the orthogonal channelestimation approach cannot be used even for single cell setup(i.e., without pilot contamination). Towards this end, thereare two potential methods. The first method is to employa combined time-frequency analog-digital channel estimationapproach. In such approach, the high dimensional section ofthe channel estimation process will be commonly employedfor all UEs and sub-carriers in time domain and analog form(i.e., depending on N ). And the low dimensional part (i.e.,depends on NRF ) can be designed in digital form for eachUE and sub-carrier. In general, this approach is effective foran environment where the channel parameters have severalmultipath components (e.g., microwave frequency bands). Thesecond method is to leverage the idea of direction of arrival(DOA) estimation approach from radar signal processing tommWave channel estimation. The mmWave frequency bandsare effective in a LOS environment and detecting the DOA ofthe transmitted signal is sufficient in such an environment. Inthis regard, one can estimate the DOA at microwave frequen-cies and employ this DOA information for data transmission atmmWave bands. Specifically, this approach is quite interestingwhen the low-power node is capable of operating at bothmmWave and microwave frequency bands such as a WiGIGaccess point6.

Once the channel estimation process has been completed,the next step is to perform beamforming. Again, the con-ventional MIMO system is based on digital beamformingwhich is performed at the baseband level and it requiresNRF = N , which is very costly. To reduce the implementation

5In a traditional multicell multiuser MIMO system, although pilot con-tamination arises, its effect is negligible or can be alleviated by employingappropriate coordinated BS transmission and CSI acquisition approaches.

6Note that in areas where there are multiple reflected rays, the DOAinformation of mmWave and microwave frequency bands may be differentfrom each other. For such scenario, environmental (context) aware DOAestimation approach can be employed jointly with the CR technology [18].

cost of beamforming, a combined time-frequency analog-digital approach is one of the most promising ones. Thisbeamforming approach will have both high dimensional analogpart operating in the time domain, and low dimensional digitalpart designed for each sub-carrier [13]. It is well-known thatthe channel estimation and beamforming are interrelated; whenthe channel estimation is more inaccurate, the performanceof the beamformer can become worse. Therefore, the criticalchallenge is how to jointly and optimally perform channelestimation and beamforming taking into account the number ofRF chains and pilot contamination for the multiuser multi-cellmassive MIMO system operating over the frequency selectivechannel.

V. CASE STUDIES

In the following, we discuss some design and case studies,which illustrate how one can address some challenges of the5G MIMO HetNet presented in the previous section.

A. Low-Cost Hybrid Digital-Analog Beamforming for Mul-tiuser Massive MIMO

We demonstrate the desirable cost and performance tradeoffachieved by the hybrid beamforming for the multiuser massiveMIMO system with large-scale antennas at both the transmitterand receiver sides [13]. As detailed earlier, the key advantagesof hybrid beamforming is that it requires smaller number ofRF chains, ADCs and DACs compared to the number oftransmitter and receiver antennas, respectively. To investigatethe performance achieved by hybrid and digital beamformings,we adopt a geometric channel model with L scatterers, whereeach scatterer is assumed to contribute a single propagationpath between the BS and each UE. Furthermore, the BS andeach UE are equipped with uniform linear arrays (ULA) an-tenna with half wavelength spacing. The number of transmitterantennas, transmitter RF chains, receiver antennas and receiverRF chains are set equal to 128, 64, 32 and 16, respectively.The number of receivers is K = 4, the number of multiplexedsymbols for each receiver is Sk = 8 and L = 16. Fig. 2 showsthe sum rate of the digital and hybrid beamformings for thedownlink multiuser massive MIMO system. As can be seenfrom this figure, the hybrid beamforming approach of [13]achieves almost the same performance as that of the digitalbeamforming.

The result in this figure is obtained for the channelparameters discussed in this subsection. Thus, one couldwonder whether the hybrid design of [13] can achieve thesame performance as that of the digital one for arbitrarychannel parameters. In this respect, it is demonstrated viasimulation in [25] that the hybrid beamforming design of[13] can experience significant performance loss (comparedto digital beamforming) when the number of scatterers arelarge. This motivates [25] to develop a hybrid beamformingutilizing a fixed (digitally controllable) paired phase shiftersand switches, that achieves closer performance to that of thefully digital digital beamforming. This paper also determinesthe required number of RF chains (and phase shifters) such thatthe hybrid design achieves the same beamforming performance

−7.5 −5 −2.5 0 2.5 5 7.5 10 12.5 15 17.517.550

100

150

200

250

300

SNR (dB)

Sum

Rat

e (b

/s/h

z)

Digital beamformingHybrid (digital−analog) beamforming

Fig. 2. Comparison of digital and hybrid beamformings.

−8 −6 −4 −2 0 2 4 6 8 10 12 14 160

20

40

60

80

100

120

SNR (dB)

Tot

al s

um r

ate

(in b

/s/h

z)

Without mobile relayWith mobile relay, M=8With mobile relay, M=10With mobile relay, M=12With mobile relay, M=14

Fig. 3. Comparison of the sum rates achieved with and without mobile relay.In this figure, M denotes the number of antennas at the mobile relay.

as that of the digital one. Moreover, for the parameter setupsof this subsection, it is shown that the hybrid design requiresa maximum of KSk and Sk RF chains at the transmitter andreceiver, respectively, to maintain the same performance asthat of the digital design [25].

B. Mobile Relay with Large-Scale Antenna Array

We demonstrate the performance gain achieved by imple-menting large-scale antenna arrays at mobile relays whichcan be deployed on public transportation vehicles such astrains or buses as depicted in Fig. 1 to better support highlymobile UEs. In particular, we consider two downlink scenarioswhere a macro BS communicates with individual UEs directlyand through the mobile relay which are refereed as first andsecond scenarios, respectively. In the second scenario, thecommunication between UEs inside the vehicle and mobilerelay are performed at mmWave frequency bands where thereis almost no capacity limitation. The BS is located at thecenter of its cell whereas, each UE is located randomlyinside the vehicle of dimension 3.10m × 25.91m (Amtrakpassenger train) with uniform distribution. The propagationmodel between the BS and each UE contains two components.

BS

MUE

LUEb

a

mmWave

Microwave

MicrowavemmWave

Macro BS

1

Fig. 4. Suggested dual band, layered and prioritized cell association. MUE(LUE) denotes UE served by the macro BS (low power node).

One is the path loss component decaying with distance, andthe other one is the Rayleigh fading random component whichhas a zero mean and unit variance. All the other parametersettings are summarized as shown in Table I.

TABLE ISIMULATION PARAMETERS FOR FIG. 3.

Distance between macro BS and Vehicle 1kmNumber of antennas at each UE 2Macro BS transmitter power 5WRadius of the cell 1.6kmReference distance (d0) 1.6kmPath loss exponent 3.8Mean path loss at d0 134dBChannel bandwidth 5MHzCarrier frequency 1.8GHzReceiver noise figure 5dBReceiver vertical antenna gain 10.3dBiReceiver temperature 300K

We compare the total rates achieved in these two scenarios.For the first scenario, we consider that the macro BS employsmultiuser block diagonalization (BD) beamforming approach.In the second scenario, since the mmWave frequency bandtransmission is assumed to support very high rate, the achiev-able rate is determined by the radio link between the macroBS and mobile relay. For this scenario, the macro BS employssingular value decomposition (SVD) beamforming. Fig. 3shows the sum rate computed by the Shannons capacityformula of these two scenarios. As we can see from this figure,the rate achieved by the second scenario (i.e., with mobilerelay) is significantly higher than that of the first scenario (i.e.,without mobile relay). In addition, we also see that increasingthe number of mobile relay antennas improves the achievablerate which is indeed expected. These results confirm that alarge-scale antenna system (massive MIMO) can lead to thesignificant performance gain when utilized appropriately.

C. Dual-Band Small-Cells

We now discuss a potential design for a small-cell networkthat operates in both microwave frequencies and mmWave

frequencies [15]. In general, microwave frequencies havemore favorable propagation properties but are more limited inbandwidth compared to the mmWave frequencies. Thus, a dualband small-cell with prioritized and layered cell associationstrategy as shown in Fig. 4 can exploit the advantages ofthese different frequency bands. In this design, the coveragearea is divided into three regions where the UEs in the innerregion (i.e., radius a)/middle region (i.e., radius b) are servedby the small cell at mmWave/microwave frequency bands. Andthe UEs in the outer region (i.e., radius > b) are served bythe macrocell at microwave frequency bands. Here, the outerregion operates at microwave frequency bands since thesebands allow long-range communications to support mobility.The parameters a and b can be chosen or optimized adaptivelybased on different context information as discussed in theprevious section. The middle region (i.e., with radius b) isanalogous to the “expanded range” considered in the LTEstandard where its radius can vary from one design criteriato another [10]. This dual-band small-cell design isolates themmWave and microwave frequency UEs (i.e., the mmWaveUEs inside the radius a do not experience any interferencefrom the macro BS which is desirable). However, still howto choose the best radius for different regions again dependson specific design objectives, which present interesting openresearch problems.

VI. CONCLUSIONS

This article discusses the 5G wireless HetNet integratingmassive MIMO and mmWave technologies. We have alsopresented a potential HetNet architecture that employs bothmicrowave and mmWave frequencies. Various design andtechnical challenges for this network architecture have beendescribed. We have then presented three case studies address-ing some of the challenges and showing their benefits. Overall,this article lays out the potential architecture and points outthe underlying research challenges, which must be addressedto meet the technical requirements of the future 5G network.

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