Paradigm shifts with hetnets

IEEE Communications Magazine • March 2013136 0163-6804/13/$25.00 © 2013 IEEE

INTRODUCTION

The global cellular communication network isone of electrical engineering’s crowning achieve-ments, reliably connecting over half the planet’spopulation, virtually everywhere where peopleare. These networks — particularly in urbanareas — are in the midst of a paradigm shift asthe number of base stations (BSs) increasesrapidly each year, nearly all by virtue of smallBSs (pico and especially femto) being added tothe existing network. This unprecedented escala-tion is due to intense consumer demand forfaster data connectivity, the impossibility ofmeeting this demand by adding spectrum, andthe increasing technical and financial viability ofsmall BSs. By 2015, there will be perhaps 50 mil-lion BSs [1], and some even predict that in thenot too distant future, say 10–15 years out, thenumber of BSs may actually exceed the numberof cell phone subscribers [2], resulting in a cloud-like “data shower” where a mobile device mayconnect to multiple BSs, or at least frequentlyhave a BS to itself. How is this possible? Whatimplications does such a scenario have on cellu-lar network design, wireless communicationsresearch, and the mobile computing industry?

NOT YOUR PARENTS’ BASE STATIONBase stations are typically envisioned as bighigh-power towers or cell sites. And indeed,many are. Fundamentally, though, a BS must dothree things. First, it must be able to initiate andaccommodate spontaneous requests for commu-nication channels with mobile users in its cover-age area. Second, it must provide a reliablebackhaul connection into the core network. Thisconnection often is, but need not be, wired, but

if wireless (possibly to another wired-in BS), itmust not be in the same spectrum used for com-munication with the mobile users; otherwise,such a device should be considered a relay .Relays may be useful in some cases for coverageenhancement, but by reusing the same scarce“access” spectrum for backhaul, are inherentlyinferior to BSs. And third, BSs need to have asustainable power source. Usually, this is a tradi-tional wired power connection, but it could inprinciple be solar, scavenging, wind-powered,fossil fuel generated (e.g., “mobile access points,APs” in vehicles), or something else.

It may seem frivolous to define a ubiquitoustechnology that has existed for several decades.But it is important to recognize that traditionaltower-mounted BSs — what we call macrocells inthis article — are just a single type of BS, albeitthe backbone that has enabled cellular’s successto date. However, in many important markets,adding further macrocells is not viable due tocost and the lack of available sites; for example,many cities or neighborhood associations aresimply not very cooperative about opening upnew tower locations. The problem facing opera-tors is not coverage — which is now nearly uni-versal — but capacity. There are just too manymobile users demanding too much data.

This will only worsen due to the continuingadoption of tablets, laptops with cellular connec-tions, and smart phones along with their data-hungry applications. Adding BSs has been by farthe most important factor historically for increas-ing capacity. When BSs are added, each usercompetes with an ever smaller number of usersfor a BS’s bandwidth and backhaul connection:it may even have one or more BSs to itself. Thisis the only scalable way to meet the current“capacity crunch.” Note that WiFi access pointstypically meet the above three criteria and arethus also BSs by our definition. WiFi is rapidlyintegrating with the cellular network, and roam-ing between cellular and WiFi will becomeincreasingly transparent to end users. Smartphones and tablets have sophisticated user inter-faces and high-definition screens, expensiverechargeable batteries, substantial consumersoftware, and support multiple wireless stan-dards. In short, there is no inescapable reasonBSs needs to be more expensive than the phonesthey serve once they have lower transmit power(the power amplifier cost is considerably higher

ABSTRACT

Imagine a world with more base stations thancell phones: this is where cellular technology isheaded in 10–20 years. This mega-trend requiresmany fundamental differences in visualizing,modeling, analyzing, simulating, and designingcellular networks vs. the current textbookapproach. In this article, the most importantshifts are distilled down to seven key factors,with the implications described and new modelsand techniques proposed for some, while othersare ripe areas for future exploration.

TOPICS IN RADIO COMMUNICATIONS

Jeffrey G. Andrews, University of Texas at Austin

Seven Ways that HetNets Are aCellular Paradigm Shift

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IEEE Communications Magazine • March 2013 137

in BSs, typically). Indeed, as of late 2012, aniPhone costs about 10 times more than a typicalWiFi access point. Such trends will soon extendto femtocells and then picocells, in a dramaticreversal from a decade ago, when BSs cost about1000 times the mobile devices they served.

IMPACT ON RESEARCH AND DESIGNCellular networks will thus be increasingly organ-ic deployments of BSs of widely varying transmitpowers (and hence coverage areas), carrier fre-quencies, backhaul connection types, and com-munication protocols. A typical smart phone willbe capable of communicating via multiple bandsover protocols including GSM/EDGE, code-divi-ion multiple access (CDMA), Long Term Evolu-tion (LTE), WiFi, and perhaps other protocols,and will make a choice based on what its needsare (e.g., high-speed data or voice, or the amountof mobility) and a quick analysis of the availableconnections.

Needless to say, the implications of this trendon the theory and implementation of cellulartechnology are extensive. This article organizesthese changes into seven topics, which are subse-quently described in some detail, with a summa-ry given in Table 1. These are:

Metrics. Even the way we discuss and com-pare the performance of cellular systems needsretooling.

Network topology. Clearly, a heterogeneousnetwork (HetNet) will have a very differenttopology than a macrocell-only network. Sincethe distance to desired and interfering BSs is thefirst-order factor in determining performance, areasonable topological model is required.

Cell association. How should users be associ-ated with cells as the network load fluctuates?Because of massive differences between nominal

cell sizes, cell association should not be basedjust on signal strength or signal-to-interference-plus-noise ratio (SINR). Load is often moreimportant.

Uplink-downlink relationship. HetNets intro-duce a major asymmetry between the uplink anddownlink, which affects several of the otheritems in this list.

The backhaul bottleneck. Placing BSs all overthe place is great for providing the mobile sta-tions high-speed access, but does this not justpass the buck to the BSs, which must now some-how get this data to and from the wired corenetwork?

Mobility. How and when should users behanded off between BSs when moving through aHetNet? Supporting mobility makes all the itemsin this list much more challenging.

Spectrum and interference management.Nearly all of the above affect the nature ofinterference in a HetNet. Furthermore, tradi-tional methods of interference management likefrequency reuse or BS coordination do notdirectly translate to HetNets. The above list isnot exhaustive, nor are the items orthogonal toeach other; indeed, there is considerable over-lap. For example, managing mobility properlyoften boils down to appropriate cell associationchoices. Nevertheless, this list captures theessence of how thinking must shift if we as engi-neers are to make the most of this incredibleopportunity.

METRICSWe begin with the basic metrics used to rate theperformance of a given cellular network. Natu-rally, we must have meaningful metrics to mean-ingfully compare different designs and

Table 1. Summary of the seven changes.

Aspect Traditional Cellular HetNet

PerformanceMetric

Outage/coverage probability distribution (in terms of SINR)or spectral efficiency (bps/Hz)

Outage/coverage probability distribution (in terms ofrate) or area spectral efficiency (bps/Hz/m2)

Topology BSs spaced out, have distinct coverage areas. Hexagonalgrid is an ubiquitous model for BS locations.

Nested cells (pico/femto) inside macrocells. BSs areplaced opportunistically and their locations are bettermodeled as a random process.

CellAssociation

Usually connect to strongest BS, or perhaps two strongestduring soft handover

Connect to BS(s) able to provide the highest data rate,rather than signal strength. Use biasing for small BSs.

Downlink vs.Uplink

Downlink and uplink to a given BS have approximately thesame SINR. The best DL BS is usually the best in UL too.

Downlink and uplink can have very different SINRs;should not necessarily use the same BS in each direction.

Mobility Handoff to a stronger BS when entering its coverage area,involves signaling over wired core network

Handoffs and dropped calls may be too frequent if usesmall cells when highly mobile, overhead a major concern.

Backhaul BSs have heavy-duty wired backhaul, are connected intothe core network. BS to MS connection is the bottleneck.

BSs often will not have high speed wired connections.BS to core network (backhaul) link is often the bottle-neck in terms of performance and cost.

InterferenceManagement

Employ (fractional) frequency reuse and/or simply toleratevery poor cell edge rates. All BSs are available for connec-tion, i.e. “open access”

Manage closed access interference through resourceallocation; users may be “in” one cell while communicat-ing with a different BS; interference management harddue to irregular backhaul and sheer number of BSs


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IEEE Communications Magazine • March 2013138

technologies. Two ubiquitous metrics are theoutage/coverage probability and the spectral effi-ciency.

The outage (equivalently one minus cover-age) probability is typically expressed in terms ofthe SINR cumulative distribution function (i.e.,the probability that the SINR is below a particu-lar value). This maps to the probability that thebit error rate (BER) is above a correspondingthreshold, or the maximum instantaneous datarate (at a target BER) is below one. What doesoutage mean in a network with so many BSsoperating over so many bands? The user experi-ence depends on the application-level data rateachieved within a certain latency. What affectsthis, however, is not just SINR; the load can beconsiderably more important.

The reason a HetNet requires a differentviewpoint is that the law of large numbers(LLN) — which more or less applies in amacrocell-only network due to the large num-ber of users per cell — simply does not apply ina HetNet. When the LLN is applicable, theSINR distribution provides a strong correlationto the rate and/or quality of service (QoS) theusers will achieve. For example, the cell edgeusers have lower SINR and hence lower rate;interior users have higher SINR and higherrate. In a HetNet, though, small BSs will oftenbe very lightly loaded, while others (the macro-cells) will be very heavily loaded. Hence, thecongestion and load mostly determine theachieved rate. Most readers of this article willknow this to be true even anecdotally: theirdata rates and ability to use data-hungry appli-cations screech to a halt in many cities at peakhours. A better definition of “outage” is theprobability that no BS — or combination ofBSs — can provide an aggregate data rate oversome threshold. In a macrocell-only network,the metrics of SINR outage and rate outageresult in similar conclusions. In a small cell net-work, these two definitions of outage yield verydifferent outcomes.

Another ubiquitous metric in both

academia and industry is the spectral efficien-cy, usually given in bits per second per Hertzof spectrum. This has always been a trickymetric for cellular, since the spectral efficien-cies vary so radically over the cell. For exam-ple, in LTE-Advanced, peak downlink ratesshould allow a whopping 30 b/s/Hz, while thecell edge requirement in the uplink is just0.04 b/s/Hz: a nearly 1000 times difference.Following similar logic, the spectral efficiencydistribution can be used to describe the spec-tral efficiency statistics a BS observes, withthe average just being the expected value ofthis distribution. But this again neglects thekey consideration of congestion and load. Abetter metric is area spectral efficiency, whichnormalizes by the cell area. For example, inan interference-limited environment (i.e., vir-tually any urban area), one can place fourtimes more BSs with spacing R than with spac-ing 2R. The spectral efficiencies would be thesame, but the area spectral efficiency of thedenser setup is four times higher, so the typi-cal throughput a mobile user could achieve isalso four times higher, assuming (optimistical-ly) that the mobiles and load are evenly spreadin space.

As an example of a step in this direction,Third Generation Partnership Project (3GPP)Release 9 (see Technical Report 36.814, Sec.A.2.1.4 on System Performance Metrics) recom-mends using a user-perceived throughput cumu-lative distributed function (CDF), and focusesmore generally on end-user throughput ratherthan SINR, BER, or spectral efficiency. Howev-er, this important distinction seems to have beenslow to migrate to the overall discourse aboutcellular system performance, including industrymore broadly and also in academia.

Recommendation: Stop measuring perfor-mance with BER or SINR distribution, or withspectral efficiency. These metrics are no longervery relevant. Instead, use the rate distribution(user-perceived, i.e., accounting for load) or areaspectral efficiency.

Figure 1. Downlink (left) and uplink (right) Max-SINR association regions for a three-tier network withmacros (red), picos (green), and femtos (black). Note that the BSs are in the same locations in these twoplots, but the regions are very different.

Stop measuring

performance with

BER or SINR

distribution, or with

spectral efficiency.

These metrics are no

longer very relevant.

Instead, use the

rate distribution

(user-perceived,

i.e., accounting for

load) or area spectral

efficiency.


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TOPOLOGY

An obvious change in a HetNet is the placementof base stations and their corresponding “cover-age” or association regions. Whereas macrocellsare generally somewhat evenly spaced (althoughless so than widely believed), they have mostcommonly been modeled as lying on a lattice, inparticular a hexagonal grid. The associationregions are then simply the correspondinghexagons. Smaller base stations are not regularlyspaced, nor are their association regions homo-geneous. Rather, they are scattered or clusteredwithin the existing macrocell network and formtheir own embedded association regions, whichare generally smaller, especially for the down-link, because the transmit power is significantlyless. For example, typical macro, pico, and femtotransmit powers are on the order of Pt = 40 W(effectively even higher due to high antennagain), 2 W, and 100 mW. Thus, about an orderof magnitude separates the transmit power ofeach “tier” of base stations, and their nominaldownlink association areas vary by about thesame amount, as seen in Fig. 1. If a simple pathloss model is used for average received power(i.e., Pr = Ptd–a), it can easily be shown that thecell area increases as Pt

a/2, but it is in reality clos-er to linear given that mounting heights andantenna gains are larger for higher-power BSs;thus, they have effectively larger coverage areas.

SPATIAL MODELS FOR HETNET BASE STATIONSThe spatial modeling of the BS locations in aHetNet remains an open topic, since little infor-mation is yet available about picocell or femto-cell deployments. It does seem clear that thegrid-based models of the past are not scalable toan accurate model of a multitier HetNet,although one could construct a series of overlap-ping grids of differing densities. For example, in[3], macrocells are modeled with a hexagonalgrid, with exactly six picocell BSs per macrocell,which are each located precisely on the bound-aries between neighboring BSs (Fig. 2). Needlessto say, such a setup is highly idealized. In theabsence of prior information, the best statisticalmodel is a uniform distribution, which in thetwo-dimensional plane corresponds to a Poissonpoint process. Such a spatial distribution for BSlocations corresponds to complete randomness,whereas the grid provides no randomness. Thus,they are philosophically opposite, and any plau-sible HetNet BS deployment is bounded betweenthese two extremes.

Although the relative merits of the determin-istic and Poisson models are open to debate, oneimportant difference is that of tractability.Although the grid model is familiar, popular,and easy to conceptualize, it is not tractable.Distributions on rate, SINR, and other metricsare found through detailed system-level simula-tions that model nearby BSs as interferencesources, and typically ignore more distant BSs.As small cells are added to the mix and the num-ber of “nearby” interferers grows, the complexityof such simulations will also grow. Currently, thesimulation of interference in such networks andthe accurate determination of the SINR statisticsunder network dynamics is very time-intensive.

On the other hand, the Poisson model is sur-prisingly tractable, and a large class of powerfulresults and analytical tools are available from thefield of stochastic geometry [4]. Because of thepresumed independence between BS locations,SINR distributions can be obtained in closedform even for networks with an arbitrary numberof BS types: where each class of BSs is distin-guished with different transmit powers, densities(i.e., average number of BSs per unit area), andSINR targets [5]. A high-level introduction andsummary of the model and results in [5] aregiven in [6] and thus not repeated here.

INSIGHTS FROM THE POISSON MODELThe Poisson BS model is uncomfortable formany because of the independence assumption.Clearly, BSs are not actually placed indepen-dently. Thus, alternative random distributionsthat introduce an appropriate level of correla-tion should be developed as more data becomesavailable. What is important to recognize is thatthere is little evidence that the Poisson model isany less accurate than the ubiquitous grid model,which is also very idealized. In fact, the twomodels give similar SINR distributions in termsof shape, and differ mostly in terms of absoluteSINR: with the grid model being optimistic (abest-case deployment for coverage) and the PPPmodel being pessimistic (hard to do worse thana purely random scattering of your BSs) [7],assuming the mobiles too are uniformly scat-tered.

The PPP model does, however, allow certainsuspected “truths” to be confirmed mathemati-cally. Many of these truths are not widely known.For example, it can be proven that once a net-work is interference-limited, adding BSs of anytype does not change the downlink SINR statis-tics, assuming any user can connect to the

Figure 2. The macro-pico model used by 3GPP in [3]. Other 3GPP modelsinclude randomly located picocells inside the macrocell area.

Wrap-aroundcontour

Pico cells


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strongest one (i.e., open access), the network isfully loaded, and also interference-limited. Thisfurther means that: • The interference from new small cells is

perfectly balanced by a decrease in the typi-cal access distance to a nearby BS, and thecorresponding increase in desired signalstrength.

• Adding BSs of any type strictly increasescapacity, since congestion is decreasedwhile SINR is constant.

• Downlink power control is not terriblyimportant, since different power levels canbe modeled as different types of BSs, whichfrom the above result do not affect theSINR distribution.These insights have been corroborated by

industry field trials and observations fromextremely detailed simulations. For example,Qualcomm [8] observes that adding picocells toa macrocell network does not change the SINRdistribution and can only increase capacity (butno reason or evidence is given). Drawing onNokia Siemens’ field trials based on LTE Het-Nets, [6] shows a high degree of agreement withthe theoretical results of [5]. 3GPP also utilizesrandom small cell distributions for simulationmodels; for example, TR 36.814 randomly dropsa fixed number (1, 2, 4, or 10) small cells insidea larger macrocell area, and then assumes mobileuser clustering around such cells.

Recommendation: Phase out the grid modelfor BS locations, which is neither tractable norrealistic for HetNets. Instead adopt a randomspatial model for the BS locations. Poisson canbe used for analysis, but more accurate distribu-tions should be used for system-level simulations(e.g. by the standards bodies). Such distributionsneed to be developed and validated byresearchers using actual BS deployment data.

UPLINK-DOWNLINK RELATIONSHIPThe results in the last section focused on thedownlink. When thinking about a HetNet, it isnatural to think about macrocells having largecoverage areas, and pico and femtocells havingmuch smaller coverage areas. Indeed, because ofthe large transmit power disparities, a femtocellcoverage area might be limited to a single house,or even part of a single floor of a single building.This intuition does not extend to the uplink of thenetwork. This can be observed in the right side ofFig. 1: for the same BS locations, the downlinkand uplink max-power (and hence max-SINR)coverage areas are very different.

It is easy to understand why. In the uplink(UL), all transmitters are roughly equal: they aremobile devices running off batteries. They allhave about the same transmit power and thusrange: to a transmitting mobile device, a BS isjust a receiver, and thus a femtocell and macro-cell appear the same. Of course, macrocells maybe tower-mounted and have higher-gain anten-nas (including receive antenna gain), while fem-tocells may be indoors and have low-gainantennas. But these affects are about the sameas in the downlink (DL), while the transmitpower disparities between BS types in the DLare 20 dB+, which is not the case in the UL.

Thus, these nominal coverage regions do notdepend on UL power control (even though ittoo forms a major difference between DL andUL): at any transmit power, the closest BS willallow the maximum received power on average.

Therefore, in a HetNet, the UL-DL relation-ship is quite different than in a macro-only net-work. For starters, a mobile may wish to beassociated with different BSs in the UL and DL,and not doing so may be highly suboptimal. Forexample, users toward the macrocell edges wouldlikely prefer to connect to a nearby picocell orfemtocell, even if the corresponding signal is tooweak for DL reception. But if this is relaxed sothat the two directions become independent,there are interesting implications for the corenetwork (traffic in each direction being routedto and from different BSs), and for the mobile’sQoS. For example, a mobile may be on the celledge and have poor SINR in one direction butnot in the other. One saving grace that mitigatesthis UL-DL imbalance is biasing, discussed inthe next section, which effectively increasessmall-cell DL regions, making them more likethe UL regions.

Additionally, the interference models andresulting SINRs would be quite different in thetwo links. Assuming orthogonal access (e.g.,orthogonal frequency-division multiple access,OFDMA), users that are orthogonal to oneanother on the downlink (sharing the samemacrocell BS) may interfere with each other onthe UL if they are transmitting to different BSs.Two-way channel models (e.g., classical interfer-ence channel models) that assume symmetry inthe two directions are increasingly questionable.Many results in information theory based onUL-DL dualities are further eroded (interfer-ence from other cells is already ignored to obtainsuch results), because the channel gains andSINR in the two directions may be almost uncor-related, especially if they are via different BSs.

Recommendation: The DL and UL need tobe considered as two different networks, and willrequire different models for interference, cellassociation, and throughput. Although this asym-metry exists in macrocell only networks as well,the difference is potentially much larger in aHetNet.

CELL ASSOCIATIONIn traditional cellular networks — and indeed, inthe prior two sections for multitier HetNets —we typically assume that mobile users connect tothe strongest BS, which offers the best SINR.Assuming all BSs are fully loaded — transmittingand receiving packets in all their time-frequencyblocks at all times — such a strategy can easilybe shown to optimize sum throughput, whereeach BS just communicates with its max-SINRuser in each such block. Two obvious problemsimmediately emerge. First, maximizing sum-rateis not a very practical objective, as cell-edge userswill be ignored. However, even if all users aregiven an equal share of the resources (e.g.,round-robin or proportional fair scheduling), itstill maximizes sum rate for each of them to con-nect to the strongest BS. However, in reality, thesecond and more important issue is that most

The downlink and

uplink need to be

considered as two

different networks,

and will require dif-

ferent models for

interference, cell

association, and

throughput.

Although this asym-

metry existed in

macrocell only net-

works as well, the

difference is poten-

tially much larger in

a HetNet.


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BSs — especially those with small coverage areasand hence fewer active users — will not be fullyloaded, and instead may be very lightly loaded.

In a macrocell-only network, the max-powercoverage regions for each BS are designed tohave roughly the same amount of traffic. That is,over time, more BSs are deployed in areas thatgenerate more traffic, while sparsely populatedareas get fewer. The law of large numbers isapplicable in such a network, so although thetraffic load certainly varies over space and time,it oscillates around a slowly varying value that isthe sum of a large number of nearly uncorrelat-ed traffic requests. Many small cells will havejust a few users, so the LLN is not applicable,and the aggregate loads vary wildly from no loadat all to heavy load in times of sustained filedownloading or video streaming. An intelligentcell association policy should assign users to BSsthat offer them the best user-perceived rate.This rate will depend on both the SINR and theload; for example, the rate offered by BS i wouldbe approximately

where Ki is the number of users currently beingserved by that BS. This can be visualized throughFig. 3, where a modified criterion for cell associ-ation is shown on the right, which is clearlymuch more balanced.

A challenge is that optimizing the rate for allusers is very complex, and results in an exhaus-tive search over all possible pairings, which is NPhard. This is because the rates of all users arenominally coupled: by shifting a user onto a dif-ferent BS, the rates for all users on those twoBSs go up or down because of the change inload. This is not computable in finite time evenfor modest-sized cellular networks. However,through various physical relaxations, our recentwork [9] was able to find a numerically com-putable upper bound on the rate distributionand a distributed algorithm that nearly achievesit. The rate gains are very large compared tomax-SINR association, on the order of two timesfor “average” users and three times for cell edgeusers, as seen in Fig. 4. This is really quite alarge gain in the context of today’s systems,

which are operating fairly near the Shannonlimit in most cases, and it shows that significantperformance improvements are possible fromload balancing.

Interestingly, a very simple suboptimalapproach known as biasing, which is the pre-ferred industry and 3GPP method of pushingload onto small cells, does nearly as well as a fullnetwork-wide optimization. In biasing, small BSsare preferred by some amount known as the biasvalue, to account for the fact that they are lightlyloaded. Then the usual max-SINR association isused with the biased SINRs. Referring to Fig. 4again, we see that (optimized) SINR bias valuesget very close to the network-wide optimumsolution, which is fairly remarkable. The optimalSINR bias values vary depending on the networkparameters, particularly the transmit power (andthus coverage area) of each type of BS [9], andthe density of the mobile users. Importantly,however, the optimum bias values do not strong-ly depend on the number or density of such BSs,so the values may not need to be adjusted as thenetwork topology changes or new BSs are added.

Recommendation: Initial work shows thatload balancing through cell range extension isvery valuable in a HetNet, and that biasing isnearly optimum compared to a centralized opti-mization, which is perhaps surprising. Morework is needed to better understand how tooptimize (and adapt) biasing for HetNets, partic-ularly under realistic loading models and diversetypes of traffic (e.g., balancing QoS for data,voice over IP [VoIP], and video streaming).

THE BACKHAUL BOTTLENECKCellular engineers typically assume that the maindesign challenge is the air interface: the connec-tion between the mobile and the BS. The BS isassumed to have significant amounts of process-ing power and a high-speed backhaul connectionthat easily handles the data flowing to and fromthe BS. WiFi users — that is, anyone readingthis article — have long been aware that this isnot true, and that the wired connection behindthe WiFi access point is far more important thanthe peak advertised speed of the WiFi devices.Cellular is rapidly heading in this direction.

Start with femtocells: these are very similar to

= +RB

Klog (1 SINR )i

ii2

Figure 3. Base station associations in a three-tier HetNet using the traditional max SINR criteria (left) or a revised max sum log rate cri-teria (right). The right figure results in more balanced load and higher achieved data rates, especially for users who were previously onthe macrocell edges.

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WiFi in terms of their range and deployment,and often use the same backhaul connections asWiFi APs. Clearly, a 10 MHz LTE femtocell isoften going to be limited in speed by a typicalcable modem or digital subscriber line (DSL)connection, particularly in the UL. There isprobably little to be done about this from a cel-lular engineering point of view, other than toconservatively allocate resources, and for the BSto be aware of the backhaul speed and latency(which will affect its ability to coordinateresource allocation or handoffs with other BSs).

Picocells are more interesting since they areoperator deployed and aim to provide a com-mercial grade experience, and thus require ahigh-quality backhaul connection. Many desir-able picocell locations (lampposts, building cor-ners, etc.) do not have existing wiredconnections, and installing and maintaining suchconnections may be financially prohibitive. Adesirable alternative is a wireless solution thatuses underutilized spectrum that is not useful forthe air interface. One example is to use millime-ter wave frequencies (e.g., 30–100 GHz) that arevery difficult to deploy in a mobile network, butfor pico to macro backhaul communication overa fairly static channel, may soon be viable [10].Another example is to use unlicensed or white -space frequencies, although this may be lessrobust in some markets unless significant newinterference mitigation technologies aredeployed. Significant industry activity is occur-ring in this space, including solutions from wellfunded startups like Fastback, Siklu, and BLiNQ,as well as the established telecom giants.

Recommendation: From a research point ofview, a shift is required that recognizes theimportance of this bottleneck. For example,massive multiple-input multiple-output (MIMO) solutions [11] may be very challenging in mobilechannels, but can be adapted (particularly athigh frequencies) to the backhaul channel.Another clever example is the idea of cachingpopular content such as video clips or othercommon downloads at the small cells [12], which

appears to have been implemented commerciallyby Ubiquisys’s “smart cell” picocell. Such con-tent can be updated periodically at a time of lowbackhaul load. The gain of such innovations onnetwork performance can be large, but are invis-ible unless the backhaul bottleneck is included inthe formulation.

MOBILITYThe reliable support of mobile connections isone of the cellular network’s main achievements,and why cellular providers are able to commandlarge subscription fees vs. other forms of tele-phony and data access. Ensuring reliable voiceand data support in view of the challenges raisedthus far is a difficult task compared to macro-cell-only networks, in which mobility support wasalready far from trivial. First is the issue of whenhandoffs should occur. Referring again to Fig. 1,envision a mobile user traveling through this net-work. When should it hand off? If the user ismoving slowly — a pedestrian, for example —handing off to a picocell is almost surely justi-fied, and possibly to an open access femtocell ifthe offline signaling support is sufficient and it isable to offer a high data rate (possibly allowingthe user to clear any download or upload queueif it would otherwise be on a congested macro-cell). On the other hand, a user traveling atvehicular speeds would likely prefer to avoidhandoff into and then out of a small cell that itwould only be in for a few seconds or less, giventhe overhead (utilizing overloaded backhaullinks) and delays such handoffs incur. In 3GPP,the minimum time of stay (MTS) defines athreshold of time spent on a given BS, belowwhich a handoff should not be done. A nominalvalue for MTS is 1 s [3]. Recall also that DL andUL assignments may be distinct or else highlyasymmetric, and loads on each BS are also veryimportant in the context of BS association,adding to the complexity of handoff decisions.

Traveling through a small cell without hand-off results in what we term the “unwelcomeguest” problem. The mobile user that does nothand off causes very strong temporary interfer-ence to the small cell (on the UL) while receiv-ing strong interference on the DL. This can bemitigated in OFDMA through an appropriateresource allocation that is mobility-aware, forexample, enhanced intercell interference coordi-nation (eICIC) with subframe blanking, as inLTE [3]. Overall, the rate of failure for handoffsin HetNets is bound to be higher than in amacro-only network, and this has been con-firmed by 3GPP studies, which see failure ratesas high as 60 percent in a macro-pico HetNet,with the average failure rate about doubling vs. amacro-only network. While the problem increas-es overall with mobility, the ping-pong effect(leaving and then re-entering the same cell) isactually worse with low mobility [3]. One seem-ing paradox, however, is that at a fixed networkload, adding picocells may improve mobility per-formance by reducing the load per BS, thusreducing the overall interference and likelihoodof handover failures.

Recommendation: Network-level analysis,simulation, and design of HetNets must account

Figure 4. Rate complementary distribution function on a log scale. A very largerate gain is observed over a max-SINR association for users in the bottom 50percent (edge users).

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for the suboptimal associations introduced bymobility. Improved mobility modeling, handoveroptimization, and mobility-aware interferencemanagement are all challenging topics for futurework.

SPECTRUM ANDINTERFERENCE MANAGEMENT

Managing interference is a major concern in anycellular network. As we saw previously, and con-trary to popular belief, interference is not inher-ently a worse problem in a HetNet. True, thereare many more interfering sources, and they willusually be closer, but the desired BS is also clos-er, and these effects roughly cancel each otherout in a co-channel deployment. What is alsotrue is that HetNets introduce some new chal-lenges as far as managing and mitigating inter-ference, since a traditional macrocell methodlike static frequency reuse (or in GSM, time slotaveraging) is hard to implement and not terriblyeffective. Biased cell associations, which we haveseen are crucial for macrocell offload and formaximizing the overall network utility, place afurther burden on interference managementsince users are now communicating with BSsother than their “home” BS, and thus subject toincreased DL interference, as well as causingstrong UL interference.

Fortunately, OFDMA-based cellular systemsprovide significantly more flexibility and robust-ness than CDMA systems, which are single carri-er and also sensitive to the near-far problem. Inan OFDMA system, edge users can be assigneddifferent time-frequency blocks than either edgeusers in adjacent cells or interior users in anycell. This robust approach, which can be donesemi-statically, is known as fractional frequencyreuse (FFR) and is shown visually in Fig. 5 for arealistic macrocell deployment. Such anapproach can be extended to HetNets, althoughit is more complex, particularly as the number oftiers increases [13]. It can also be implementedin the time domain, which is known as eICIC in3GPP. Another OFDMA-based approach couldbe to just have small cells use certain specificsubbands that are distinct from the macrocells,and for the macrocells to not use those bandsfor highly mobile users (to avoid the unwelcomeguest problem). A further possibility is carrieraggregation, whereby mobile users may use sev-eral bands simultaneously, possibly over differ-ent tiers, with the band allocation varyinggeographically and in time depending on thetraffic pattern. In general, using different bandsfor different tiers (e.g., 800 MHz for macrocelland 2.5 GHz for picocells) is suboptimal from athroughput standpoint.

There is considerable optimism that coordi-nated multipoint (CoMP), alternatively known asnetwork MIMO or BS cooperation, is an impor-tant aspect for HetNet interference suppression.The main idea behind CoMP is to have neigh-boring BSs cooperatively encode (DL) anddecode (UL) messages for multiple simultaneoususers, thus getting a multiplexing gain vs. treat-ing them as interference. Some theoreticaland/or numerical results predict a several-fold

increase in network throughput [14], or in aby-now notorious example, up to 1000 times ifthe scheme is renamed distributed-input dis-tributed-output (DIDO) [15]. Such optimismdoes not appear to be very well supported bythe evidence, however. The Qualcomm HetNetsystem design team, for example, found thatafter accounting for necessary overheads, the“gains” from CoMP in a HetNet were lessthan 0 percent [16], while recent theoreticalresults show that if channel uncertainty andbackground interference are brought into theanalysis, even the theoretical best case gainsare much smaller than widely envisioned [17].In short, coordinating small cell transmissionsis much less important than just maximizingthe sheer volume of small cell deployments,and providing efficient load balancing viabiased cell associations.

Recommendation: Efficient interference man-agement in a HetNet relies on reasonable mod-els for all the previous topics discussed untilnow, and can be seen as encompassing keyaspects from the previous six sections (e.g. biasedcell associations, random network topologies,mobility, and UL-DL asymmetry). Ignoringthese crucial issues will usually result in mislead-ing conclusions regarding interference.

CONCLUSIONSThe rapid trend toward extreme heterogeneity inmobile communication networks requires manylongstanding models and the associated conven-tional wisdom to be reevaluated. This trend isirreversible, and will have a profound impact onboth the theory and practice of communication

Figure 5. Power-based fractional frequency reuse in a realistic macrocell net-work.

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systems. In this article, we have attempted tohighlight some of the changes required, bothfrom a design and implementation perspective(many of which are well underway in industryalready), and in basic research, where thisparadigm shift seems to be less appreciated.

ACKNOWLEDGMENTSThe author appreciates feedback and input fromArunabha Ghosh, Angel Lozano, Kevin Negus,Harpreet Dhillon, Xingqin Lin, and QiaoyangYe. He also appreciated several very helpfulcomments from the reviewers.

REFERENCES[1] J. G. Andrews et al., “Femtocells: Past, Present, and

Future,” IEEE JSAC, Apr. 2012.[2] D. P. Malladi, “Heterogeneous Networks in 3G and 4G,”

IEEE Commun. Theory Wksp., http://www.ieeectw.org/program.html, May 2012.

[3] 3GPP TR 36.839 v11.0.0, “Mobility Enhancements inHeterogeneous Networks (Release 11),” Sept. 2012.

[4] M. Haenggi, Stochastic Geometry for Wireless Net-works, Cambridge Univ. Press, 2012.

[5] H. S. Dhillon et al., “Modeling and Analysis of k-TierDownlink Heterogeneous Cellular Networks,” IEEE JSAC,Apr. 2012.

[6] A. Ghosh et al., “Heterogeneous Cellular Networks:From Theory to Practice,” IEEE Commun. Mag., June2012.

[7] J. G. Andrews, F. Baccelli, and R. K. Ganti, “A TractableApproach to Coverage and Rate in Cellular Networks,”IEEE Trans. Commun., vol. 59, no. 11, Nov. 2011, pp.3122–34.

[8] A. Damnjanovic et al., “A Survey on 3GPP Heteroge-neous Networks,” IEEE Wireless Commun., vol. 18, no.3, June 2011, pp. 10–21.

[9] Q. Ye et al., “User Association for Load Balancing inHeterogeneous Cellular Networks,” IEEE Trans. Wire-less., http://arxiv.org/abs/1205.2833, to appear.

[10] Z. Pi and F. Khan, “An Introduction to Millimeter-WaveMobile Broadband Systems,” IEEE Commun. Mag., vol.49, no. 6, June 2011, pp. 101–07.

[11] T. Marzetta, “Noncooperative Cellular Wireless withUnlimited Numbers of Base Station Antennas,” IEEETrans. Wireless Commun., vol. 9, no. 11, Nov. 2010,pp. 3590–600.

[12] N. Golrezaei et al., “Femtocaching: Wireless VideoContent Delivery Through Distributed Caching Helpers,”http://arxiv.org/abs/1109.4179, 2011.

[13] T. D. Novlan et al., “Analytical Evaluation of FractionalFrequency Reuse for Heterogeneous Cellular Networks,”IEEE Trans. Commun., vol. 60, no. 7, July 2012, pp.2029–39.

[14] G. J. Foschini, K. Karakayali, and R. A. Valenzuela,“Enormous Spectral Efficiency of Isolated MultipleAntenna Links Emerges in A Coordinated Cellular Net-work,” IEE Proc. Commun., vol. 153, no. 4, Aug. 2006,pp. 548–55.

[15] S. Perlman and A. Forenza, “Distributed-Input-Dis-tributed-Output (DIDO) Wireless Technology: A NewApproach to Multiuser Wireless,” Rearden White Paper,July 2011.

[16] A. Barbieri et al., “Coordinate Downlink Multi-PointCommunications in Heterogeneous 4G Cellular Net-works,” Info.Theory and Applications Wksp. (ITA), Feb.2012.

[17] A. Lozano, R. W. Heath, and J. G. Andrews, “Funda-mental Limits of Cooperation,” submitted to IEEE Trans.Info. Theory, vol. http://arxiv.org/abs/1204.0011, 2012.

BIOGRAPHYJEFFREY ANDREWS [S’98, M’02, SM’06, F’13] ([email protected]) received his B.S. in engineering with High Dis-tinction from Harvey Mudd College in 1995, and his M.S.and Ph.D. in electrical engineering from Stanford Universityin 1999 and 2002, respectively. He is a professor in theDepartment of Electrical and Computer Engineering at theUniversity of Texas at Austin (UT Austin), where he was thedirector of the Wireless Networking and CommunicationsGroup from 2008 to 2012. He developed CDMA systems atQualcomm from 1995 to 1997, and has consulted for enti-ties including the WiMAX Forum, Intel, Microsoft, Apple,Clearwire, Palm, Sprint, ADC, and NASA. He is co-author oftwo books, Fundamentals of WiMAX (Prentice-Hall, 2007)and Fundamentals of LTE (Prentice-Hall, 2010), and holdsthe Earl and Margaret Brasfield Endowed Fellowship inEngineering at UT Austin, where he received the ECEDepartment’s first annual High Gain award for excellencein research. He is a Distinguished Lecturer for the IEEEVehicular Technology Society, served as an Associate Editorfor IEEE Transactions on Wireless Communications from2004to 2008, was Chair of the 2010 IEEE CommunicationTheory Workshop, was Technical Program Co-Chair of ICC2012 (Commuications Theory Symposium), and holds thesame position for IEEE GLOBECOM 2014. He received theNational Science Foundation CAREER award in 2007 andhas been co-author of five best paper award recipients,two at IEEE GLOBECOM (2006 and 2009), Asilomar (2008),the 2010 IEEE Communications Society Best Tutorial PaperAward, and the 2011 Communications Society HeinrichHertz Prize. He is an elected member of the Board of Gov-ernors of the IEEE Information Theory Society.

The rapid trend

towards extreme

heterogeneity in

mobile communica-

tion networks

requires many long-

standing models and

the associated con-

ventional wisdom to

be reevaluated. This

trend is irreversible,

and will have pro-

found impact on

both the theory and

practice of commu-

nication systems.


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______________

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http://www.qmags.com/clickthrough.asp?url=http://arxiv.org/abs/1109.4179&id=18075&adid=P144E4


http://www.qmags.com/clickthrough.asp?url=http://www.ieeectw.org/program.html&id=18075&adid=P144E2


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