feasibility of secondary networks: analysis methodology

1

Feasibility of Secondary Networks: AnalysisMethodology and Quantitative Study of Cellular

and Wi-Fi-like TVWS DeploymentsAndreas Achtzehn, Student Member, IEEE, Ljiljana Simic, Member, IEEE, Marina Petrova, Member, IEEE,

and Petri Mahonen, Senior Member, IEEE

Abstract—Recent rulings by US and UK regulators allowing access to unused portions of TV spectrum have elicited high hopesfor the practical value of these TV whitespaces (TVWS) for secondary exploitation. However, this optimism has been largely fueledby rather simple early studies, which do not consider all the system aspects in sufficient detail; the few early experimental worksreported are proprietary and consider only a small number of nodes, whereas the existing more sophisticated theoretical studiesfocus on singular aspects of secondary spectrum access in isolation from the overall system interactions. In this paper we studyquantitatively the deployment of secondary networks in TVWS by considering two archetypal candidate systems: LTE-like cellular andWi-Fi-like networks. We develop a systematic framework for the performance evaluation of secondary networks, which we then use toobtain realistic estimates of the performance of our example systems. The secondary network performance assessment methodologydemonstrated in this paper can be directly applied for other regions and systems. Our work explicitly takes into account limitationsarising from aggregate interference, user density, and specific secondary transceiver characteristics. We argue that a systematicanalysis, jointly considering all of these aspects, is key for obtaining realistic and robust results on the estimated value of whitespacespectrum. Our detailed system-level approach reveals a much more conservative picture of the realistic benefit of TVWS deploymentsthan what has been commonly assumed thus far. We find that cellular TVWS networks have limited capabilities, but that a macro-cellular-only network may be a viable option for traffic offloading. Our results also show that Wi-Fi-like secondary deployments inTVWS, although increasing coverage range, lead to increased congestion, which limits the system capacity.

Index Terms—Secondary Networks, Dynamic Spectrum Access, TVWS, Cellular, Wi-Fi, Interference

F

1 INTRODUCTION

With the ever-increasing growth of wireless services,future wireless networks depend on the availability ofspectrum [1]. Since most of the frequencies below 5 GHzare assigned, researchers and regulators have been work-ing on new paradigms that allow for dynamic and moreflexible spectrum use. For surveys of research on dy-namic spectrum access and cognitive radio techniques,we refer to [2], [3] and references therein.

The recent decisions of the FCC in the USA and Ofcomin the UK to open the TV bands for secondary usehave been embraced with great interest because of thepotential of these bands to provide long-range wirelessbroadband access. The enthusiasm behind the idea ofusing TV whitespaces (TVWS) for wireless broadbandservices has lead to active research on secondary reuse.However, the majority of existing research is based ontheoretical work, which although interesting and some-times seminal [4], has the serious shortcoming of beingbased on strong simplifying assumptions and focusingon singular aspects of the problem. For example, theexisting works on the raw availability of whitespacesin different parts of the world (e.g. [4], [5]) are typi-

The authors are with the Institute for Networked Systems at RWTH AachenUniversity, Germany. We acknowledge partial financial support from DFGreceived through UMIC research centre.

cally based on rather abstract models of the secondarynetwork under idealized conditions. In other words,these studies focus on the absence of primary users inestimating whitespace availability, without defining withreasonable accuracy the exploiting secondary system,which severely limits the possibility to calculate the realutility of whitespace spectrum for secondary networks.

Moreover, there is still a lack of work considering thefeasibility of the deployment of realistic secondary net-works over large geographical areas. Early experimentalstudies into the potential of Wi-Fi-like deployments inthe UHF band [6], [7], [8], [9] have strongly contributedto the commonly held high expectations on the capacityof TVWS secondary networks. Yet these studies merelydemonstrate operation of modified Wi-Fi transceiversover TV channels, while assuming that a large amount ofTV spectrum is available; the optimistic results reportedare thus inconclusive at best and in need of careful in-terpretation. Existing R&D work in TVWS deploymentsis similarly nascent, consisting of relatively small-scaleongoing trials attempting to demonstrate the use of TVbands for mobile broadband access [10], from which itis difficult to derive general conclusions.

Therefore, despite the intensity of recent studies, thereremain doubts and a lack of proper understanding of thecommercial and technical viability of different secondaryTVWS systems. Resolving some of these uncertainties

2

would be highly desirable for improving regulatoryexploitation of dynamic spectrum access beyond thecurrent initial steps by FCC and Ofcom, and also forpotentially increasing active interest by commercial ven-dors. In this paper we address some of these issues byconducting a systematic and detailed performance studyon well-defined secondary systems in realistic setups.

The first major contribution of this paper is to developand describe a systematic framework for the robustperformance analysis and comparison of the technicalfeasibility of different whitespace candidate technolo-gies. Importantly, our secondary network performanceassessment methodology can be directly applied fordifferent regions and systems. Unlike earlier studies, ourframework allows us to take into account the effectsof medium access, transceiver design, and aggregateinterference. We also carefully evaluate the amount ofspectrum available for secondary reuse by using TVWSchannel availability estimates derived from real dataof TV transmitter configurations and terrain conditions.Our systems approach thus enables us to obtain a robustand realistic secondary network capacity estimate.

We demonstrate the importance and power of sucha methodology through a case study, where we ana-lyze recently proposed TVWS systems. The case studyitself is the second major contribution of this paper, asour analysis results are significantly more realistic anddetailed than in the prior literature. Specifically, in thispaper we study the feasibility of operating two differenttypes of secondary networks in TVWS in Germany:a nationwide macro-cellular network and a Wi-Fi-likenetwork in indoor urban, outdoor urban and outdoorrural deployments. The archetypal systems for wirelessdata access are cellular or infrastructure-less Wi-Fi net-works. Traditionally, a cellular-type approach implies theexistence of a planned network and higher infrastructurecosts, which may or may not be attractive for someservice providers. A Wi-Fi-type approach is normallythe opposite, exhibiting a more ad-hoc growth of thenetwork, and has been seen as a golden technology formany low cost mobile computing applications. Hencethese two main approaches warrant initial considerationand comparison in the context of TVWS deployments formobile broadband access.

Operators have shown an interest in extending cel-lular technologies such as Long Term Evolution (LTE)[11] to the TV bands [12]. Studies on coexistence is-sues [13] have shown that LTE can be employed inTVWS, but the current cellular network structure maybe suboptimal [14]. LTE is already used in adjacentUHF frequencies – the digital dividend bands – andtrials are currently underway to test practical deploy-ments [15]. IEEE standardization committees have alsoworked with the IEEE 802.22 approach, and recentlywith IEEE 802.11af [16], which is a draft extension ofthe current Wi-Fi standard for TVWS operations. One ofthe stated aims of such systems is to provide extendedcoverage, especially for rural outdoor connectivity. Prior

research in this area has largely focused on modifyingstandard 2.4 GHz Wi-Fi hardware to experimentallydemonstrate long-haul links over UHF [6], [7], [8], [9].

Thus existing research on cellular and Wi-Fi-type net-works in TVWS has mostly focused on early proof-of-concept studies without evaluating systems interactionsin any detail. By contrast, this work takes a detailedsystems view, employing real TVWS channel availabilityestimates and user densities while taking into accountthe effects of medium access, transceiver design, andaggregate interference in order to derive more realisticperformance results. Our analysis is the first compre-hensive and detailed comparative study between suchsystems we are aware of.

We emphasize that for a reliable performance analy-sis of secondary networks, it is imperative to considerrealistic channel availability and propagation charac-teristics; namely, abstracting out geographical and na-tional TV deployment details is not an option. We havetherefore chosen Germany as our test case study area.Choosing a particular geographic area thus enables usto demonstrate our performance assessment frameworkand conduct a comparison of our two candidate sys-tems. Our analysis indicates that our results would notbe significantly different were we to instead use otherEuropean countries or the US as the study area. Theamount of available TVWS reuse opportunities and thedistribution of population density in Germany are infact quite typical of many TVWS early-adopter countries,as confirmed by earlier studies such as [5], and we areconfident that we have not chosen an area which wouldprovide biased results, in particular not against TVWSexploitation feasibility. We thus argue that our analysisresults are applicable in general.

The remainder of this paper is organized as follows.Section 2 details our proposed spectrum assessmentmethodology. In Section 3 we apply our methodology toobtain an estimate of raw TVWS availability. In Sections4 and 5 we present case studies for cellular and Wi-Fi-like TVWS deployments, thereby demonstrating theimportance of completing a full spectrum assessmentin order to the evaluate the real utility of whitespacespectrum. Section 6 concludes the paper.

2 SPECTRUM ASSESSMENT METHODOLOGY

Early studies, such as [4], [5], mostly considered thenumber of channels available to a single whitespacedevice. Subsequent works have more critically assessedthe techno-economical utility of these available spectrumresources for networks of multiple secondary devicesvia scenario-driven analyses [14], [17]. In this paperwe present a spectrum assessment methodology thatprovides a unified framework for such studies on theperformance of secondary systems with dynamic spec-trum access. We note that our focus is not on establishinga detailed mathematical model, but rather on addressingthe more pertinent and challenging task of formulating a

3

PrimarySystem

Definition

AccessPolicy

Application

AvailabilityAssessment

SecondarySystem

Definition

TechnicalOpportunities

Analysis

PrimarySpectrum

Usage

SecondaryUsage

Constraints

Raw SpectrumAvailability

TechnicalPerformance

Metrics

EconomicalPerformance

Metrics

SecondarySystem

Realizations

BusinessOpportunities

Analysis

1 2 3 5 64

TransmitterInformation

Regulatory AccessPolicies

Statistical Moments Description

Sec. System Configuration

Tech. ParameterDescriptions

KPIDescriptions

Fig. 1. Spectrum assessment workflow.

systematic and robust, yet flexible, unified spectrum as-sessment methodology. Our framework thereby enablesdetailed and realistic analyses and meaningful compar-ative studies of secondary access utility for differentregulatory policies, radio technologies, and metrics. Theeffectiveness of the framework has also been practicallydemonstrated in a prototype software tool [18].

As shown in Fig. 1, the workflow of a spectrumassessment can be divided into six steps that build uponeach other. Step 1, primary system definition, comprises thespecification of spectrum use by the primary system. InTVWS scenarios studied in this paper, this step focuseson describing the transmission characteristics of theprimary broadcasting system. For this purpose, trans-mitter databases are required that include the locationsof broadcasting towers, their transmit powers, antennacharacteristics, and channel usage. Such information isavailable in the emerging whitespace databases in theUS, as well as in the various databases of national regu-lators worldwide; we have used the latter in our analysis.Propagation modelling is then applied through standardpathloss models or by integrating measurement cam-paign data to calculate received signal strength. Auxil-iary information relevant for this step can include terrainelevation information to allow for a more precise signalstrength estimation. The resulting primary spectrum us-age database stores data on the signal strength and otherrelevant primary reception characteristics. Aside fromwhitespace and regulatory uses, there have been recentproposals to build similar spectrum databases – so-calledradio environmental maps (REM) – for optimization andradio resource management [19].

Regulatory access policies for secondary spectrum areapplied in Step 2. The FCC proposal for the US [20]and the ECC-SE43 draft for Europe [21] are the mostreferenced policies formulated for TVWS secondary ac-cess. Common to both is a description of the access per-missions relative to geometric and/or primary receivedtransmit power constraints for a secondary whitespacedevice at a given location. They define a protection con-tour around the primary transmitter within which pri-mary receivers ought to be able to operate undisturbedby secondary interferers. The application of the accesspolicy results in a description of per-channel acceptableoperational parameters, such as the allowed secondarytransmit power outside the protection contours.

The resulting per-channel operational parameters are

used in Step 3 to derive statistics on the raw spectrumavailability. These statistics can be mapped to a coarsesecondary capacity estimate by considering a singlewhitespace device operating at any given location andassuming a simple secondary link model. As discussedpreviously, such an assessment of raw whitespace availabil-ity is, by itself, inherently of limited use for evaluatingthe utility of these whitespaces for networks of multiplesecondary devices: it stops short of considering effectsof secondary-secondary interference on the achievablesecondary network performance, and the effects of ag-gregate secondary interference on maintaining primaryprotection (which become significant for a sufficientlydense secondary network).

A proper evaluation of the utility of secondary ac-cess necessitates an assessment under the constraints ofprototypic secondary system configurations, defined inStep 4, the secondary system definition. The extent of pa-rameterization of these secondary system prototypes de-pends on the envisioned scenario and required analysisaccuracy, but in general the parameters encompass dif-ferent whitespace device distribution models, secondarytransceiver parameters such as the maximum supportedtransmit power and resource combining options, andtransmitter-receiver geometries. Simplified assumptionsmay allow the use of probabilistic secondary systemmodels, but we stress that a very high level of abstractionis not consistent with the complexity of real-world sys-tems and would thus not permit us to estimate realisticsecondary network capacity.

Instead, multiple realizations of secondary system de-ployments must be studied for their statistical properties.This analysis of the technical opportunities for secondarysystems is carried out in Step 5 and yields results onthe achievable link and system level throughput, overallenergy consumption, coverage of large-scale systemsand other functional parameters.

Finally, an analysis of techno-economical key performanceindicators is conducted in Step 6, either from the results ofa best-case secondary scenario (e.g. for planned cellulardeployments) or from averages and higher moments foropportunistic deployments (e.g. Wi-Fi-like). The techno-economical analysis is then carried out by defining asystem of utility functions that map technical metricsinto user experience, business metrics, etc.

The spectrum assessment methodology presented inthis section allows larger-scale studies on the feasibility

4

of secondary networks by providing a flexible stepwiseframework, with precisely defined inputs and outputs,that significantly extends previous single-secondary-device or single-system analyses. Our approach allowsfor redefining parameters in each step without com-plicated intra-step dependencies. The methodology isgeneric enough to be applicable to different secondarysystem scenarios and provides a self-contained analysisbasis, enabling meaningful comparative studies.

In the remainder of this paper, we demonstrate ourmethodology via a case study of TVWS secondary net-works, applying Steps 1-3 in Section 3 to obtain apreliminary estimate of raw spectrum availability andSteps 4-5 in Sections 4 and 5 to evaluate the performanceof cellular and Wi-Fi-like deployments respectively. Wenote that Step 6 of the spectrum assessment framework isoutside the scope of the present paper due to space con-straints, however the appropriate business metrics couldbe readily applied to our technical feasibility results.

3 RAW WHITESPACE AVAILABILITY IN EXAM-PLE AREA OF GERMANYIn this section we apply Steps 1-3 of the assessmentmethodology to derive the raw whitespace capacitystatistics for our study area of Germany. We stressthat the analysis in this section only provides the rawavailability of spectrum for secondary operation, as pre-sented in detail by earlier studies on the number ofchannels available for a single whitespace device [4],[5]. Importantly, our work goes beyond this preliminarystage of whitespace quantification by evaluating the realexploitation opportunities for well-defined secondarynetworks via application of Steps 4-5 of our methodologyin Sections 4 and 5. We thus limit the scope of thissection to presenting typical secondary access policiesand establishing a baseline for our case studies.

3.1 Primary System DefinitionWe consider the case of TVWS operations in Germany,where a secondary system would need to coexist witha large primary DVB-T network. The German regula-tor publicly provides data on TV transmitter locations,antenna heights and patterns, and the used transmitpowers for Germany and surrounding countries for eachof the 40 channels assigned to TV broadcasting, whichwill be used for Step 1 of the assessment. The referencegeometry we chose at the primary receiver and all otherrelevant parameters are listed in Table 1. Due to thelarge terrain variations in the area, we have employedthe Longley Rice propagation model [22] for coverageestimation. The secondary interference calculation usesthe ITU P.1546-3 [23] model.

3.2 Regulatory Policy for Secondary Spectrum Ac-cessCurrent proposals for regulation of secondary access tounderutilized spectrum resources, as required for Step 2

TABLE 1Primary System Model

Primary System DVB-T [24]

Channel bandwidth 8MHzConstellation 64−QAMCode rate 2/3SNRmin 16.7 dB [24]Residual noise floor −105.167 dBmInterference Budget Imax −105.167 dBmACIR −40 dB @ N ± 1TX Propagation Model ITU P.1546-3, F(50, 90)† [23]RX Antenna Height 10m [25]RX Antenna Gain 0 dBPrimary Coverage Model Longley-Rice ITM, F(50, 90)†

Interference Model ITU-R P.1546-3, F(50, 50)†σ = 5.5 dB, qmin = 0.9[23, Section 12, Table 2]

Modeling radius dmax 150 km

†F(X,Y ): In at least X% of locations, Y% of time.

of the assessment, can be roughly divided into two majorclasses. As in the FCC ruling in the US [20], secondaryaccess may be granted with a fixed transmit power if thesecondary transmitter is outside a certain “safety zone”surrounding the original coverage area of a primarytransmitter. By contrast, the working group SE43 in ECChas filed a proposal for an access policy that would allowsecondary access with a dynamic power budget dependingon the actual distance of the secondary transmitter to theclosest primary receiver [21]. The FCC model has thusfar been predominantly considered in various regulatorydomains, hence we adopt it throughout this paper.

The design space of FCC-type regulation comprisesthe no-talk radius, the minimum enforced distance to thecoverage area of the primary system, and the allowedsecondary transmit power, assuming a single secondarydevice. The reference geometry (i.e. the positioning ofreceiver antennas) and the assumed properties of thenecessary signal-to-noise ratio (SNR) determine the ex-tent of coverage for the primary network. The regulatordecides on the permissible level of signal degradation atthe edge of the coverage area, by defining a maximumallowed mean interference power from the secondarydevice. A feasible combination of no-talk radius andmaximum secondary transmit power can then be se-lected through application of the secondary interferencepropagation model.

3.3 Whitespace Availability For a Single SecondaryDeviceFigs. 2 and 3 show the raw whitespace availability inGermany for a single secondary device, as derived inStep 3 of the spectrum assessment. These results illus-trate that the estimated whitespace availability stronglydepends on the enforced no-talk radius dmin. Increasingthe no-talk radius allows a higher transmit power for thesecondary device, due to the spatial separation from theinterfered TV receivers. A disadvantage is the decreased

5

20 30 40 50 60 70 80 90 1000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Radius of no−talk zone dmin

[km]

P(X

> C

)

C = 0

C = 1

C = 2

C = 3

C = 4

Fig. 2. Fraction of Germany for which given number of channelsis available for secondary use vs. the radius of the no-talk zonedmin. As dmin increases, a greater portion of the country isleft with no available whitespaces (blue curve) and the sizeof the regions with large channel availability reduces (greencurve). Consequently, the curves representing the locations withintermediate channel availabilities (gray and red) exhibit a peakat a given dmin.

spectrum availability, because at a given location, oper-ation on fewer channels will be allowed. Fig. 2 showsthat, whereas very small no-talk radii permit havingmore than 5 channels available in more than 40% ofthe locations, these figures quickly decay with largerno-talk radii, and the fraction of the country with noTVWS resources at all increases exponentially. At a no-talk radius of approximately 20 km, 2 − 5 channels areavailable for over half of the country’s area. Moreover,Fig. 3(a) reveals that there is a large regional variation inthe channel availability over the country. It is thus crucialto consider regional availability of whitespace spectrumin evaluating the feasibility of secondary deployments,rather than relying simply on average availability statis-tics as in many earlier works. In our case studies pre-sented in the sequel, we consider the specific distri-bution of available channels in our chosen study area.Importantly, the majority of existing studies stop at thispreliminary stage of quantifying the number of avail-able channels and simply calculating the correspondingsingle-link Shannon capacity as shown in Fig. 3(b). Bycontrast, we apply Steps 4-5 of our framework in orderto estimate the achievable performance of a network ofsecondary devices operating in the available whitespacespectrum, thereby taking scalability issues into accountfor a full systems evaluation of the utility and feasibilityof secondary spectrum access.

4 CASE STUDY: CELLULAR SECONDARYNETWORK IN TVWSIn this section we study the feasibility of deployinga cellular secondary network in TVWS. We focus onthe downlink performance of the network. Due to theelevated position of its transmitters, a cellular secondarynetwork is particularly likely to cause severe aggregate

Hamburg

Berlin

Munich

Aachen

(a) (b)

1700136010206803400

Hamburg

Berlin

Munich

Aachen

0 5 10 15 20 25

Fig. 3. Distribution of TVWS availability in Germany for a no-talkzone radius of 20 km: (a) number of available channels and (b)corresponding single-link Shannon capacity (Mbps).

interference for the primary network, i.e. the receivedsignal power at the protection contour of the primarysystem from all secondary transmitters must the explic-itly accounted for. In this section we firstly develop asimple policy for limiting the power budget of individ-ual transmitters with a predetermined regular geometry,before analyzing the achievable system performance foran LTE-like secondary network.

4.1 Secondary System Model4.1.1 Base Station Distribution and Aggregate Interfer-ence ConstraintsWe begin our analysis of the cellular use with a study ofthe expected aggregate interference of a cellular networkcomposed of M base stations (BSs) to a test point T lo-cated at the edge of the coverage area of a TV transmitter,which we assume to be circular for the sake of simplicity.BSs are placed following a regular hexagonal structureat distances di, 1 ≤ i ≤ M relative to T . The aggregateinterference in T is calculated by summing over the in-dividual power contributions P i

rx := P itx×γ(di)×Xi. For

developing a tangible policy model, we postulate thatall base stations operate at the same power, i.e. P i,j

tx ←Ptx. The distance-dependent function γ(·) describes thepathloss model. We employ the commonly used ITU-R P.1546-3 model [23]. Dynamic fading effects due toshadowing from buildings and trees are represented bya lognormally distributed random variable (RV) Xi withµ = 0 and a fixed standard deviation σ. The value of σrepresents the expected variability, which we assume tobe uncorrelated between secondary transmitters.

4.1.2 Bounds on the Aggregate InterferenceTo prohibit outage in the primary network, the regu-latory policy defines the maximum permissible inter-ference level Imax. The lower bound on the aggregateinterference from the secondary network is given by

I ≥ maxi

(P irx) + (M − 1)min

i(P i

rx). (1)

6

Exploiting the inequality in (1), which states that the sumof interference will necessarily exceed the maximum ofthe single power contributions plus (M -1) times theirminimum, a lower bound on the probability of accept-able interference is derived as [26]

P (I < Imax) ≥∫ ln(Imax)

−∞G(z)dz (2)

G(z) =

∫ ln((Imax−ez)/(M−1)))

−∞

∂2F (y, z)

∂y∂zdy, (3)

F (y, x) =

∏i

[1− FP i,jrx

(y)], if y ≤ x∏i

[1− FP i,jrx

(y)]− . . .∏i

[FP i,jrx

(y)− FP irx(x)] otherwise,

(4)

FP irx(x) = Q

(x− ln(P i

txγ(di))

0.3256σ

), (5)

where Q(·) is the CDF of the standard normal distribu-tion. In order to find a convergent solution, we define amodelling radius dmax. Base stations for which di > dmax

are considered not to contribute to the interference [27].For the case of Germany studied later we found that,due to the channel reuse of TV networks, a modellingradius of 150 km is appropriate [14].

In addition to denying access to a TV channel withinthe coverage contour of its transmitter, whitespace poli-cies specify a relative no-talk zone radius (NTR) dmin.The NTR is defined as the minimum separation distancefrom a secondary transmitter to the edge of a coveragecontour, see Fig. 4. Due to the irregular contour shapewhich a terrain-aware propagation model such as theLongley Rice ITM yields, interference may originatefrom any secondary transmitter outside no-talk zone1, whereas for terrain-agnostic models the coveragecontour will be circular with radius dPC, and thus anabsence of transmitters from the larger zone 2 can besafely assumed in the terrain-agnostic models.

Fig. 5 shows the aggregate interference of a cellularnetwork in which all base stations are transmitting withPtx = 43dBm (all other scenario parameters are listedin Table 1 and reflect the standard situation in centralEurope). We observe that a dense cellular network, withcell radii of r = 1km, generates high interference at thetest point under both constraint models. We note thatdeployment of such a high-power, high-density networkis improbable under realistic interference constraints. Forsparser networks with higher cell radii, enforcing a no-talk zone will eventually provide sufficient protection.The relationship observed in Fig. 5 can be transformedinto a mapping of enforced NTR to the permissible trans-mit power for the BSs of the cellular network. At thispower, the secondary network will exhaust the budgetof maximum allowable interference budget for at leastone protection contour point. Fig. 6 shows the resulting

Fig. 4. Protection model for aggregate interference calcula-tion in cellular scenario. Only base stations outside protectioncontour and no-talk zone 1 are allowed to operate on thechannel. Aggregate interference calculations for a test pointinclude either the interference from base stations outside no-talkzone 2 (circle-marked) or additionally base stations sufficientlyfar from the individual test point (circle- and star-marked).

Fig. 5. Aggregate interference of cellular networks with cell radiir and a common base station transmit power of P = 43dBm.Unmarked lines denote the upper bound to the aggregateinterference for qmin = 0.9 and with interference only fromoutside no-talk zone 1. Marked lines show the case excludinginterference from base stations outside no-talk zone 2.

maximum individual BS transmit power as functionof dmin. We observe that for macro-cellular secondarynetworks, high transmit powers become possible.

4.1.3 Operating ChannelsFuture cellular base stations will be capable of simul-taneously operating within multiple frequency bands.For discontinuous bands, this carrier aggregation (CA)generally uses multiple radio transceiver chains and isan important extension for 4G cellular networks [28].However, this may be undesirable for cellular operatorsdue to the associated increased power consumption. Wewill therefore compare three different transceiver types.For simplicity we assume that the network will onlyuse carriers of 5MHz each, as this is the largest single-carrier bandwidth in the LTE specifications supportedin a single DVB-T channel of 8MHz. Firstly, we considera flexible transceiver that operates on all possible TV

7

Fig. 6. Permissible maximum transmit power for regularlyshaped cellular networks if interference margin of 3 dB (Imax =−105.167 dBm) is required at TV receiver. Placement of basestations following (1), (2) means closest base stations are sep-arated by di,j = max(

√3R, dmin).

channels at the same time and thus exploits the TVWSmost fully; this is likely to be most energy-inefficienttransceiver. Secondly, we consider a non-continuous car-rier aggregating transceiver (NCA) that supports oper-ating on up to four channels which do not need tobe adjacent. Thirdly, we consider a continuous carrieraggregating transceiver (CCA) that supports up to four5MHz carriers, provided they are in adjacent channels(see Table 2).

4.1.4 Throughput ModelStep 4 of the spectrum assessment methodology com-prises the technical specification of the secondary systemin terms of the operation parameters of the transceiver,hardware, and secondary transmitter locations. Whereasearly studies approximated the expected network perfor-mance through upper theoretical bounds on the channelcapacity, e.g. [4], we conduct a more realistic analysis byalso considering radio hardware restrictions.

We employ a transceiver model with features simi-lar to those used in 3GPP Long Term Evolution (LTE)cellular networks [11]. For the sake of clarity, we ab-stract here from the complex radio resource managementand power control mechanisms found in current LTEnetworks1. Instead, we focus on the performance ofthe lower PHY layer to map the signal-to-noise ratioat a location to the achievable raw throughput. Wehave selected LTE as a reference point, as it is alreadyused for cellular networks operating in the adjacentdigital dividend part of the UHF bands. User devices(UEs) and base stations (eNodeBs) of LTE are capable ofoperating in multiple frequency bands of 1.4 − 20MHzcarrier bandwidth simultaneously. Limiting exploitationto the center of the channel will provide guard bands

1. Secondary-secondary interference effects have also not been takeninto account to keep our analysis tractable; although this yields best-case performance results, we note that the overlap between the operat-ing channels of adjacent cells is typically only partial since the numberof TV channels that may be used per BS is limited.

TABLE 2Cellular Network System Parameters

System configuration Long Term Evolution, R8

Carrier bandwidth 5MHzMax. number carriers 4 (for CCA/NCA)Carrier aggregation (Non-)ContinuousAntenna configuration 1× 2 SIMOMaximum spectral efficiency 4 bps/Hz@18 dBMax. output power 38 dBmTX antenna gain 18 dBiCut-off SINR −10 dBUF RF noise figure 6 dBIn-System Interference 4 dBTX Propagation Model ITU P.1546, F(95, 95)†Noise floor −105.167 dBm+ IPr

TX Antenna Height 15mRX Antenna Height 1.5m

†F(X,Y ): In at least X% of locations, Y% of time.

sufficient for high-power operations [13]. Furthermore,a 1× 2 SIMO antenna configuration with homogeneousradiation pattern in the transmitter side is assumed.Other parameters listed in Table 2 reflect the typical linkbudget planning for LTE networks [11].

Although our cellular network model does not exactlycapture the real system-level performance, the level ofabstraction is appropriate to enable arguments on cellu-lar TVWS operations in general, and helps to illustratethe rationale behind the proposed spectrum assessmentmethodology. Our model gives an upper bound on thethe real achievable performance of a user link, whichwill also depend on other parameters such as the loadin the cell (sector) and interference from other cells. Thesecondary interference budget would likewise need totake into account UE-originating interference. However,incorporating such details is outside the scope of thispaper, as it would not change the qualitative results ofthe analysis carried out in Step 5 of the framework.

4.2 Performance Analysis4.2.1 Link-Level PerformanceFor the subsequent analysis we focus on two scenarioswith cell radii of r = 5km and r = 10 km. Fig. 7(a) showsthe effect of the selected regulatory policy we selectedin Step 2, in terms of the specified no-talk zone radius,on the achievable average link throughput. The powerconstraint applied to cope with the aggregate secondarynetwork interference is given in Fig. 6. The expectedlink throughput is calculated for users randomly locatedwithin the area served by the network (locations withoutcoverage are excluded). We observe that the achievablethroughput initially increases with the no-talk radius,because the benefits of operating at higher transmitpowers outweigh the disadvantages of lower channelavailability. Then at a scenario-specific turning point,the expected link throughput starts to drop rapidly,due to an increasing number of channels becoming un-available. Non-continuous carrier aggregation improves

8

(a)

(b)

Fig. 7. Expected link throughput and outage probability of acellular network user in TVWS. The expected performance fordifferent no-talk radii of a TVWS user randomly located withinthe network coverage area is shown in (a). The outage probabil-ity in (b) shows the fraction of the country that is not servedby the TVWS network, compared to a “dedicated-spectrum”network using all existing TV channels with the same maximumtransmit power in each channel.

the link throughput significantly, with approximately6 Mbps more throughput compared to the continuous-only case. While the achievable link throughput appearspromising, the downside is limited coverage.

Fig. 7(b) compares the outage probability, i.e. theprobability that no radio link can be established usingTVWS resources, for the two scenarios and a user ran-domly located within Germany. We also plot the outageprobability for a “dedicated-spectrum” network, whichis identical to the TVWS network except that we assumeall TV channels are always available. The dedicated-spectrum network thereby serves as a reference, sincehere outage can only occur at the cell edge due to trans-mit power limitations and not due to unavailability ofspectrum resources. We observe that for a no-talk radiusof up to 20 km and 37 km for the TVWS and dedicated-spectrum network respectively, the major outage is aresult of the insufficient transmit powers. Both networksare only sporadically providing throughput due to thelarge cell sizes in relation to the low transmit power.As the power budget is increased further, the dedicated-spectrum network achieves universal coverage. By con-

trast in the TVWS network the outage probability in-creases approximately linearly, but due to decreasingspectrum availability.

4.2.2 Cellular Network Throughput DistributionThe utility of a cellular network in TVWS would bestrongly limited by discontiguous coverage and highvariability in achievable link throughput. Fig. 8 showsthe distribution of network capacity, for the example ofa cellular network with r = 5km for the link throughputoptimizing case of a no-talk radius of 25 km. Base sta-tions operate at a maximum power of 38 dBm and theoverall outage probability of approximately 11% is closeto its minimum.

Comparing the results in Fig. 8 with the overly op-timistic Shannon capacity estimate in Fig. 3(b) typicalof earlier studies clearly demonstrates the importanceof our spectrum assessment methodology for obtaininga realistic estimate of secondary network performance.Areas of high population density, e.g. in the westerncentral part of the country, and larger cities such asBerlin, Hamburg, and Munich, are not covered by theTVWS network. The high demand for TV channels fromthe broadcasting network hinders secondary operationshere. Furthermore, a large discrepancy can be observedbetween southern and northern Germany, whereby theless densely populated areas in the north-eastern re-gions can support high link throughputs. The transceivermodel does not affect the outage distribution, becausethe outage in this configuration is shaped by the localchannel availability, not the global transmit power con-straint. The NCA-type transceiver model is neverthelesscapable of providing more homogeneous throughputlevels throughout the country.

Fig. 9 shows the complementary cumulative distribu-tion functions (CCDFs) of the single-user link through-put. As noted before, the smaller cell radius significantlyimproves the achievable performance, offers a loweroutage probability and a higher average throughput.While this statement is not universally applicable due tothe constraint of the maximum allowed transmit power,we found it to be valid for reasonably sparse networks inour environment. As expected, the NCA-type transceiveris always superior to the equivalent CCA-type config-uration for a given cell radius. For the two examplecases we found that the NCA transceiver configurationwith r = 10 km yields higher throughputs in the top20% of the locations than the CCA transceiver withr = 5km, but is inferior for the rest. We thus concludethat the transceiver design must not be neglected whenthe desired cell radius is determined.

5 CASE STUDY: WI-FI-LIKE SECONDARYNETWORK IN TVWSIn this section, we study the feasibility of deploying aWi-Fi-like secondary network in TVWS. We base ouranalysis on real TVWS channel availability estimates

9

Hamburg

Berlin

Munich

Aachen

(a) All TVWS channels

Hamburg

Berlin

Munich

Aachen

(b) CCA

Hamburg

Berlin

Munich

Aachen

(c) NCA

Fig. 8. Spatial distribution of expected link throughput (in Mbps) for a cellular network with r = 5km operating in TVWS with differenttransceiver models. The link-throughput maximizing configuration with no-talk radius of 25 km is shown, whereby a maximumtransmit power of Pmax = 38dBm is allowed.

Fig. 9. Complementary cumulative distribution function of thelink throughput for a cellular user located randomly in Germany.For cell radii of r = 5km and r = 10 km the average linkthroughput maximizing case is shown.

from an example region of Germany, as derived in Sec-tion 2 by application of Steps 1-3 of our spectrum assess-ment methodology. To obtain a realistic estimate of theachievable range and downlink rate of such a secondarysystem, we take into account the effects of inter-AP in-terference and congestion arising from neighbouring co-channel APs. Moreover, we consider how respecting theconstraint of a limited aggregate secondary interferencebudget to the primary system restricts the permissibleoperating parameters of the secondary network. Namely,in this section we apply Steps 4-5 of our methodologyin order to evaluate the practical utility of TVWS for Wi-Fi-like networks for various deployment scenarios.

5.1 Secondary System Model5.1.1 AP Distribution and Propagation ModelsWe model the location of secondary transmitters (APs)using a homogeneous Poisson point process with densityλ. We consider three potential deployment scenarios forthe secondary Wi-Fi-like network: outdoor urban, indoor

TABLE 3System model parameters for different Wi-Fi-like secondary

network deployment scenarios

OutdoorUrban

IndoorUrban

OutdoorRural

Network study area (2 x 2) km2 (0.5 x 0.5) km2 (5 x 5) km2

AP density, λ 12.5/km2 125/km2 0.25/km2

Propagation model k = 3 k = 4, 18 dBwall loss

k = 2.5

TVWS availability:no. 8-MHz channels 17 17 14no. 16-MHz channels 7 7 4no. 24-MHz channels 4 4 0

urban, and outdoor rural. We take the city of Aachen andthe area around Wipperfurth in the Southern Rhinelandregion of Germany as examples of an urban and ru-ral study area, respectively, and assume AP density isproportional to the population [17]. The parameterscharacterising each scenario are summarised in Table 3.

We assume the log-distance path loss model2, whichgives the average path gain γ at a transmission distanced as

γ(d) = γrefd−k, (6)

where k is the path loss exponent and γref is the free-space path gain at the reference distance of 1 m [29].Table 3 lists the typical values of k characterizing eachscenario. To keep our analysis tractable, we set the TVWScarrier frequency fc = 630 MHz (centre of the 470-790 MHz band, DVB-T channels 21-60), regardless of theactual TV channel the secondary AP occupies.

2. Without loss of generality, we have opted for an adequatelyaccurate, though simpler, propagation model for the short-link Wi-Fi-like secondary network compared to that used for the country-wide primary TV and secondary cellular networks, but chosen ourpropagation model parameters such that we obtain a conservativeestimate of the average performance in TVWS.

10

5.1.2 Operating ChannelsWe consider a realistic example of TVWS availabilityfrom the analysis in Section 3; in the studied areas ofAachen and Wipperfurth channels {21, 22, 30-32, 39-48,56, 60} and {21, 23, 24, 32, 34, 38, 39, 41, 42, 44, 45, 51,54} are predicted to be available3, respectively. In linewith earlier proposals, we allow channel aggregationof up to three 8 MHz TV channels. However, we takeinto account the impact of the fragmentation of the TVspectrum by assuming that only adjacent channels maybe aggregated for reasons of practical implementationfeasibility. Table 3 lists the corresponding number ofavailable non-overlapping TVWS channels of differentwidth. For reference, IEEE 802.11g Wi-Fi operates on 3non-overlapping 20 MHz channels in the 2.4 GHz ISMband, whereas IEEE 802.11a in the 5 GHz unlicensedband has 15 and 19 non-overlapping 20 MHz channelsfor outdoor and indoor operation, respectively [30].

We assume that TVWS access for opportunistic low-power secondary network deployments is coordinatedby a geolocation TVWS database, in line with a num-ber of existing proposals, e.g. [20]. As for a cellularsecondary network, we assume a fixed maximum co-channel interference budget for each TV channel ofImax = -105.157 dBm. We assume that, for each TVchannel, the TVWS database assigns a given proportionof this interference budget to the area where a secondarynetwork is located. The fraction of Imax assigned to thesecondary network area can be calculated in variousways and is ultimately a matter of policy. As an exampleof one reasonable approach, we assume that the calcula-tion is based on population density, and that the fractionof Imax(i) assigned for TV channel i is proportional tothe fraction of the total population outside the primaryprotection contour of channel i which corresponds to thearea covered by the secondary network.

We differentiate between an available and accessibleTVWS channel4 as follows. A TVWS channel is said to beavailable if the secondary network is outside the primaryprotection contour of the corresponding TV channel(s),as advised by the TVWS database. An available TVWSchannel is then deemed to be accessible if the secondarynetwork, with a given transmitter density and transmis-sion power, is permitted to operate on the correspondingTV channel(s) when respecting the maximum allowedaggregate secondary interference budget assigned to thenetwork. We assume that the list of available channelsis obtained from a TVWS database by a local centralcoordinator entity for the Wi-Fi-like secondary network,along with the maximum allowed aggregate secondaryinterference budget IAmax(i) assigned to the network(comprising the set A of secondary users) for each avail-

3. We note that although the specific list of available channels wouldbe different for another geographic region, we select these study areaswithout loss of generality since the qualitative trends and conclusionsemerging from our analysis would not be significantly different forother locations.

4. TVWS channel can be aggregation of several 8 MHz TV channels.

TABLE 4Definition of ρ(β) auto-rate function,

consistent with IEEE 802.11g specifications

Index,n

Spectralefficiency(bps/Hz)

βn(dB)

Raw bit rate, ρn (Mbps)

20 MHzchannel(802.11g)

8 MHzchannel(TVWS)

16 MHzchannel(TVWS)

24 MHzchannel(TVWS)

1 0.3 4 6 2.4 4.8 7.22 0.45 5 9 3.6 7.2 10.83 0.6 7 12 4.8 9.6 14.44 0.9 9 18 7.2 14.4 21.65 1.2 12 24 9.6 19.2 28.86 1.8 16 36 14.4 28.8 43.27 2.4 20 48 19.2 38.4 57.68 2.7 21 54 21.6 43.2 64.8

able TV channel i. The local central TVWS coordinatorof the Wi-Fi-like network then decides on the numberof accessible TVWS channels, and the permissible trans-mission power, and advertises this to its secondary APs.We will investigate these parameters via simulation inSection 5.2.2. In practice, the local TVWS coordinator’sdecision regarding the accessible TVWS channels can beimplemented as a precomputed look-up table, based onthe number of registered APs in the secondary network,the distance to the primary protection contour of eachTV channel, and the typical propagation characteristicsof the deployment scenario (e.g. outdoor vs. indoor).

5.1.3 Throughput ModelWe assume the Wi-Fi-like APs employ the CSMA/CAMAC protocol. Let ρ(β) be the piecewise constant auto-rate function which maps the raw bit rate ρ provided byan AP to a user for a given minimum received SINR,as given in Table 4 based on the spectral efficiency andminimum receiver sensitivity specifications in the IEEE802.11g standard [30]. Let SINRu,x be the SINR at useru associated with AP x. The coverage area (cell) of APx is defined as the set of user locations Ux for whichSINRu∈Ux,x > β1. Correspondingly, the maximum (cell-edge) coverage range of AP x, rmax, is the mean distancefrom AP x to users located on the contour for whichSINRu,x = β1.

To keep our analysis tractable, we consider downlinksaturated traffic and a single user per AP5. In orderto estimate the downlink throughput when interferencefrom other co-channel APs in the network is taken intoaccount, we employ the model proposed in [17], [31].LetA be the set of all secondary AP transmitters in thenetwork, and Aj be the subset of all APs in the networkoperating on channel j, where j = {1, 2, 3, ..., J} is theindex of each of J non-overlapping operating channels.Let Aj

x be the set of all co-channel APs which are in the

5. Our analysis thus represents a best-case estimate of the per-usercapacity of a secondary Wi-Fi-like network in TVWS.

11

contention domain of AP x. A pair of co-channel APsare considered to be in a common contention domainif they receive each other’s signal with power greaterthan the carrier-sensing detection threshold. As per theCSMA/CA MAC protocol, an AP will refrain from trans-mitting if another AP in its contention domain is alreadytransmitting. It follows that the set of APs which causeinterference during the transmission of AP x are thoseAPs which operate on the same channel as AP x but areoutside its contention domain. Thus the SINR at user uassociated with AP x is given by

SINRu,x =Ptxγu,x

N0 + IPU +∑

y∈Aj\AjxPtxγu,y

, (7)

where Ptx is the transmission power of the AP (assumedsame for all APs), N0 is the noise power at the receiver,and IPU is the interference power from the primarytransmitter at the secondary receiver. We make the con-servative assumption of setting IPU = -87.5 dBm, corre-sponding to being at the primary protection contour [32].

Let Mx be the fraction of time that AP x is grantedchannel access by the CSMA/CA protocol. If AP x hasother APs in its contention domain (i.e. if Aj

x 6= ∅), theother APs will also be contending for access to the sharedwireless medium, and the channel access time of AP xwill be Mx < 1. Thus the downlink throughput of useru associated with AP x may be estimated as

Ru,x =Mxρ(SINRu,x), (8)

where ρ(SINRu,x) is the raw data rate provided touser u by AP x. Assuming fair channel access, Mx isapproximately equal to the inverse of |Aj

x|, the numberof APs in the contention domain of AP x. Therefore, (8)may be re-expressed as

Ru,x = ρ(SINRu,x)/|Ajx|. (9)

5.2 Performance AnalysisIn this section we present and analyse simulation resultsof the performance of a secondary Wi-Fi-like network inTVWS for different deployment scenarios. Our resultswere obtained via MATLAB simulations by consideringuser terminal locations on an evenly spaced grid overthe secondary network study area and calculating thedownlink throughput obtained at each potential userlocation for each AP in the network, thereby determiningthe coverage area of each AP and the downlink ratefor an associated user within its cell. The estimateddownlink throughput for a user randomly located withinthe cell of AP x is given by

Rx =1

|Ux|∑u∈Ux

Ru,x, (10)

where Ru,x is given by (9) and Ux is the set of userlocations on the sampling grid which are covered by APx. The mean estimated downlink rate over the networkof |A| APs, RA, is given by the average of Rx over A,and the mean cell-edge (maximum) AP coverage rangeover the network, rAmax, is defined analogously.

5.2.1 Effect of Secondary-to-Secondary InterferenceFig. 10 presents simulation results of RA versus rAmax foreach deployment scenario. Results for TVWS operationare presented alongside IEEE 802.11g Wi-Fi at 2.4 GHzfor reference. Each curve in Fig. 10 was generated byvarying the transmission power of the secondary APs,Ptx = {0, 5, 10, 15, 20, 25, 30} dBm. The dashed curvesrepresent the idealised case of ignoring the effects ofinter-AP interference6, whereas the solid curves takeinter-AP interference into account. The thin solid curvesrepresent the worst-case scenario of all APs being co-channel (i.e. J=1, Aj=A). The thick solid curves char-acterise the performance of the Wi-Fi-like secondarynetwork whereby each AP randomly selects a channelto operate on from the list of available TVWS channels.The curves in red show the performance when operatingonly on accessible TVWS channels; we will defer discus-sion of these results until Section 5.2.2.

When interference among APs is ignored, the resultsin Fig. 10 confirm the highly attractive viewpoint of asecondary Wi-Fi-like deployment in TVWS advocated byproponents of “Wi-Fi on steroids” in earlier studies. Fora given power budget, operation in the lower frequencyTV bands results in a significant communication rangeincrease compared to operating at 2.4 GHz, while achiev-ing average downlink rates proportional to the channelwidth. For example, operating in TVWS with a 24 MHzchannel provides a 20% higher throughput while increas-ing the maximum AP range by up to 240 m comparedto IEEE 802.11g. However, once inter-AP interference istaken into account, we observe a marked degradation inperformance. For example, the range difference betweentraditional Wi-Fi and operating in TVWS with a 24 MHzchannel reduces to at most between 67m and 130m,while the rate is greatly diminished due to neighbouringco-channel APs with overlapping contention domains.This congestion effect is more pronounced for APs op-erating in the lower frequency range of TVWS preciselybecause of the accompanying larger cell sizes. Conse-quently, operating in TVWS with a 24 MHz channelyields a 23% lower throughput than traditional Wi-Fi at2.4 GHz, despite a 20% wider channel (for Ptx=30 dBm).By contrast, for the indoor urban scenario in Fig. 10(b),the coverage range remains small enough that inter-APinterference does not become as dominant, so that therange extension afforded by operating in TVWS (of upto 10-20 m) holds even under interference conditions.Thus one of the key potential benefits of TVWS isbetter propagation through walls, making it attractivefor indoor deployments. However, throughput being di-rectly proportional to the channel bandwidth, the TVWSspectrum in this context simply becomes a new ISMband with slightly extended range. The outdoor rural

6. This is equivalent to each AP operating on its own non-overlapping channel, such that |Aj |=1 ∀j, A

jx=x ∀x ∈ A; it is then

straightforward to derive Rx and rmax analytically for a given Ptx

and deployment scenario.

12

0 100 200 300 400 500 600

0

5

10

15

20

25

Avera

ge e

stim

ate

d d

ow

nlin

k r

ate

over

cell

[Mbps]

Maximum (cell edge) coverage range [m]

no inter−AP interference

with inter−AP interference: all APs co−channel

with inter−AP interference: APs randomly select an "available" non−overlapping channel: − TVWS, urban: [17 x 8−MHz] or [7 x 16−MHz] or [4 x 24−MHz] channels available − TVWS, rural: [14 x 8−MHz] or [4 x 16−MHz] channels available − 2.4 GHz ISM: [3 x 20−MHz] channels available

with inter−AP interference: APs randomly select an "accessible" non−overlapping channel(only channels where interference constraint to primary is respected are accessible)

8−MHz channel in TVWS



20−MHz channel in 2.4 GHz ISM band (802.11g Wi−Fi)

(a) Outdoor urban scenario

0 5 10 15 20 25 30 35 40 45

5

10

15

20

25

30

Avera

ge e

stim

ate

d d

ow

nlin

k r

ate

over

cell

[Mbps]


(b) Indoor urban scenario

0 500 1000 1500 2000 2500

2

4

6

8

10

12

14

16

18

20

Avera

ge e

stim

ate

d d

ow

nlin

k r

ate

over

cell

[Mbps]


(c) Outdoor rural scenario

Fig. 10. Average estimated downlink rate for a covereduser vs. maximum AP coverage range (corresponding Ptx ={0, 5, ..., 30} dBm), for network of Wi-Fi-like secondary APs inTVWS. Results for real example of TVWS channel availabilitycompared to no inter-AP interference (best-case) and all APsbeing co-channel (worst-case); performance of Wi-Fi at 2.4 GHzshown for reference. Shown in red are results for permissiblesecondary operation on accessible TVWS channels only (i.e.aggregate secondary interference constraint respected).

scenario in Fig. 10(c) exhibits qualitatively similar trendsto the indoor urban case, due to its low AP density.

5.2.2 Effect of Aggregate Secondary-to-Primary Inter-ferenceLet us further refine our estimate of the realisticallyachievable performance of a Wi-Fi-like network inTVWS, as represented by the red curves in Fig. 10. For agiven transmission power Ptx, we iteratively determinethe number of available TVWS channels which remainaccessible to the secondary network once the aggregatesecondary user interference constraint to the primary isrespected, as follows. For each TV channel i the sec-ondary network operates on, we calculate the aggregatesecondary interference caused at the test point PUi onthe primary protection contour (protected pixel closestto the study area),

IA(i) =∑x∈Ai

γPUi,xPtxMx, (11)

where Ai is the subset of all APs in the network op-erating on TV channel i. The calculated value of IA(i)for each simulated network realization is compared withthe maximum aggregate secondary interference budgetassigned to the network7, IAmax(i). Those TV channelswhere IAmax is exceeded are removed from the list ofoperational TVWS channels; the simulations of networkperformance and the aggregate interference testing arethen iteratively repeated until the IAmax constraint isrespected for all remaining accessible channels.

The results of these simulations are presented inFig. 11, which reveals that when aggregate secondaryuser interference is taken into account, there is a dra-matic decrease in the number of TVWS channels thesecondary network can operate on. For the outdoorurban scenario, Fig. 11(a) shows that no 16 MHz or24 MHz channels remain accessible, and the numberof 8 MHz channels reduces from 17 to at most 4 (forPtx below 10 dBm). The highest permissible Ptx forthe outdoor urban scenario is 25 dBm, operating on asingle remaining accessible 8 MHz channel. Fig. 11(b)shows that in the indoor case, no 24 MHz channelsremain accessible, and three 16 MHz channels are onlyaccessible at Ptx = 0 dBm. Of the 17 available 8 MHzchannels, the number of channels that remain accessibleto secondary APs progressively reduces from 10 and 1 asPtx is increased from 0 dBm to 30 dBm. Thus the highestpermissible Ptx for the indoor urban scenario is 30 dBm,operating on a single accessible 8 MHz channel. Finally,Fig. 11(c) shows that in the outdoor rural scenario, of the14 available 8 MHz channels, at most 3 channels remainaccessible to the secondary network (Ptx of 0dBm), withthe highest permissible Ptx being 5 dBm (operating ontwo 8 MHz channels). Of the 4 available 16 MHz TVWSchannels, only one remains accessible, at Ptx of 0 dBm.

7. The budgets assigned to our study areas, using the methoddescribed in Section 5.1.2, range from about -140 to -130 dBm.

13

0 5 10 15 20 25 30

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

Secondary AP transmission power, Ptx

[dBm]

Num

ber

of

TV

WS

channels

for

secondary

opera

tion

"available" channels(aggregate secondary interference constraint to primary ignored)

"accessible" channels(aggregate secondary interference constraint to primary respected)

8 MHz channels in TVWS




0 5 10 15 20 25 30

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17


[dBm]

Num

ber

of

TV

WS

channels

for

secondary

opera

tion


0 5 10 15 20 25 30

0

1

2

3

4

5

6

7

8

9

10

11

12

13

1414


[dBm]

Num

ber

of

TV

WS

channels

for

secondary

opera

tion


Fig. 11. Comparison of the number of available and accessible TVWS channels for secondary operation vs. secondary APtransmission power Ptx, for different TVWS channel widths. A TVWS channel is deemed to be available if the secondary network isoutside the primary protection contour of the corresponding TV channel(s); an available TVWS channel is deemed to be accessibleif the aggregate secondary interference constraint is respected for the corresponding TV channel(s).

0 20 40 60 80 100 120

0

10

20

30

40

50

60

Range (maximum) for given rate [m]

Estim

ate

d d

ow

nlin

k r

ate

[M

bps]

2.4 GHz, 3 x 20−MHz channels, Ptx

= 10 dBm


= 20 dBm

5 GHz, 15 x 20−MHz channels, Ptx

= 10 dBm


= 23 dBm

TVWS, 4 x 8−MHz channels, Ptx

= 10 dBm


0 5 10 15 20 25 30 35

0

10

20

30

40

50

60


Estim

ate

d d

ow

nlin

k r

ate

[M

bps]


= 25 dBm


= 20 dBm


= 25 dBm


= 23 dBm


= 25 dBm


0 50 100 150 200 250 300

0

10

20

30

40

50

60


Estim

ate

d d

ow

nlin

k r

ate

[M

bps]


= 5 dBm


= 20 dBm


= 5 dBm


= 23 dBm


= 5 dBm


Fig. 12. Estimated downlink rate vs. coverage range for network of Wi-Fi-like secondary APs operating in TVWS on accessiblechannels only, where the aggregate secondary interference constraint is respected for the given Ptx. This best realisticallyachievable performance for the secondary Wi-Fi-like network in TVWS is compared to that of IEEE 802.11g and IEEE 802.11aWi-Fi at 2.4 GHz and 5 GHz respectively (operating at same Ptx as the TVWS network, and at European regulatory limit of 20 dBmand 23 dBm for the respective unlicensed bands).

The red curves in Fig. 10 show the realistically achiev-able performance in TVWS for the number of practicallyaccessible channels shown in Fig. 11. Given the rateversus range trade-off when operating at various Ptx

and channel widths, the results in Fig 10 suggest thefollowing best-case feasible operating TVWS points forcompetitiveness with 2.4 GHz Wi-Fi: (i) outdoor urban:Ptx = 10 dBm, 4 x 8-MHz channels; (i) indoor urban:Ptx = 25 dBm, 4 x 8-MHz channels; and outdoor rural:Ptx = 5 dBm, 2 x 8-MHz channels.

Fig. 12 presents simulation results of the rate versusrange performance8 for the identified best-case feasibleTVWS operating points. We additionally present IEEE802.11a Wi-Fi performance results in the largely under-utilized 5 GHz band, to benchmark the relative utilityof TVWS as a “congestion-relief” solution for the over-crowded 2.4 GHz band. For the outdoor urban scenario,Fig. 12(a) reveals that best-case feasible operation inTVWS yields a lower maximum and average throughputcompared to the 2.4 or 5 GHz bands, but gives a higherrange at an equivalent rate for the same power budget.However, compared to a Wi-Fi network operating in the

8. Each curve in Fig. 12 corresponds to a single point in Fig. 10.

2.4 or 5 GHz bands at the European regulatory limit of 20dBm and 23 dBm for the respective unlicensed bands, theTVWS variant is always inferior. Fig. 12(c) demonstratesthe same qualitative behaviour for the best-case feasibleTVWS operation in the outdoor rural deployment. Thissuggests that dense outdoor operation of a Wi-Fi-likesecondary network in TVWS is not attractive. For theindoor urban scenario, Fig. 12(b) shows that best-casefeasible operation in TVWS, despite yielding a lowermaximum and average throughput compared to the 2.4or 5 GHz bands, does provide a range extension (of upto over two-fold) for an equivalent rate. This indicatesthat operation of a Wi-Fi-like secondary network inTVWS may be viable for extending range in indoorenvironments, but only for low-rate applications such assensor reading and machine-to-machine communication.

6 CONCLUSIONS

In this paper we have argued that merely quantifyingthe number of available channels for a single secondarydevice is a strictly limited indication of the real-worldvalue of such whitespace spectrum. Secondary networksmust be analyzed under more realistic conditions of ag-gregate interference to derive appropriate power levels

14

and access constraints, and valid and reliable perfor-mance metrics can only be obtained once the detailsof medium access, transceiver design, and deploymentmodel are included in the whitespace usage models.Therefore, in this paper we have presented a systematicand robust spectrum assessment methodology to facili-tate such studies on the feasibility of secondary networkdeployments, and demonstrated it via a case study ofcellular and Wi-Fi-like secondary deployments in TVWS.

Our analysis suggests that macro-cellular only deploy-ment of an LTE offloading network for TVWS could be aviable option. While universal coverage is generally notachievable, data offloading and regional deploymentsare plausible applications. Nevertheless, TVWS remaina challenging operation environment even in this case,because the transceiver complexity puts significant limitson the network’s exploitation capabilities. Consideringrecent proposals in the regulatory community to depletethe TVWS through creating new digital dividends, theauthors hence remain skeptical whether TVWS operationwill become a feasible option for cellular networks.

We have shown that the performance of the Wi-Fi-like system is hardly “Wi-Fi on steroids” as had beenclaimed based on naive extrapolations of early averagewhitespace availability estimates. The efficiency and theachievable data rates of such a system in rural andurban broadband scenarios is poor, and the best use caseseems to be urban indoor range extension. However,this case alone may be a weak point for regulatorychanges in countries that have not opened TVWS. Wi-Fi-like systems can be deployed, but one could as wellargue, for example, that the rural broadband problemcould be simply solved by using directive antennas andslightly higher transmission power with the existingIEEE 802.11g devices.

Therefore, our case studies clearly illustrate the largeextent to which the overall picture on the attractivenessand feasibility of secondary deployments changes oncewe go beyond simple whitespace availability estimationand undertake a full spectrum assessment. Our systemsanalysis has particularly underlined that it is vital to con-sider the aggregate interference constraint to the primarysystem when estimating the number of accessible TVWSchannels. This has important design implications forTVWS database services: unless they perform dynamicinterference estimation based on the locations and trans-mission power of each secondary user, very conservativeestimates for the protection radius must be used.

REFERENCES[1] J. Zander and P. Mahonen, “Riding the data tsunami in the cloud:

Myths and challenges in future wireless access,” IEEE Commun.Mag., vol. 51, no. 3, pp. 145–151, March 2013.

[2] Y.-C. Liang, K.-C. Chen, G. Y. Li, and P. Mahonen, “Cognitive ra-dio networking and communications: An overview,” IEEE Trans.Vehic. Commun., vol. 60, no. 7, pp. 3386–3407, Sept. 2011.

[3] E. Biglieri et al., Principles of Cognitive Radio. Cambridge Univer-sity Press, 2012.

[4] K. Harrison, S. Mishra, and A. Sahai, “How much white-spacecapacity is there?” in Proc. IEEE DySPAN, Singapore, 2010.

[5] J. van de Beek, J. Riihijarvi, A. Achtzehn, and P. Mahonen, “TVwhite space in Europe,” IEEE Trans. Mobile Comput., vol. 11, no. 2,pp. 178–188, Feb. 2012.

[6] A. Masini et al., “WhiteFi: The usage of UHF frequencies forbi-directional services in mountainous scenarios,” in Proc. IEEEAPWC, Torino, 2011.

[7] P. Bahl et al., “White space networking with Wi-Fi like connectiv-ity,” Comput. Commun. Rev., vol. 39, no. 4, pp. 27–38, Oct. 2009.

[8] Y. Yuan et al., “KNOWS: Cognitive Radio Networks Over WhiteSpaces,” in Proc. IEEE DySPAN, Dublin, 2007.

[9] S. W. Oh, F. Chin, and S. G. Kerk, “TV white-space for smartgrid,” in Proc. CogART, Barcelona, 2011.

[10] Cambridge White Spaces Consortium, “Cambridge TV WhiteSpaces Trial: A Summary of the Technical Findings,” 2012.[Online]. Available: http://www.cambridgewireless.co.uk

[11] H. Holma and A. Toskala, LTE for UMTS - OFDMA and SC-FDMABased Radio Access. Wiley, 2009.

[12] T. Baykas et al., “Overview of TV white spaces: Current regu-lations, standards and coexistence between secondary users,” inProc. IEEE PIMRC Workshops, Istanbul, 2010.

[13] ECO WG SE43, “ECC Report 148: Measurements on the perfor-mance of DVB-T receivers in the presence of interference from themobile service (especially from LTE),” 2010.

[14] A. Achtzehn, M. Petrova, and P. Mahonen, “On the performanceof cellular networks in TVWS,” in Proc. IEEE ICC, Ottawa, 2012.

[15] Huawei, “Huawei to Launch TV White Space LTETDD System Trial,” Oct. 2011. [Online]. Available:http://www.huawei.com/en/about-huawei/newsroom/press-release/hw-103931-huaweitvwsltetdd.htm

[16] IEEE. (2012, Jan.) Status of project P802.11af. [Online]. Available:http://www.ieee802.org/11/Reports/tgaf update.htm

[17] L. Simic, M. Petrova, and P. Mahonen, “Wi-Fi, but not on steroids:Performance analysis of a Wi-Fi-like network operating in TVWSunder realistic conditions,” in Proc. IEEE ICC, Ottawa, 2012.

[18] A. Achtzehn, L. Simic, M. Petrova, P. Mahonen, V. Rakovic,P. Latkoski, and L. Gavrilovska, “Software Tool for AssessingSecondary System Opportunities in Spectrum Whitespaces,” inProc. IEEE WoWMoM Demos, Madrid, 2013.

[19] Y. Zhao, B. Le, and J. Reed, “Network support - the radioenvironment map,” in Cognitive Radio Technology. Elsevier, 2006.

[20] Federal Communications Commission, “In the matter of unli-censed operation in the tv broadcast bands: Second memorandumopinion and order,” Tech. Rep. 10-174A1, 2010.

[21] ECO WG SE43, “ECC Report 185: Definition of technical andoperational requirements for WSD in 470-790 MHz,” 2013.

[22] G. Hufford, A. Longley, and W. Kissick, “A guide to the use ofthe Irregular Terrain Model in the area prediction mode,” NTIA,Tech. Rep. 82-100, 1982.

[23] ITU-R, “Method for point-to-area predictions for terrestrial ser-vices in frequency range 30 MHz to 3 000 MHz (P.1546-3),” 2010.

[24] ETSI, “Digital Video Broadcasting (DVB); Framing structure,channel coding and modulation for digital terrestrial television(EN 300 744),” 2009.

[25] ITU-R, “Planning criteria, including protection ratios, for digitalterrestrial television services in the VHF/UHF bands (BT.1368-9),”2011.

[26] S. Ben Slimane, “Bounds on the distribution of a sum of indepen-dent lognormal random variables,” IEEE Trans. Commun., vol. 49,no. 6, pp. 975 –978, June 2001.

[27] K. Harrison and A. Sahai, “Potential collapse of whitespaces andthe prospect for a universal power rule,” in Proc. IEEE DySPAN,Aachen, 2011.

[28] G. Yuan, X. Zhang, W. Wang, and Y. Yang, “Carrier aggregationfor LTE-Advanced mobile communication systems,” IEEE Com-mun. Mag., vol. 48, no. 2, pp. 88–93, Feb. 2010.

[29] T. S. Rappaport, Wireless Communications: Principles and Practice,2nd ed. Upper Saddle River: Prentice Hall, 2002.

[30] IEEE Standard, Std. IEEE Std 802.11-2007 (Revision of IEEE Std802.11-1999), 2007.

[31] H. Nguyen, F. Baccelli, and D. Kofman, “A stochastic geometryanalysis of dense IEEE 802.11 networks,” in Proc. INFOCOM,Anchorage, 2007.

[32] A. Achtzehn, M. Petrova, and P. Mahonen, “Deployment of acellular network in the TVWS: a case study in a challengingenvironment,” in Proc. CoRoNet, Las Vegas, 2011.

15

Andreas Achtzehn received degrees in com-puter engineering and in business administrationand engineering from RWTH Aachen University,Germany. He is working as a research assistantand study coordinator at the Institute for Net-worked Systems (iNETS). His research interestsare focused on the system-wide design and opti-mization of next-generation communication net-works, with an emphasis on radio-environmentmodeling and regulatory policy design. Beforejoining iNETS, he stayed at the Communication

Research Group, Uppsala University, Sweden and in the DistributedSensor Systems Group at Philips Research, Eindhoven, The Nether-lands. He is a student member of the IEEE.

Ljiljana Simic is a senior researcher and re-search coordinator at the Institute for NetworkedSystems at RWTH Aachen University. She re-ceived her Bachelor of Engineering (with 1stClass Honours) and Doctor of Philosophy de-grees in Electrical and Electronic Engineeringfrom The University of Auckland in 2006 and2011, respectively. Her PhD research focusedon energy efficient cooperative communicationfor distributed wireless networks, and was sup-ported by a Bright Future Top Achiever Doctoral

Scholarship from the Tertiary Education Commission of New Zealand.Prior to joining RWTH in 2011, she held a teaching position in theDepartment of Electrical and Computer Engineering at The Universityof Auckland. A part of her research work at RWTH has contributed tothe EU FP7 project QUASAR, the focus of which was quantifying thereal-world benefits of secondary spectrum access for future cognitiveradio networks. Her research interests are in the areas of efficient spec-trum sharing paradigms, cognitive and cooperative communication, self-organizing and distributed networks, and telecommunications policy.She is a member of the IEEE.

Marina Petrova is an assistant professor in theFaculty of Electrical Engineering and InformationTechnology at RWTH Aachen University. Sheleads the Self-Organized Networks researchgroup, which strongly focuses on system-levelstudies of future wireless systems and model-ing and prototyping of protocols and solutionsfor heterogeneous and DSA wireless networks.Dr. Petrova holds a degree in engineering andtelecommunications from University Ss. Cyriland Methodius, Skopje and a Ph.D from RWTH

Aachen University, Germany. She has served in technical programcommittees of numerous IEEE conferences and workshops. She wasa TPC-co Chair of DySPAN 2011. Currently she serves as a TPC-cochair of SRIF’14 in conjunction with SIGCOMM. She is a member of theIEEE.

Petri Mahonen works as a full professor and thechair of networked systems at RWTH AachenUniversity. He is the founding head of the Insti-tute for Networked Systems at RWTH AachenUniversity. Before joining RWTH, he was a pro-fessor and research director of networking in theCenter for Wireless Communications at the Uni-versity of Oulu, Finland. He has been a princi-pal investigator in several international researchprojects, including several large European Unionresearch projects for wireless communications.

His current research focuses on cognitive radio systems, embed-ded intelligence, future wireless broadband networks, including mmW-systems, Medium Access techniques, and applied mathematical physicsmethods for telecommunications. He is a senior member of the IEEE.

feasibility of secondary networks: analysis methodology

Documents