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Hindawi Publishing CorporationEURASIP Journal on Wireless Communications and NetworkingVolume 2008, Article ID 470571, 12 pagesdoi:10.1155/2008/470571

Research ArticleExamining the Viability of Broadband Wireless Access underAlternative Licensing Models in the TV Broadcast Bands

Timothy X. Brown and Douglas C. Sicker

Interdisciplinary Telecommunications Program, University of Colorado, Boulder, CO 80309-0530, USA

Correspondence should be addressed to Timothy X. Brown, [emailprotected]

Received 5 June 2007; Accepted 25 January 2008

Recommended by Milind Buddhikot

One application of cognitive radios is to provide broadband wireless access (BWA) in the licensed TV bands on a secondaryaccess basis. This concept is examined to see under what conditions BWA could be viable. Rural areas require long rangecommunication which requires spectrum to be available over large areas in order to be used by cognitive radios. Urban areashave less available spectrum at any range. Furthermore, it is not clear what regulatory model would best support BWA. This paperconsiders demographic (urban, rural) and licensing (unlicensed, nonexclusive licensed, exclusive licensed) dimensions. A generalBWA efficiency and economic analysis tool is developed and then example parameters corresponding to each of these regimes arederived. The results indicate that an unlicensed model is viable; however, in urban areas spectrum needs can be met with existingunlicensed spectrum and cognitive radios have no role. In the densest urban areas, the licensed models are not viable. This isnot simple because there is less unused spectrum in urban areas. Urban area cognitive radios are constrained to short ranges andmany broadband alternatives already exist. As a result the cost per subscriber is prohibitively high. These results provide input tospectrum policy issues.

Copyright © 2008 T. X. Brown and D. C. Sicker. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.


Cognitive radios (CRs) have the potential for providingbroadband wireless access (BWA) as an alternative to existingbroadband options. In the Notice of Proposed Rule Making,Unlicensed Operation in the TV Broadcast Bands, the FCCproposed both low- and high-power cognitive radio alter-natives in the TV bands [1]. The latter can provide BWAvia outdoor access points (AP) to individual customers. Astandard for BWA in the TV bands is already being developedby the IEEE in the event when such rules are made [2].In urban areas, CR-based BWA is a potential competitor tocable, DSL, and wireless options in the unlicensed bands[3]. In rural areas, the better propagation at TV-bandfrequencies below 1 GHz may provide a low-cost option forBWA. In this paper, we test these potential outcomes via acombined technical and economic analysis tool for BWA.Unlike more technical analysis (e.g., see [2]), we examine theeconomics of providing CR-based BWA in urban and ruralenvironments. In urban environments, there is relativelylittle unused spectrum in the TV bands. However, customerdensity is high, so the system can operate using short-range

access points (APs) and have large reuse. In rural areas,the available spectrum is greater. However, APs need to uselonger ranges to efficiently cover the sparse customers. Long-range transmitters may find many channels excluded becauseof potential interference with distant TV coverage areas.

A further nuance to CR BWA deployment is the reg-ulatory regime under which it operates. Access to the TVspectrum is controversial [4] and several alternatives havebeen proposed [5], that is, commons and property rightsmodels. To capture this range, we examine several unlicensedand licensed regimes. In an unlicensed regime, spectrum isfree, but the CR must contend with other users who mayor may not have compatible architectures. In an exclusivelicensed regime, the CR BWA operator must pay for thespectrum and can plan efficient use of the spectrum. Inbetween is a nonexclusive licensed regime where differentlicensed CR operators pay for access to the spectrum and maybe required to cooperate with each other. We do not dwellin this paper on the likelihood or mechanism through whichany of these regimes would be realized. Rather, we investigatethe impact of each of these regimes on the economics andspectrum needs of BWA.

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In this paper, we develop a general purpose BWA spec-trum requirements and economics tool. With this tool, weexamine the network cost for deploying a BWA networkin the six combinations of demographics (urban, rural)and licensing (unlicensed, nonexclusive licensed, exclusivelicensed). For each of these regimes, parameters are esti-mated. The resulting spectrum requirements and cost ofeach regime indicates its relative viability. This paper extends[6], by providing sensitivity analysis of key parameters. Westart by providing an overview of the BWA communicationarchitecture and a description of each regime.


The primary purpose of the BWA system is to provideconnectivity between the user stations and the Internet. TheBWA system consists of one or more access points (AP) thatcommunicate with fixed user stations. Multiple APs may beneeded to provide sufficient coverage or to provide sufficientcapacity similar to a cellular system. The AP may consistof one or more antennas each covering different directions.Radio channels are reused over the coverage area.

The user traffic from each AP needs to be backhauled to asingle or a small number of Internet gateways. The backhaulchannels can be wired or wireless. Thus the spectrumrequirements can be divided into access spectrum betweenusers and the AP and backhaul spectrum between the AP andthe Internet gateways.

The APs communicate to users over links that maypass through or around man-made clutter, vegetation, andterrain. For such links, frequencies below 3 GHz are mostsuitable [7]. However spectrum below 3 GHz is less plentifulcompared to higher frequency spectrum. Since TV bandsare below 1 GHz and potentially have large tracts of unusedspectrum, they are especially suitable. The backhaul linksare more likely to be line of site since APs are mountedhigher and the Internet gateways can have dedicated towers.Such links can be provided using higher frequencies, above3 GHz, where unlicensed spectrum is plentiful and dedicatedhigh-capacity microwave links are available. For instance,this is the approach used in the Philadelphia municipal BWAsystem [8]. Therefore, in this paper we assume that thebackhaul spectrum needs (if any) are met with the readilyavailable higher frequencies and we focus on the accessspectrum needs.

For this paper, the BWA system uses unused spectrum inthe TV bands for its access spectrum. We focus on the UnitedStates, however the analysis framework applies more broadlyto other countries as well. The BWA system must avoid inter-fering with the licensed broadcast uses of the spectrum. TheAPs in the BWA system use any of a number of techniques toidentify unused spectrum. To be specific, we assume that theyuse a combination of geolocation and access to a database asdescribed in [9]. The user stations are controlled by the APand only transmit as permitted by the AP.


We describe the six regimes and six factors which distinguishthem. The six regimes we consider vary across demographic

(urban, rural) and licensing (unlicensed, nonexclusivelicensed, exclusive licensed) dimensions.

3.1. Demographic and licensed regimes

We explore two aspects that follow from this cognitive radiousage of the spectrum. First, the available spectrum variesfrom place to place. Areas that have fewer licensed userswill have more potential spectrum for BWA. The questionis whether the available spectrum is sufficient for a viableBWA system. To explore this aspect, we will investigate ruraland urban areas. As a limit, we consider two extremes: NewYork City, one of the busiest television broadcasting regionsin the country; and Buffalo County South Dakota, noted asbeing sparse. (Buffalo county, SD was chosen since it has thelowest median per capita income among all US countries. Itis a candidate for using BWA to close the digital divide.)

The second aspect to BWA access to the TV spectrumis that the licensing regime for this secondary access hasnot been finalized and we seek to understand how differentlicensing regimes could impact the BWA service. Unlicensedaccess to the spectrum enables many users and potentiallyuncoordinated services to be offered. Barriers to newentrants are low and the BWA radio would need to resolvethe uncoordinated contention for radio resources. At theother extreme, the BWA may be given licensed and exclusiveaccess to the spectrum not being used by primary users. Thisreduces competition at both a service level from other BWAproviders and a radio resource level from other contendingusers. However, the exclusive access may require the BWAprovider to pay for the license, which would increase theBWA service cost. As a third option, we consider offeringmultiple licenses (nonexclusive licensing). These licensescould take several forms, ranging from permission to accessthe entire available unused spectrum to divide the spectruminto specific blocks, which are licensed and used on anexclusive basis. For our purposes, we consider this rangeof options equivalent if the number of licensees is small. Asmall number of licensees will be motivated to cooperate andprovide de facto divisions of spectrum in the case that nospecific exclusive block license is provided. The nonexclusivelicense regime may require the BWA operator to pay for thelicense.

3.2. Six factors

For the purposes of our analysis, the six regimes differ insix factors: population density, transmission range, availablespectrum, traffic per person, spectral efficiency, and cost ofspectrum. These are divided along demographic and licenseaxis.

Urban and rural areas, by definition, differ in populationdensity. An urban area can have densities over 4,000 peopleper square kilometer and a rural area under 10 people persquare kilometer [10].

Generally, to be more efficient, rural systems will requireAPs to have longer range in order to efficiently reach thepopulation. In urban areas, the AP can be mounted onexisting structures and, as described later, a short rangesuch as 500 m is both achievable and sufficient. In rural

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T. X. Brown and D. C. Sicker 3






01 10 100 1000

Buffalo, SD

New York, NY

Interference radius (km)








Figure 1: The number of 6 MHz TV channels available for cognitiveradio use as a function of potential interference. Computed forNew York City (Times Square) and Buffalo County, SD (geographiccenter).

areas, APs will be mounted on higher towers to achievelonger ranges. As an example, 10 km would be a reasonabletarget. The choice of range depends on the availability ofspectrum in the vicinity of the BWA transmitters. Longertransmission range requires spectrum to be available overlonger distances. This issue is addressed below. Populationdensity and transmission range together affect the numberof people captured by a single AP. However, their affects arecounterbalancing. As an example, rural areas may have 400times smaller density, D, while the range, r, can be 20 timeslarger so that r2 is 400 times larger. In this case, they wouldexactly counterbalance each other so that a rural AP and anurban AP capture the same population.

A key factor in BWA viability is the availability ofspectrum. The appendix describes a method for estimatingthe unused spectrum, also known as “whitespace.” Figure 1shows the availability of unused TV channels as a functionof the interference radius of the CR. The interference radiuscan be significantly larger than the transmission range ofthe CR due to TV receivers’ sensitivity to interference. Theappendix estimates that the interference range is 10 times thetransmission range. From Figure 1, a transmission range of500 m (5 km interference range) in New York would yield4 unused channels (24 MHz). In Buffalo, SD a transmissionrange of 10 km (100 km interference range) would yield 32unused channels (192 MHz). The exclusive licensed modelwould make this spectrum available to the BWA operator.The unlicensed and nonexclusive licensed model woulddivide the spectrum between different operators.

The unlicensed model can be supported by otherunlicensed spectrum below 3 GHz. There is 109.5 MHz ofuseful spectrum, that is, 26 MHz at the 902–928 MHz and83.5 MHz at 2.4–2.4835 GHz. Other unlicensed spectrum isavailable but it is not useful for this application because of thesmall size of the bandwidth block, limits on power, or limitson usage.

The traffic per person, U, represents the total trafficdemanded on the BWA system divided by the total popu-

lation. It is affected by both the licensing and demographicregimes. In urban areas, BWA is one of several existing broad-band delivery modes. In rural areas the major competitoris satellite. Compared to satellite, BWA has the potential toprovide significantly lower delays and greater bandwidth. Asa result, BWA’s relative market share for broadband accesswill be more in rural areas than in urban areas. If unlicensedor nonexclusive licenses are used, then there will be lowerbarriers to entry for BWA competitors and the market sharefor each BWA provider will be less. The traffic per personaffects the amount of spectrum required. More user trafficper person requires more spectrum.

Spectral efficiency, E, captures the ratio of system trafficto required spectrum to carry that traffic. It will dependon whether unlicensed or licensed access will be granted.With unlicensed spectrum, the BWA operator must contendwith other uncoordinated spectrum users. More robust butless efficient transmission schemes are required in this case,which lowers the spectral efficiency and accordingly increasesthe required spectrum. Though the unlicensed approachmay require more spectrum, unlicensed spectrum promotescompetition and supports multiple service providers withoutrequiring any additional spectrum. Moreover, unlicensedspectrum promotes innovation since it presents lower bar-riers to diverse new services and applications. Further, inthe future if the BWA service becomes less viable, thenthe unlicensed spectrum will already be available for otheruses, providing a natural technology evolution path withoutprotracted spectrum reassignment periods. Thus increasedspectrum requirements are traded against the reducedadministrative burden and operator flexibility when usingunlicensed access.

Spectrum cost depends on the licensing and demo-graphic regimes. Unlicensed spectrum has no direct costto the BWA operator. Based on recent history, the licensedregimes will require the BWA operator to pay some costin proportion to the population and the bandwidth of thespectrum. This cost has been determined through spectrumauctions. In these auctions, the cost of rural spectrum isoften much lower than urban spectrum. Lower spectrumcost tends to lead to more spectrum usage; however, morespectrum is available.

To make the different regimes and factors concrete, thenext section develops a tool for assessing the persubscribercost and required spectrum.


We now present three approaches to determine the requiredspectrum. When deploying a network, two major designconstraints dominate design—cost and usage. Engineeringthe design of a network generally requires minimizing thecost of the system, while ensuring the operational demandscan adequately be maintained. We use these principlesto inform our approach in defining the overall spectrumrequirements.

The first approach is based on a required service datarate. The amount of spectrum required at an AP to providethis rate to a user is a lower bound on the required spectrum.

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We denote this as the minimum service rate spectrumrequirements (MSR). The second approach is based onminimizing the number of APs. Fewer APs lowers the systemcost, while requiring more spectrum to be able to carrythe greater traffic load on each AP. We denote this as theminimum system cost spectrum requirement (MSC). MSRand MSC set upper and lower bounds on the requiredspectrum. Within these bounds, an operator will minimizethe overall cost to build their system. The third approachanalyzes the total capacity required by the system to carryevery user’s average traffic load. In principal this capacitycan be provided with any amount of spectrum. However,to have sufficient total capacity there is a trade off betweenthe amount of spectrum and number of APs. As the amountof spectrum decreases, the number of APs and the cost ofthe system increase. Thus it becomes a tradeoff betweenavailable spectrum and cost of providing the service. Basedon the value placed on the spectrum used, we can determinea spectrum that minimizes the total cost of the BWAdeployment and spectrum. We denote this as the minimumtotal cost spectrum requirements (MTC).

4.1. Key factors

The key factors in the model are described in detail in thissection.

Spectrum efficiency factors

A number of wireless technologies are in place today forproviding BWA. The IEEE 802.11a/b/g family of protocolsprovides a range of communication capabilities with ratesfrom 1 up to 54 Mbps. The 802.16 family of protocols providedata rates up to 134 Mbps. These technologies can use moreor less spectrum to increase or decrease communicationrates. The 802.16 standards work at a variety of spectralbandwidths with proportional variations in channel rates.An AP with more than one wireless interface working ondifferent channels will also have more capacity. Two ormore interfaces will yield a proportional two or more factorincrease in capacity. These observations suggest that a singleAP can use whatever spectrum is made available to it andthe useable channel rate is proportional to the spectrumassigned. We denote the ratio of channel rate to spectrumassigned as the spectral efficiency. Given these observations,an AP can provide a rate B = SE, where B is the data rate (inbps), S is the spectrum (in Hz), and E is the spectral efficiency(in bps/Hz).

The spectral efficiency is a function of several factorsE = emodulation ereuse eprotocol eloading esharing. The modulationefficiency, emodulation, is the ability of a modulation schemeto produce a bit rate in a given channel bandwidth, in(bps/Hz). The reuse efficiency factor, ereuse ≤ 1, accountsfor the fact that channels may not be used at every APdue to cochannel interference between adjacent AP. Theprotocol efficiency factor, eprotocol ≤ 1, accounts for theoverhead of packet headers and channel access. The loadingefficiency factor, eloading ≤ 1, accounts for the level to whicha channel can be loaded in the long term and still experience

good performance. Too high a loading leads to excessivequeuing and delays. The minimum service rate modelconsiders only the peak rate and so loading is not relevant(eloading = 1). The sharing factor, esharing ≤ 1, accountsfor additional overhead to resolve contention between thedifferent coexisting operators in the same band.

Access point cost

The cost of building the BWA network infrastructure andpaying for it depends on the cost of the AP and the cost ofterminating to the Internet. For these costs, we consider thenet present value costs with discount factor d (A discountfactor of d means that a cost of x dollars y years in the futurehas NPV of x(1 − d)y . Given an ongoing cost stream of xdollars per year and discount factor of d, the NPV of thisstream is x/d.).

For the AP, this is the initial cost of the hardwareand installation, and the discounted cost of the futuremaintenance and operations expenses

Kap = k f +kom

d, (1)

where k f is the initial fixed hardware and installation costsand kom is the annual operations and maintenance costs.

Traffic per person

Active BWA users can generate significant traffic. However,these users may be a fraction of the total populationdepending on a number of factors. Let U be the trafficper person where U = utraffic uactive utakeup umrktshr uoperator.The traffic per active user, utraffic, is the average usage ofsuch a user over the busy hour in bps. It includes thetotal of uplink and downlink traffic. The active user factor,uactive ≤ 1, is the average fraction of users that are activeduring the busy hour. The take up factor, utakeup ≤ 1, isthe ratio between the number of broadband users and thetotal population. The market share factor, umrktshare ≤ 1, isthe fraction of broadband users that are users of BWA. Theoperator factor, uoperator ≤ 1, is the fraction of the BWAmarket captured by one BWA operator. A BWA operator hasutakeup umrktshare uoperator customers (as a fraction of the totalpopulation) which are generating utraffic uactive bits per secondof traffic on average in the busy hour.

4.2. Minimum service rate spectrum requirements

Broadband service providers often specify a service rate thatthey are providing to users, such as 1.5 Mbps DSL or a27 Mbps Cable modem. This rate is the peak rate at whichusers can exchange data with their service provider. Thisrate is typically shared among different users and individualusers can have average rates that are only a fraction of thiscarrier specified rate. However, this specified rate is oftena criterion in comparing different service offerings. Theminimum service rate spectrum requirements model relatesa specified minimum service rate offered to users, denotedas the user bandwidth, BU , and the spectrum required to

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provide this bandwidth at each AP. Given a total spectrumS, the user bandwidth per AP is BAP = SE. This bandwidthmust be shared by all users in practice, but defines the peakusable rate any customer could hope to achieve. Thus therequired spectrum is


(MSR spectrum requirement), (2)

where SMSR is the required spectrum (in Hz, Hertz), BU isthe user bit rate per user (in bps, bits per second), and E isthe spectral efficiency of the radio system (in bps/Hz). TheMSR spectrum requirement does not depend on the numberof APs or user traffic for the covered area.

4.3. Minimum system cost spectrum requirements

We define the maximum spectrum that can be usefullyexploited to carry a given traffic load per user. As will be seenin Section 4.4, the NPV cost of the BWA system decreaseswith additional spectrum. To a first order, more spectrummeans that each AP can carry more load and so fewer APsare needed, which lowers the overall system cost. However,coverage requires a minimum number of APs (Nmin) toprovide service over the metropolitan area, A:

Nmin = A

πr2, (3)

where πr2 is the maximum coverage area of an AP. Theminimum cost system will have Nmin APs. How muchspectrum is required for these few APs? If U is the averagetraffic per person in the busy hour and D is the populationdensity, then a single AP captures at most UDπr2 traffic. Thebandwidth capacity per AP is SE. Thus,

SMSC = U ·D · πr2

E(MSC spectrum requirement). (4)

This incorporates the number of APs and required trafficcapacity.

4.4. Minimum total cost spectrum requirements

The MSR and MSC spectrum requirements are sufficientif spectrum cost is not considered. The required spectrumis simply the maximum of SMSR and SMSC. The second ismore important since the minimum system cost is typicallyreached with a large SMSC. However, there may be limitedspectrum available. Even if unlimited spectrum is available,there may be a cost to this spectrum. In this case, the BWAoperator will trade the savings in fewer APs against the costof more spectrum.

We first introduce a system cost model. We thenintroduce the spectrum cost and determine what spectrumis required to minimize the total cost of the system andspectrum. The costs only consider the system and spectrumcosts. The customer costs of Internet backhaul, marketing,billing, customer service, and customer premises equipmentare a significant portion of the service cost. However, thesecosts are independent of the spectrum and so are notincluded.

System cost

The system cost, to a first order, is proportional to thenumber of APs. For a total spectrum, S, the data rate per APis again SE. It follows that to provide UP total capacity to atotal population, P, requires the following number of APs:

N = UP

SE. (5)

Thus the system cost per person is

KSys(S) = NKap

P= UKap

SE. (6)

This shows that the cost of the system is directly propor-tional to the traffic generated per user.

The system cost decreases monotonically as S increases.However, the number of APs is lower bounded by Nmin

and so the cost is minimized at SMSC as computed earlier.Additional spectrum only serves to increase the data ratesexperienced by users without changing the system costs.

Spectrum cost

Spectrum is valued in a number of ways. In this study, we useKS to denote the cost of one unit of spectrum (e.g., one MHz)for an area divided by the population of that area (dollars perMHz pop). The total system and spectrum cost per person isthen

KT(S) = KSys(S) + KSS. (7)

The amount of spectrum that minimizes this cost can befound by standard minimization techniques with the result




(MTC spectrum requirement).


This requirement incorporates the user traffic, spectrum effi-ciency, and cost factors. However, the square root decreasesthe sensitivity to these factors.

4.5. Variable sensitivity

The three spectrum models are sensitive to the variables thatare assumed. All of the models depend on the spectrumefficiency, E, and its constituting factors. The first twomodels are directly sensitive. A factor of two change in thespectrum efficiency yields a factor of two change in therequired spectrum. The last two models depend on the userbandwidth, U, and its constituting factors. The relationshipis linear for the MSC model and sublinear for the MTCmodel.

The required user bandwidth, BU , affects only the MSRmodel and the effect is linear. The max population coveredby an AP, Dπr2, affects only the MSC model and the effect islinear. However, D and r tend to have a negative correlationthat reduces the impact of these factors. The cost factors onlyaffect the MTC model and have a sublinear relationship.

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Table 1: Output variables.

Variable Description

S Total spectrum required for all BWA operators

N Total number of APs per 1000 km2 for all BWAoperators

BAP Bandwidth capacity provided by each AP

KSys(S) System cost per subscriber

KT(S) Total cost per subscriber

Table 2: Output spectrum requirements.

Variable Description

SMSR Spectrum required to provide a minimum service rate

SMSC Spectrum required to minimize system cost

SMTC Spectrum required to minimize total system cost

4.6. Analysis outputs

The analysis can be summarized via the output variablesand output spectrum requirements in Tables 1 and 2. Thespectrum is the required spectrum according to each model.The number of APs is based on assuming an area of A= 1000 km2. This area is large compared to most citiesand small compared to most rural areas, but it provides acommon point of reference. The number of APs indicates thesystem infrastructure required. The spectrum and number ofAPs as computed in the previous section are per operator. Inorder to correctly reflect the total spectrum and number ofAPs, we need to incorporate the number of BWA operators.If S and N are the per operator requirements, then S/uoperator

and N/uoperator are the total requirements for all BWA users.The bandwidth capacity per AP indicates the bandwidthrequired to provide sufficient traffic capacity. It is alwaysat least BU . Though the models considered cost factors todifferent degrees, we compute the system and total costsfor each method. Cost per person is converted to cost persubscriber to give a better indication of what costs willbe from a network operator’s perspective. If K is a costper person, then K/( utakeup umrktshr uoperator) is the cost persubscriber. We reiterate that these costs consider networkcosts and do not include customer equipment and marketingcosts.

As a final comparison, we consider a startup systemmodel. The startup system model uses spectrum as deter-mined by the minimum service rate model, SMSR, andenough APs to provide coverage, that is, Nmin. This systemdoes not consider the user traffic. It is the lowest cost systemthat could be built and start to provide service. The costper subscriber is calculated as described above. However, thisis the cost per eventual subscriber since the startup systemwould need to invest in additional APs in order to haveenough capacity to carry these subscribers’ traffic.

4.7. Analysis summary

The interaction between the different models is seen inFigure 2. In Figure 2(a), the relationship between the min-





0.10.1 1 10 100








Minimum service rate (Mbps)

8 MHz











1 10 100 1000


22 MHz


Minimumsystem costupper limit

Minimumservice ratelower limit

Total spectrum (MHz)


Figure 2: Example derivation. The minimum service rate spectrumrequirements (a) sets a lower limit on the required spectrum(8 MHz). The “knee” in the system cost (b) sets the upper limit onthe usable spectrum (63 MHz). The minimum total cost determinesthe persubscriber cost and required spectrum ($900, 22 MHz).

imum bandwidth per user and the required spectrum isplotted. For a given minimum required user bandwidth (e.g.,1 Mbps), the minimum required spectrum is plotted (e.g.,8 MHz). In Figure 2(b) this sets a lower limit on the requiredspectrum. The minimum system cost sets an upper limit onthe usable spectrum (e.g., 63 MHz). The minimum of thetotal cost within this range sets the overall minimum cost andspectrum requirements (e.g., $900, 22 MHz).


This section describes the input variables used in Section 6.Many of the variables are based on the recent project toprovide a municipal wireless network in Philadelphia, USA[8, 11].

5.1. Spectrum efficiency factors

A number of wireless technologies are in place today forproviding BWA. Cellular technologies are also available.So-called CDMA 2000 and W-CDMA are third-generation

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Table 3: Modulation efficiency of several wireless technologies.

TechnologyChannel bandwidth Channel rate Efficiency

(MHz) (Mbps) (bps/Hz)

802.11b22 11 0.5

(WiFi) [13]

802.11a [13] 20 54 2.7

802.16 [12] 28 134 4.8

CDMA-20001.25 3.1 2.5

EVDO [23]

technologies with data rates in the few megabits per secondrange.

Table 3 lists the modulation efficiency of a few wirelesstechnologies including wireless LAN (802.11b, 802.11a),wireless MAN (802.16), and third generation cellular(CDMA-2000 EVDO release 0). Spectral efficiencies rangefrom 0.5 to about 5 bps/Hz [12, 13]. These efficienciesare best case efficiencies. For instance 802.11a can onlyachieve its highest rate within about 10 meters of the accesspoint, whereas it can achieve lower rates to significantlyfurther distances. To account for this we downgrade the bestavailable efficiency by 50% in emodulation.

These rates are so-called channel rates and do notinclude wireless protocol overhead, which reduces the usablecapacity. For instance, 802.11b has a maximum channel rateof 11 Mbps, while the maximum usable capacity is about3.5 Mbps. Overhead from other protocols (e.g., TCP/IP/LLC)can reduce capacity further to below this rate. In other words,the true capacity is about 30% of the channel rate [14].Similar overhead can be observed in other protocols.

Beyond protocol inefficiencies, Internet applications gen-erally perform better when the loading on the channel isbelow full capacity. As the load approaches capacity, queuingdelays can develop that degrade the performance. For real-time applications, such as voice, low delays are critical. Formore bursty applications such as Internet browsing, delaysare less critical. However, an average load below capacity isnecessary to avoid significant periods of congestion. Withsuch traffic, a high load, for example 50%, can result inacceptable performance. This loading is the average over thepeak busy hour. Typical wireless access networks have muchlower loading over the day [15, 16]. Nevertheless, busy-hourprovisioning is necessary to provide adequate service.

The maximum raw channel rates are best-case rates fordedicated spectrum. In shared or unlicensed environments,the available channel rates are below these maximum ratessince the lower rates are more robust to radio noise andinterference. The ratio of the lower rate used for the purposesof providing more robust coverage to the maximum rate isthe sharing efficiency, esharing. If dedicated spectrum is pro-vided to a single operator to provide BWA, then esharing = 1.We assume that the nonexclusive licenses are well organizedso that esharing = 1. Non-cooperative operators can chooseinterfering channels. Even if cooperating, different operatorsmay cover the same area multiple times using incompatiblechannel assignments. Besides other BWA operators, there

may be other services and applications that are not amenableto coordination. Because of these inefficiencies more robustmodulation is necessary. The 802.11 standards are designedto operate in unlicensed environments, while the 802.16standards are designed for unlicensed and licensed with themost efficient protocols designed for licensed. The maxi-mum current 802.11 efficiency (2.7 bps/Hz) is approximatelyhalf of the maximum 802.16 efficiency (4.8 bps/Hz). Theresulting sharing efficiency in shared unlicensed spectrum isesharing = 0.5.

The spectral efficiency above assumes that an operatorassigns different frequency channels to its nearby APs inorder to avoid interference. A simple strategy to achievethis is to divide the spectrum into subbands and assign thespectrum in a nonconflicting pattern. This pattern can berepeated over the coverage area so that channels are reusedmany times. This strategy is applied in cellular and wirelessLAN deployments. Cellular systems use a variety of reusepatterns depending on the technology. For instance, theentire spectrum is assigned to each AP in CDMA cellularsystems. This is traded against a lower net spectral efficiency.Since WLAN technologies are most similar to the BWAtechnologies, we will follow their reuse strategy, that is, areuse of three. Every AP would then have at most one thirdof the total spectrum available.

In this study, we will assume a radio technology similarto 802.16 that can utilize a variety of spectral bandwidths,has a modulation efficiency of about emodulation = 2.5 bps/Hz,a protocol efficiency of eprotocol = 0.30, and typically transmitsat one half of the maximum channel rate, eloading = 0.50. Inthe minimum service rate model, eloading = 1.00. Channelsare reused in a pattern of three channels, ereuse = 0.33.The sharing factor depends on whether channel access isunlicensed, esharing = 0.5, or licensed, esharing = 1.00.

5.2. Access point costs

The access point costs can be divided into (a) costs thatare independent of the coverage and total usable bandwidthper AP; (b) costs that depend on the coverage per AP;and (c) costs that depend on the usable bandwidth per AP.The model AP is based on the configuration to achievethe minimum number of APs (i.e., have the maximumcoverage). It consists of a broadband wireless radio; a setof 3 to 6 directional antennas either attached to an existingstructure or on a mast; additional radios as necessary forwireless backhaul; and connections to power. As the coveragedecreases, it is possible to use lower power and less expensiveamplifiers. As the user bandwidth per AP decreases, the APcan use fewer channels and fewer antennas to achieve itscapacity goal. This reduces the hardware and installationcost. For simplicity we assume that the NPV cost of an APis independent of these capacity and coverage factors. Forinstance, a rural AP will consist of a taller more expensivemast than an urban AP. However, the site costs in urbanenvironments are higher. Based on data from Philadelphia,the average installed cost of an AP is $5,000. The initial totalestimated capital cost in Philadelphia is $10 M, while the totalannual operating expenses are $8 M. If we assume these costs

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8 EURASIP Journal on Wireless Communications and Networking

are proportional to the number of AP, the annual operatingcosts per AP are 80% of the initial capital costs, or $4,000 perAP. Given a discount factor of 20%, this indicates that theNPV cost of each AP is KAP = $25,000.

5.3. Traffic per person

A BWA system might provide service to a variety of usersincluding residential, commercial, and municipal. The usersmight access the BWA system for communication, web-browsing, and media download applications. There maybe other embedded users including sensors, transactionprocessing devices (e.g., parking meters), security videocameras, and remotely controlled devices (e.g., sprinklers).For simplicity, we consider a single typical subscriber whichgenerates traffic at a rate of utraffic during the busy hour. Thistraffic is the total of uplink and downlink bandwidths sincethe capacity of many wireless protocols can be divided asneeded between up and down links. Separate up and downlink analysis is unnecessary. Applications such as voice overIP use 10’s of kilobits per second (kbps). Web browsingalternates between brief periods of high data rate downloadsand longer periods of viewing the content. Streaming videoor audio can be many 100’s of kbps. A remote video cameracan generate 300 kbps. These rates are growing over time.These observations suggest that an active user in the nearfuture could generate 100 kbps of traffic on average duringthe busy hour.

Users access the Internet at different times of the day.In any given busy hour, only a fraction of the users may beactively using the system. Internet access is a regular part ofmany users’ daily activity and as many as 50% of the usersmight be active during the busy hour.

Not every person in the population corresponds to a user.Some people will not be able to afford or will not have theneed of a broadband service. Household members mightshare the service. A household consists of 2.5 people onaverage, suggesting that the take up rate is at most 100/2.5= 40 lines per 100 people. The take up rate was 17 broadbandlines per 100 people at the beginning of 2006 and has beengrowing steadily [17]. We extrapolate that, in the near future,the take up rate will approach 25 broadband lines per 100people.

Given the set of broadband users, only a fraction will usea BWA service depending on the market share of the BWAservice provider. In rural areas, the primary competition toBWA will come from satellite service and existing WirelessISPs based on the 2.4 GHz unlicensed bands. Because ofbetter coverage and more bandwidth, we expect the BWAto have a competitive advantage over these alternativescapturing a majority of the broadband users. The marketshare in this case is 50%. In urban areas, there are additionalcompetitors such as DSL and Cable. These are alreadyentrenched. The BWA service will have lower market shareagainst these four competitors. The market share in this caseis 20%. This market share is for a single BWA operator.If nonexclusive licenses or unlicensed access regimes areused, then each BWA operator will enjoy half of this marketshare.

10000 100000 1000000 10000000 100000000








Figure 3: Normalized spectrum cost as a function of population forfull BTAs auctioned in the PCS broadband auction.

In this study, we assume an active user that generatesutraffic = 100 kbps in the busy hour. Half of these users areactive in the busy hour, uactive = 0.50 and a fraction of thepopulation that is a user, utakeup = 0.25. The market share willvary from umrktshare = 0.20 to umrktshare = 0.50 depending onthe regime. The operator fraction is uoperator = 1.00 for thelicensed exclusive regime and uoperator = 0.50 for the licensednonexclusive and unlicensed regimes.

We note the difference between our factors here andthe industry “over subscription factors.” A typical wirelessInternet service provider (WISP) will share an 11 Mbps linkbetween 100 users [18]. The over subscription factor of 100is based on implicit assumptions about the average trafficper user. In our model we make these assumptions explicit.To compete with a WISP, the BWA service provider mustprovide at least Mbps service to customers. We assume BU= 1 Mbps. This is the same target as in Philadelphia.

5.4. Spectrum cost

The cost of the spectrum can be estimated from recentFCC auctions. The PCS broadband auction was both recentand appropriate for a BWA service [19]. Figure 3 shows thenormalized cost (in $/MHz pop) as a function of the licensedbasic trading area (BTA) population (only includes full BTAsfor the full license size that actually were sold). Clearly, lesspopulated BTAs tend to have lower spectrum costs than morepopulated areas. If we use BTAs with populations less than100,000 to represent rural areas and BTAs with populationsmore than 1,000,000 people to represent urban areas, thenwe can estimate the relative spectrum cost. The averagenormalized cost for the rural areas is $0.21 and for urbanareas is $1.01, or approximately $0.2 and $1.0, respectively.

5.5. Transmission range

A BWA system requires a minimum number of APs toprovide sufficient signal to reach the intended coverage area.We assume frequencies are in the TV bands; the APs use highgain antennas; in urban areas the APs are not placed on hightowers, the subscriber equipment uses an outdoor antenna;and the transmit power is at least 1 W.

What kind of coverage can be expected under theseassumptions? Wireless links using 802.11 typically have

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T. X. Brown and D. C. Sicker 9

Table 4: Regime independent input variables used in the model.

Variable Value Description

emodulation 2.5 (bps/Hz) Modulation efficiency

ereuse 0.33 Frequency reuse factor

eprotocol 0.30 Protocol efficiency factor

eloading 0.50 Network loading factor

k f $5,000 Fixed cost of an AP

kom $4,000 Annual operations andmaintenance cost per AP

d 20% Discount factor for NPVcalculation

BU 1 Mbps Minimum data bandwidthper user

utraffic 100 kbps Traffic rate of active user inbusy hour

uactive 0.50 Fraction of users active inbusy hour

utakeup 0.25 Take up fraction forbroadband service

specified outdoor ranges of 100 m or more [20, 21]. Exper-iments have shown point-to-point links at distances of many10’s of kilometers under line-of-site conditions with high-gain antennas [21, 22]. Under more typical conditions withAPs placed on rooftops, the range can approach a fewkilometers. These data suggest that in urban areas high-gainantennas placed at modest heights should enable ranges up to500 m. In rural areas high towers and less urban clutter canenable transmission ranges of 10 km. We emphasize that therange limit is not purely a question of meeting the radio linkbudget. A CR-based operator will use shorter ranges thanpossible in order to avoid interference with TV receptionareas as described in the appendix. In any case, these rangesare only a direct factor for the minimum system cost model.For the other models, the number of access points is greaterthan Nmin and the transmission range is set by other factorsthan this minimum.

5.6. Model variables

The input variables that are independent of the regime aresummarized in Table 4. Five variables depend on the regime.They are summarized in Table 5.


Based on the input variables derived in the previous section,we apply the spectrum requirement and cost analysis toprovide some insights into the effect of each regime. Theoutput of the model is shown in Tables 6 and ?? and plottedin Figure 4.

6.1. Rural areas

Rural areas have the potential to go to low system cost persubscriber and exploit more than 300 MHz of bandwidthif unlicensed. Given the more than 100 MHz of existing

unlicensed spectrum at 900 MHz and 2.4 GHz, the additionof 100 MHz to 200 MHz can push the per subscriber costbelow $200 per subscriber.

If an exclusive license is used, then the total cost mustbe considered if the operator must pay for the license. Anexclusive license would allow an operator to have a totalcost around $250. About 80 MHz would be required toachieve that price. Many rural areas have this volume ofspectrum available. The nonexclusive license would requiremore spectrum and would have a total cost over $300,mainly because of the duplication of infrastructure impliedby having multiple operators.

In all scenarios, the effective per AP bandwidth sharedby subscribers would be 7 to 20 times the minimumrequirement of 1 Mbps. A startup system (Table 6) could bebuilt for less than $100 per eventual subscriber if licensed, butfurther investments would be needed to have the necessarycapacity.

6.2. Urban areas

In urban areas, an unlicensed approach requires more than100 MHz in order to have a price below $400 per subscriber.This much unlicensed spectrum already exists below 3 GHz.In New York City the available whitespace bandwidth is24 MHz. Going from the 110 MHz of existing spectrum tothe maximum useful spectrum of 127 MHz would yield a14% reduction in cost. This modest savings must be weighedagainst the added cognitive radio complexity to use thewhitespace bandwidth. This result follows from the relativelyshort range of each AP and the low market share. As a result,each AP can at best capture relatively few customers. Lackof bandwidth is not directly the constraint. Longer rangeAP could be used and that would increase the number ofcustomers captured per AP. However, only modest increasesare possible in urban areas such as New York before nochannel would be available (see Figure 1). The unlicensedspectrum here is similar to the 80 MHz of access spectrumused in the Philadelphia model. The cost per subscriberis higher than Philadelphia. In our sample model, we areassuming only a 20% market share for BWA split betweentwo operators. The Philadelphia model is more optimistic.For instance, if the market share is the same but the operatorshare rises to 100%, the required spectrum remains the same,but the system cost is half.

Licensing helps by reducing the required bandwidthto 22 MHz, an amount of white space available in manymarkets. However, the persubscriber total costs are at best$900 and unlikely to be viable. BWA via TV spectrum isa late comer to the urban broadband market. The lack ofviability follows from its likely low market share. As shownin Figure 5, it would require a market share of 65% of thebroadband market to drop below $500 per subscriber. Suchhigh market share is unlikely given the existing broadbandcompetitors. Even the startup system has a minimum costof around $250. Recall that the total cost as described heredoes not include additional costs such as the subscriberequipment and its installation.

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Table 5: Regime-dependent input variables.

RegimeSharing efficiency Spectrum cost

($/MHz-pop)Operator share Market share Density pp/km2 TX range

esharing KS uoperator umktshare D r

RuralUnlicensed 0.5 0 50%

50% 10 10 kmLicensed nonexclusive 1.0 0.2 50%

Licensed exclusive 1.0 0.2 100%

UrbanUnlicensed 0.5 0 50%

20% 4,000 500 mLicensed nonexclusive 1.0 1.0 50%

Licensed exclusive 1.0 1.0 100%

Table 6: Spectrum requirements and cost of a startup system.

Regime SMHz N per 1000 km2 Bap Mbps KSys $/sub KT $/sub

RuralUnlicensed 8 3 1 $127 $127

Licensed nonexclusive 4 3 1 $127 $140

Licensed exclusive 4 3 1 $64 $70

UrbanUnlicensed 8 1273 1 $318 $318

Licensed nonexclusive 4 1273 1 $318 $480

Licensed exclusive 4 1273 1 $159 $240

Licensed nonexclusive





Required spectrum (MHz)












0 100 200 300 400

Figure 4: Required spectrum and cost per subscriber in the sixregimes.


In this paper, we have presented a general analysis frameworkfor investigating the spectrum and cost issues associated withbuilding out a broadband wireless access network. Specif-ically, we have examined under what conditions cognitiveradios could be viable to provide broadband wireless access(BWA) in the licensed TV bands. We explored this issuealong demographic (urban, rural) and licensing (unlicensed,nonexclusive licensed, exclusive licensed) dimensions. Wedeveloped a general BWA efficiency and economic modelfor this analysis and derived parameters corresponding toeach of these regimes. The results indicate that in rural areasan unlicensed model is viable and the additional spectrumwould be useful despite existing unlicensed spectrum. A



0.40.7 1





Required spectrum (MHz)





Licensed exclusiveUnlicensedMarket share0.1

10 100 10000








Figure 5: Effect of market share on per subscriber cost and requiredspectrum in the urban area.

licensed model is also viable, although at a higher cost. In thedensest urban areas no model is economically viable. This isnot simple because there is less unused spectrum in urbanareas. Urban area cognitive radios are constrained to shortranges and many broadband alternatives already exist. As aresult either there is already sufficient unlicensed spectrum orthe cost per subscriber is prohibitive. An exclusive license isa better choice than nonexclusive licenses. It results in lowercost per subscriber and less required spectrum. The potentialfor monopoly behavior is unlikely, given the competitionfrom other broadband access technologies. These results arebased on one set of input variables for the model. The modelcan be easily manipulated to account for other scenarios ordifferent assumptions. These results provide useful input fora variety of spectrum policy issues.

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T. X. Brown and D. C. Sicker 11

Table 1: Output results in each regime.


Model SMHzN per Bap KSys KT $/sub

1000 km2 Mbps $/subMSR 16 125 1 $2,500 $2,500MSC 317 6 10 $127 $127MTC n/a n/a n/a n/a n/a


Model SMHzN per Bap KSys KT $/sub

1000 km2 Mbps $/subMSR 8 125 1 $2,500 $2,513MSC 159 6 10 $127 $381MTC 112 9 7 $180 $360


Model SMHzN per Bap KSys KT $/sub

1000 km2 Mbps $/subMSR 4 125 1 $2,500 $2,506MSC 159 3 20 $64 $318MTC 79 6 10 $127 $254


Model SMHzN per Bap KSys KT $/sub

1000 km2 Mbps $/subMSR 16 20000 1 $2,500 $2,500

UnlicensedMSC 127 2547 4 $318 $318MTC n/a n/a n/a n/a n/a


Model SMHzN per Bap KSys KT $/sub

1000 km2 Mbps $/subMSR 8 20000 1 $2,500 $2,662 LicensedMSC 63 2547 4 $318 $1,588 Non-ExclusiveMTC 32 5085 2 $636 $1,271


Model SMHzN per Bap KSys KT $/sub

1000 km2 Mbps $/subMSR 4 20000 1 $2,500 $2,581 LicensedMSC 63 1273 8 $159 $1,428 ExclusiveMTC 22 3596 3 $449 $899



The TV Whitespace was assessed for this paper using aFCC transmitter location database [24]. For each area ofstudy, a location was determined: 40 : 46 N, 73 : 58 Wfor New York City and 44 : 03 N, 99 : 11 W for BuffaloCounty, SD. Using the database, every TV transmitter wasidentified within 1000 km. For each transmitter, the databaseidentifies the distance to the Grade B signal contour (TheGrade B contour is a regulatory concept that corresponds tothe approximate range of a TV signal.). For channels whosecontour encompasses the location, the distance is zero. Foreach channel the closest signal contour is identified. SinceTV receivers are sensitive to both cochannel and adjacentchannel interference, for each channel the closest signalcontour on the same or adjacent channel is identified (notingthat some channels such as 13 and 14 are adjacent in numberbut not frequency). This distance is the interference distancefor that channel. For any given distance, d, n(d) counts thenumber of channels whose interference distance exceeds d.This function is plotted in Figure 1. This is not intendedto be a definitive assessment. It is not based on actualmeasurements and does not include existing secondary usessuch as wireless microphones. However, it does suggest therelative viability of cognitive radio use in rural and urbanenvironments.

How far must a BWA transmitter be from a TV receiverat the edge of its coverage area? A cognitive radio thatis transmitting at a maximum range d, can potentiallyinterfere with TV receivers that are much farther than d away.The interfering signal power at the Grade B contour mustbe less than approximately −100 dBm to avoid interferingwith TV reception [25]. Lognormal shadowing introduces

signal variability with a standard deviation of approximately5 dB. If we include two standard deviations of shadow fademargin, the required mean interfering signal power must beless than −110 dBm. The minimum receive threshold of atypical 802.11 radio at an intermediate rate is approximately−90 dBm. Freespace pathloss will introduce 20 dB of atten-uation for each factor of 10 increase in transmitter distance.In fact, long distant signals near the ground tend to suffera greater rate of attenuation so that this is conservative.Thus using an interference distance, d, 10 times largerthan the range of the BWA transmitter will conservativelyproduce interfering signals which are no more than−90 dBm(range of BWA transmitter) −20 dB (additional attenuationdue to factor of 10 distance further away) = −110 dBm asrequired.


The authors thank the useful conversations with DaleHatfield, Jim Snider, and Michael Calabrese. The work wassupported in part by NSF Grant CNS 0428887.


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[9] T. X. Brown, “An analysis of licensed channel avoidancestrategies for unlicensed devices,” in Proceedings of the 1stIEEE International Symposium on New Frontiers in DynamicSpectrum Access Networks (DySPAN ’05), Baltimore, Md, USA,November 2005.

[10] United States Census, US Census Bureau 2000.[11] D. L. Neff, V. C. Robinson, R. Bendis, et al., “Wire-

less Philadelphia business plan: wireless broadband asthe foundation for a digital city,” Wireless PhiladelphiaExecutive Committee, February 2005, http://www.wireless-philadelphia.org/organization.html.

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