seconday spectrum trading in tv white spaces get more insights from:

IEEE Communications Magazine • November 2012 1210163-6804/12/$25.00 © 2012 IEEE

INTRODUCTION

That more wireless capacity will be demanded inthe coming decades is not in question. Rather,the problem faced by policy makers, industrystakeholders, and wireless networking innovatorsis where the spectrum necessary to provide thatcapacity will come from. The spectrum of inter-est in this article is the so-called TV spectrum,that is, the spectrum lying between 470 and 862MHz. This spectrum is sometimes referred to asbeachfront property spectrum for its desirableproperties; it travels further and penetratesbuildings easily, qualities that are already exploit-

ed by TV broadcasters. Indeed, it is argued thatin the United States alone, the outright sale ofthis spectrum could have realized $100 billionfor the government in auction fees and generatea further $1 trillion from the resultant servicesthat would ensue [1].

With the transition from analog to digitaltelevision (DTV) transmission in Europe, spec-trum above 790 MHz has largely been cleared ofTV use. This so-called digital dividend (i.e. the800 MHz band between 790 MHz and 862 MHz)is being targeted for licensed cellular use, partic-ularly Long Term Evolution (LTE) systems.However, with only 72 MHz of spectrum, cellu-lar and broadband operators are unlikely to besatisfied; the question of how to access morelicensed spectrum in the TV frequencies isalready being asked [2].

The transition, which largely finished in theUnited States in 2009 and will finish in Europe bythe end of 2012, has also resulted in new kinds ofunused spectrum. Many of the DTV transmissionsystems that are now operational in the remainingspectrum, from 470 MHz to 790 MHz in Europe,are multifrequency systems employing high tower,high power network geometries. Due to the needto manage interference between these transmit-ters, the typical network plan creates large pock-ets of unused spectrum which is interleaved inboth frequency and space; a common term forthis spectrum is TV whitespaces (TVWS). ThisTVWS is complementary spectrum to the cleared800 MHz band, and could be readily exploited bynetworks with 700/800 MHz grids.

While the regulators’ primary goal is to pro-tect the DTV incumbents, their secondary goal isto ensure that the remaining TVWS is used effi-ciently. The last decade’s policy debates havefavored unlicensed use of this spectrum. TheU.S. Federal Communications Commission(FCC) and the UK’s Office of Communications(Ofcom), who can be considered the first moversin this regulatory area, have investigated the useof new cognitive radio technologies in this space.While the emerging consensus is for the use ofunlicensed, but geolocation database-controlled,cognitive radios in the TVWS, the possibility ofengaging in licensed uses has also been debated,if not approved thus far.

ABSTRACT

In this article, we discuss a spectrum tradingmechanism implemented by the spectrum brokerin TV whitespaces. TVWS are spectrum frequencybands unused by DTV, interleaved in both fre-quency and space. Underutilization of these bandsresults from the fact that the DTV transmissionsystems now operational in the spectrum from 470to 790 MHz are multifrequency systems employinghigh tower and high power network geometries,and must be managed for interference betweentransmitters. We motivate the use of a spectrumbroker, an entity that manages the TVWS sec-ondary spectrum market. Such a TVWS broker’sresponsibilities include planning the possiblebroad uses of the available spectrum in the TVWS;packaging the spectrum for short-term disposalthrough trading mechanisms; serving the broker’scustomers, with spectrum-leasing contracts; andacting as the port of call to handle interferencecaused by its customers to the primary DTV sys-tems or between its customers themselves. We dis-cuss the spectrum broker’s merchant and auctionmodes for spectrum trading. In the merchantmode, the base price is decided by the allocationprocedure, which considers various factors influ-encing the value of TVWS in a given place. In theauction mode, the customers’ demands and bidsdecide the final price of the spectrum. We discussthe auction design and show results of the spec-trum trading mechanisms, which have been suc-cessfully applied in a real-world test scenario inthe area of Munich, Germany.

COMMUNICATIONS NETWORK ECONOMICS

Hanna Bogucka and Marcin Parzy, Poznan University of Technology

Paulo Marques and Joseph W. Mwangoka, Instituto de Telecomunicações

Tim Forde, Trinity College Dublin

Secondary Spectrum Trading in TV White Spaces

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IEEE Communications Magazine • November 2012122

The process of licensing TVWS spectrum isseen as more complex than the licensing of fre-quency bands such as the 800 MHz spectrum,which covers large territories, has no incumbentusers, and can be sold off in clean, well definedparcels. Licenses in the TVWS would have todescribe rights at varying locations and varyingfrequencies, and deal with protection criteria fordifferent and difficult neighbors. It has beenargued that even in light of these perceived diffi-culties, the economic argument for purely unli-censed use of the TVWS does not stack up [3].

The concept presented in this article is based onthe premise that regulators do adopt clear, unam-biguous rules whic set out clearly the rights andresponsibilities of both the incumbent DTV multi-plex operators and any secondary systems that usethe TVWS on a licensed basis. Whatever marketuncertainty as to the demand for such spectrum,regulatory uncertainty is anathema to the emer-gence or continuance of any successful market. Thebones of such systems exist in both U.S. and EUlaw; the basic frameworks for secondary tradingand leasing systems have been legislated for. Fur-thermore, the emergence of technology and serviceneutral licensing is ideally suited to more complexlicensing situations such as the TVWS [4].

In order for a secondary trading system toemerge in the TVWS, an ecosystem that removesas many hurdles and uncertainties as possible fornew entrants and extant incumbents must beimplemented. Complex spectrum markets do notjust emerge, they must be nurtured. Facilitating,encouraging, and innovating a market in a space,and for a product, that did not previously existdemands that some entity engages with prospec-tive market players as an intermediary to assessand address their needs. While typically this hasbeen the remit of the regulator, the granularity atwhich such a process would occur in the TVWSwould be too fragmented and cumbersome forsuch bureaucracies. In this light, the concept offrequency coordinators has already been proposedfor use in situations that require more technicaland cumbersome engagement between the regula-tor and the potential spectrum users [5].

In this article we motivate the use of a spec-trum broker, an entity that builds on the conceptof frequency coordinators. Such a broker is morethan just a listing system or database; the U.S.company Spectrum Bridge acts as both an FCCTVWS database manager and an exchange forbilateral secondary trading of other spectrum.Rather, the TVWS broker’s responsibilitiesinclude planning the possible broad uses of theavailable spectrum in the TVWS; packaging thespectrum for short-term disposal through tradingmechanisms; serving the end users, who will bethe broker’s customers, with contracts coveringtheir leasing relationship; and finally, acting asfirst port of call to investigate and resolve inter-ference caused by its customers to the primaryDTV systems or between its customers them-selves. This is a dynamic and recurring processthat responds to changing technological needsand demands in the TVWS space. Note that justone spectrum broker is meant to make decisionsin a considered area (a country or a state or asmaller area), the one who handles the spectrummarket and allocates the resources to the play-

ers. It can operate with multiple databases; how-ever, the decision has to be made by one entityin this considered area. Allowing for multipledatabases to supply data to the broker eliminatesthe threat of TVWS data supply monopolization.

Below in this article, we are dealing withRegion 1 spectrum, which includes Europe,where the TVWS lay between 470–790 MHz(DVB-T channels 21 to 60). The allocations forDTV in Region 2 (including the United States)differs significantly, but the underlying processdescribed below remains the same: the brokermodel can be applied for different regulatorycontexts because the spectrum availability is pro-vided by the geolocation database. For instance,in Europe the DTV standard is DVB-T, whichuses 8 MHz per channel. The U.S. standardATSC uses 6 MHz per channel. This differencecan be accommodated easily by the geolocationdatabase, which is populated taking in considera-tion the regulatory context.

TVWS SPECTRUM BROKERAs discussed, the spectrum broker is a central-ized platform that facilitates TVWS spectrumtrading and its allocation to the interested play-ers, such as cellular operators, super-WiFi pro-viders, and machine-to-machine (M2M) serviceproviders. It can be a government (spectrumowner) controlled body or an independent thirdparty whose business is that of spectrum broker-ing. The players (spectrum buyers) are supposedto be able to make use of the spectrum in flexi-bly assigned TVWS frequency bands, whichmeans that their core network transceivers andmobile equipment can operate in multiple bands.The spectrum broker controls the manner inwhich the available resources are assigned toeach user in order to keep the desired quality ofservice (QoS) and interference below the inter-ference limits through appropriate mechanisms.The resources for sale in a given trading areaare the available (often fragmented) frequencybands, the allowable maximum transmit powerin these bands, and the time period for thelicensing that grants temporary exclusive rightsto use the spectrum. The operational goal of thebroker is to achieve robust technical protectionof the incumbent, QoS provisioning to the play-ers, and spectrum trading revenue maximization.Note that the spectrum is the national resourceand belongs to the society. Thus, this revenuemaximization should translate to social benefit.Moreover, trading of the temporary exclusiverights (short-term licenses) should naturallylower the prices and open the market to smallerplayers, which also has a social value. Theseaspects impose significant challenges and shapethe design choices of the broker system.

The spectrum broker and its position in thesecondary TVWS spectrum market scenario isillustrated in Fig. 1. Note that it handles only thelicensed use of the TVWS spectrum by tradingthe short-term licenses to the interested players,although unlicensed use of this spectrum is alsopossible, as shown in Fig. 1. A regulator shouldfind a balance in the spectrum partition to com-bine both approaches: unlicensed and licenseduse. One of the possible approaches is to handle

The operational goal

of the broker is to

achieve robust tech-

nical protection of

the incumbent, QoS

provisioning to the

players, and spec-

trum trading revenue

maximization. Note

that the spectrum is

the national resource

and belongs to the

society. Thus, this

revenue maximiza-

tion should translate

to social benefit.


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IEEE Communications Magazine • November 2012 123

vacant DTV channels that allow higher transmitpower by the broker for licensed applicationswith QoS requirements, while the vacant DTVchannels that allow lower transmit power go tothe unlicensed regime without guaranteed QoS.

The broker has the following functionalblocks:• TVWS context repositories• Dynamic TVWS allocation• Trading and price discovery• Registration and validationwhich are described below.

The TVWS context repositories obtain basicTVWS information from the geolocationdatabase. The broker then enhances the informa-tion by analyzing availability and usage patterns.Its database must also contain regulatory policiesfor the specification of secondary spectrum usagerights and obligations and prioritization of theTVWS access. Moreover, the repositories needto provide the minimum set of information thatparties to a spectrum trade must disclose, andapproaches to the protection of competition, e.g.spectrum aggregation caps that limit monopolis-tic entities and promote competition and univer-sal service requirements. Respective repositoriesstore the following information:• TVWS Occupancy Repository stores the cur-

rent status of secondary networks and real-time spectrum occupancy of TVWS.

• Trading Information Repository stores tradinginformation to maximize auction revenue,spectrum utilization and fairness such asreserve prices and transaction costs. Infor-mation about local spectrum demand can bealso stored and used to regulate the marketin the future for optimal spectrum usage.

• Policies Repository stores regulatory policiesfor the specification of spectrum usagerights, prioritization of TVWS access, spec-trum caps as well as cross-border policies.

The broker, through its trading mechanism andprice discovery, matches the players’ requirementswith available resources, and thus allocates theTVWS based on preset rules. The TVWS alloca-tion algorithm aims at the broker’s profit maxi-mization while avoiding spectrum fragmentation,provisioning the required QoS, and guaranteeingfairness in TVWS access. Having determined thebenchmark price of a given band, the broker cre-ates a spectrum portfolio for potential transac-tions. The portfolio recommends the bandwidthand power thresholds as well as geographic areasthat the band can be used. The portfolio is basedon spectrum context information analysis by thebroker to best match the needs of potential users.

The trading mechanism handles the spectrumtransactions between the broker and the cus-tomers (players) who buy spectrum. The mainfunction of the trading mechanism is price discov-ery. The broker aims at selling the spectrumrights to the most valuable user. The best way toachieve this is through auctioning. Besides discov-ering the “willingness to pay” price of the buyer,the broker needs to determine a benchmark priceto start the auction. This ensures profitability, andlimits the chances of collusion where buyers col-lude to lower the spectrum prices.

Tracing users of the broker’s service isachieved through a registration and validationmechanism. To support secure spectrum trading,a security framework is required to preventunauthorized spectrum access. The tracing ofusers is also important for conflict resolution.

Negotiation protocols enable the transactionof spectrum between the broker and the user totake place efficiently. Through these negotiationprotocols, the broker maximizes its revenue aswell as ensures fairness between players. Theinterfacing signaling between the broker and thespectrum user or market player forms the nego-tiation protocols.

Figure 1. Secondary TV white space spectrum market based on a spectrum broker.

Regulator

Geolocationspectrumdatabase

WSD + GPSWSD + GPS

Player N

Dynamic TVWS allocation

Broker

TV white space area

Negotiation protocols

Trading and price discovery

Registration and validation

Policiesrepository

TVWSoccupancyrepository

Tradinginformationrepository

Player 1

WSD

Unlicensed use ofTVWS (spectrum

commons)

The trading mecha-

nism handles the

spectrum transac-

tions between the

broker and the cus-

tomers (players) who

buy spectrum. The

main function of the

trading mechanism is

price discovery. The

broker aims at selling

the spectrum rights

to the most valuable

user. The best way

to achieve this is

through auctioning.


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Figure 2 shows the spectrum broker function-al diagram with three phases. Through its mainphase II (Operation) the spectrum broker sup-ports the merchant and auction modes for allo-cating spectrum. In the merchant mode, the baseprice is decided by the allocation procedurewhich considers various factors that influencethe value of TVWS in a given place. In the auc-tion mode, the auctioned band has a benchmarkprice, then each demand has an associated price(bid), and the winning bids decide the finalprice. In the merchant mode, the TV whitespaces are allocated on a first come first servebasis; whereas in the auction mode, the TVwhite spaces are allocated to the winning bid-ders. As shown in Fig. 2, when the spectrumdemand is higher than offer (supply), the auc-tion mode should be used for maximum eco-

nomic efficiency; otherwise, the merchant modeshould be used to allocate the TV white spaces.

OPPORTUNITY COST ANDRESERVE PRICE ESTIMATION

One of the key problems to overcome in devel-oping a broker is enabling it to estimate thereserve price for TVWS spectrum and possibly toidentify the opportunity cost of the TVWS spec-trum if the merchant mode is to be used. Oppor-tunity cost is defined as the highest valuealternative forgone. The opportunity cost of themarginal unit of a good or service in a marketequals the market-clearing price of an efficientmarket. In an efficient market, resources usageachieves optimality, and thus contributes to eco-

Figure 2. Spectrum broker functional diagram.

Acquisition of spectrumbands (TVWS pool)

Merchant mode

Geolocationdatabase

Start

Start

Phase II: Operation

Phas

e 1:

Pre

para

tion

and

ana

lysi

s

TVWS occupancyrepository

Update of TVWSoccupancy repository

Demand>offerNo Yes

Bids

Announce fixed price perMHz

Receive TVWS ordersfrom secondary users

Selection of optimal bidbased on some criteria

Auction mode

Announce the auction andthe minimum / call price

Receive and analyze thebids

Allocation of TVWS temporaryexclusive rights

Phase III: Maintenance

Spectrum tradingpolicies repositories

Estimation of demand

Acquisition of spectrum bands (TVWS pool)

Enhancement of TVWS pool

Benchmark price estimation based on AIP(including minimum price/call price for auction)

Spectrum portfolio proposal based on matchingalgorithm

Advertise the spectrum portfolio

Estimation of demand

Negotiation proto-

cols enable the

transaction of spec-

trum between the

broker and the user

to take place effi-

ciently. Through

these negotiation

protocols, the broker

maximizes its rev-

enue as well as

ensures fairness

between players.


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nomic growth. Therefore, pricing of the TVWSbased on opportunity cost gives diverse stake-holders incentives toward more economicallyefficient usage of the bands. The stakeholderscan use price information to choose the alterna-tives that enhance their economic value. If play-ers are faced with the opportunity cost ofspectrum, they will have incentives to increase/decrease their interest and spectrum use if theyvalue spectrum more/less than the opportunitycost [6]. In theory, current users would thereforebe willing to transfer rights to use spectrum if theopportunity costs of using spectrum, reflectedthrough administrative incentive pricing (AIP),are higher than the economic value to the user.

Different approaches can be used for imple-menting opportunity cost pricing within a bandbased on the objectives the broker seeks toachieve, which basically is to emulate the efficien-cy properties of a competitive market (auctions)[7]. When auctions are not used, the derivation ofopportunity costs can be achieved through marketvaluation or direct computation methods.

MARKET VALUATION METHODSOpportunity cost may be derived from marketdata in different ways. First, information on theprice of spectrum can be observed from auctionsor trades in secondary markets. Second, in acompany owning the spectrum as one of itsassets, the value of spectrum is simply the differ-ence between the company’s value and the valueof other assets. Lastly, from capacity sales ofspectrum-utilizing services (e.g., sale of digitalterrestrial TV multiplex capacity or sale of whole-sale capacity on a mobile network), the value ofspectrum would be the capacity price minus thevalue of other inputs. These approaches are quitestraightforward. For the first method, makingmeaningful comparison of frequency bands andmarket values in different geographic regions andtime frames is a nontrivial task. The last twomethods suffer from the requirements of poten-tially uncertain values of non-spectrum inputs.

DIRECT CALCULATION METHODSIn direct calculation methods, the broker acts as abidding company, and then uses the bidder’smethod of predicting prices to set spectrum price.These are standard net present value (NPV) andleast cost alternative (LCA) or optimal deprivalvalue method (ODV). In NPV, price could be setbased on the standard NPV modeling that firmsconduct, whereas LCA or ODV is the bid of anaverage bidder or bidders for multiple-use bands,and only requires the use of cost information. Inthis case, uncertain revenue projection (as inNPV) is not required. Using direct calculationmethods requires careful selection of input infor-mation. This includes equipment (e.g., whitespace devices) cost, equipment lifetime, andmaturity of the network, among others. The infor-mation has to be chosen such that the valuesobtained approximate market conditions.

PRACTICAL RESERVE PRICE ESTIMATIONEven though the TVWS can potentially beapplied to many different services in both non-interactive services such as broadcasting, andinteractive services, such as WiFi, WiMAX, and

LTE, the focus is placed on interactive services.As indicated above, the value of spectrumdepends on some estimation methods based onmarket data. However, currently, for the TVWS,there is a scarcity of data for estimation of thevalue of the TVWS. This is because, so far, whitespace equipment is still not widely available inthe market. For this reason, it is imperative touse some practical methods to project the spec-trum cost from other known usage scenarios intointended spectrum bands for the region of inter-est while considering constraining conditions.

The principle for the adjustment of thebenchmark price should enable the conversionof values from other regions, spectrum bands,and so on into the band of interest intuitively.However, actual usage will depend on other fac-tors that will be closely tied to the regulatoryregime. Figure 3 illustrates the proceduresinvolved in mapping the benchmark price intothe intended market scenario.

The method allows the modification of thebenchmark price with related modification coef-ficients to obtain the target price. Note that thebenchmark price, also called the standard price,is the price of benchmark band – a band thatcan easily be evaluated or for which the opportu-nity cost can easily be calculated. In that regard,the first step is to choose the reference spectrumfrom which to compute the benchmark price.

In secondary spectrum trading, the main func-tion of the pricing mechanism is to determine theprice of the spectrum reflecting its market value.In the case where there are not enough players inthe market to conduct an auction, spectrum canstill be directly traded through a merchant mecha-nism where the price is based on the opportunitycost. Furthermore, the benchmark price can be

Figure 3. The procedure for modifying the benchmark opportunity cost of spec-trum.

Use the modificationcoefficient model to

project the benchmarkprice of the target

spectrum

Spectrum price for a givenband, region, etc.

Based on the analyzedfactors, calculate the

modification coefficientof each factor

Analyze the various factorsaffecting the spectrum price

and establish the modificationsystem for spectrum price

accordingly.

Terrain, population, location,sharing/exclusive usage,

etc.

Start

Obtain or calculate thebenchmark price based

on the method ofopportunity cost

Techno-economic parametersto influence opportunity price

of spectrum


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used by the broker to set prices that maximize itsrevenue. The benchmark price determinationmethodology can also be used to set the initialprice in the competitive auction mode.

TVWS AUCTION DESIGNAs noted earlier, in cases when the spectrumdemand exceeds the spectrum supply, the auc-tion approach is the most effective technique.Auctions have been considered for long-termand short-term (online) spectrum trading anddiscussed extensively in many papers. A detailedcompendium on auctions can be found in [8],while a number of auction models for spectrumsharing have been described in [9]. There areseveral problems in spectrum auction design,which must be addressed if it is expected thatthis mechanism should provide effective and fairspectrum allocation to the players. First, the sizeof the market and the spectrum reserve price areeconomic aspects that must be considered. Inthe TVWS spectrum trading business scenario,mobile cellular (LTE) operators are often con-sidered to be the prime candidates to act asplayers interested in spectrum leasing to supporttheir subscribers’ demands, particularly in peaktraffic periods. There is a risk of collusionbetween the players that may affect the auctionresults. The solution for this threat is the mecha-nism of the auction (minimum) reserve price,which is estimated based on the AIP. Anotheraspect of the spectrum auction is the so-calledwinner’s curse, a tendency for the winning bid toexceed the intrinsic value of the purchased spec-trum bandwidth. The risk of such effect may beminimized by publishing the auction results forthe players to use them in their learning mecha-nisms for the future auctions.

The aspects associated with the amount ofspectrum granted to the players with temporarilyexclusive rights must be analyzed when designingthe auction. Spectrum caps rule restricts theamount of spectrum a player can (temporarily)hold in a particular geographic area. It may affectplayers’ demands by determining the maximumpercentage of the available TVWS spectrum thatcan be granted to each player in this area aftertaking into account the existing licensed spectrumalready held by each player. Such a mechanismattempts to avert market monopolization andensure some fairness in the resource distribution.The spectrum cap rule also results in some of thespectrum opportunities being reserved for newentrants to the secondary spectrum market. Notethat while the FCC removed specific spectrumcaps for auctions in 2003, it then introduced aspectrum screening process which it uses to evalu-ate holdings when companies merge with eachother or trade spectrum; these screens are con-ducted on a case-by-case basis whereby the FCCanalyzes the impact of changed spectrum holdingson the market in question [10]. Other regulatorsmaintain that caps are still necessary at the auc-tion stage to strike a balance between allowingexisting players to access more spectrum whilstalso space for new entrants. Both the U.K. Ofcom[11] and the Australian regulator [12] are impos-ing spectrum caps on their forthcoming auctionsof 700 MHz and 800 MHz digital dividend spec-

trum. Both regulators see that access to sub-1-GHz spectrum is critical to network operatorshaving the right blend of frequencies to providesustainable competitive services.

An important aspect of auction design, par-ticularly when it deals with multiple non-identi-cal objects, such as the fragmented spectrumresources with limited bandwidth, limited allow-able transmit power, and time availability, is tomatch these objects to the natural demands ofactual players, and to maximize the broker’sprofit. In our considered case of TVWS spec-trum auctions, the objects are 8 MHz DTV chan-nels with different maximum power levels andavailability time, while the considered players(mobile LTE operators) demand LTE channelsof various bandwidth (1.4, 3, 5, 10, or 20 MHz)associated with possible duplex modes (time-division duplex or frequency-division duplex, inwhich additionally a duplex frequency gap isrequired) with particular transmit power levelscharacteristic of base stations or mobile equip-ment. These demands may also be diverse forvarious considered time periods. Thus, the com-binatorial auction has to be conducted by thebroker to allocate the available resources to theplayers with bids indicating combinations of fre-quency and power demands. The combinationsof bids that maximize the broker’s revenue arethe winning ones. The question remains, howev-er, how the leasing time periods (time units con-sidered with a certain suitable resolution, e.g., ofa few hours within a day, or years in a decade)are treated. The simultaneous auction can beconducted for a number of leasing periods inparallel. In such a case, the auction results fordistinct leasing periods are independent and areannounced at the same moment for all theseperiods. It may cause the exposure problem; thatis, some players’ demands concerning the leasingperiods may be only partially satisfied [13]. Thesequential auction also treats the leasing perioddemands independently; however, the distinctauctions for each period are conducted sequen-tially, and the results are announced after eachauction, which allows the players to modify theirbids for the next period. Finally, the combinatori-al auction considers time demands of the playerstogether with their frequency and powerdemands, and matches the combinations of thesedemands to the available resources. This auctionis based on the all-or-nothing rule, that is, if thedemands of a player cannot be satisfied in allrequested leasing periods, the bid is rejectedfrom the broker’s optimization algorithm.

The problem of optimal determination of acombinatorial-auction solution is NP-complete.However, the number of players and availablewhite spaces in a real-world scenario is usuallynot high. The auction and related computationswill also not be executed too frequently (once aday or even less frequently). Finally, the branch-and-cut algorithm, a method of combinatorialoptimization for an integer linear programmingproblem, can be applied (as it has been in ourimplementation discussed in the next section),which lowers its complexity.

The time dimension of the license period isanother important aspect of the auction design.Long-term licenses (in years) bring stability and

In the case where

there are not

enough players in

the market to con-

duct an auction,

spectrum can still be

directly traded

through a merchant

mechanism where

the price is based on

the opportunity cost.

Furthermore, the

benchmark price can

be used by the bro-

ker to set prices that

maximize its

revenue.


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allow for long-term planning of the networkdevelopment for the spectrum buyers, whileshort-term licenses bring flexibility for the sec-ondary spectrum market, make the market moreopen to smaller players, and allow for the trans-fer of CAPEX into OPEX. It seems impracticalto conduct auctions for very short leasing-periods(e.g., a single connection time). Rather, the mini-mum considered period of time is a couple ofhours to cover at least the peak traffic period ina day. The short-term auction is going to beautomated (with the automated agents as play-ers), and repeated in many hundreds or thou-sands of independent TVWS areas for multipleleasing periods. Furthermore, a sealed-bid auc-tion, rather than an open auction, would seem tobe more advantageous in this case, as it limits thesignaling between the players and the broker.

REAL-WORLD TVWS SPECTRUMTRADING DEMONSTRATION

The TVWS secondary spectrum market mecha-nisms with the centralized spectrum brokerdescribed above have been considered within theEuropean COGEU project.1 Within this projectthe auction design and spectrum-trading consid-erations described above have been successfullyapplied in the real-world test scenario. Figure 4illustrates the COGEU spectrum trading demon-strator with the main building blocks and actors.In the tested scenario, the geolocation databaseconsists of the TVWS maps for the Munich area.These maps have been elaborated based on fieldmeasurements and advanced propagation-modelcalculations for the LTE systems and containspatial information about DVB-T channel avail-

ability for different transmit power requirements(Fig. 5). After contacting the database throughthe web-based link, the broker presents the spec-trum portfolio to the players as described earlier.A number of auction participants have beenengaged to test the spectrum trading algorithm.The auction winner is entitled to activate a Mas-ter and transmit an orthogonal frequency-divisionmultiplexing (OFDM) signal (video streaming)over the assigned TV channels, as shown in Fig.4. This signal must be successfully received by theSlave with required QoS while no harmful inter-ference has been measured in the DVB-T com-mercial receiver operating in the neighboringDVB-T channel. Initial measurements have con-firmed successful field trials of such LTE trans-mission over TVWS in the tested area.

In the considered system evaluation setup, theleasing time granularity is eight hours in a day. Theeight-hour periods reflect typical daily variations ofcellular systems telecommunication traffic. Alloca-tion of spectrum with temporary exclusive rights iscleared, and the auction is repeated every day.However, this adopted time granularity is scalable.In order to bring predictability to the operators’business models, one-year licenses for specific sitescan be offered. The sealed-bid first-price time-simultaneous and combinatorial auctions havebeen tested and their results analyzed. Four sce-narios of frequency resources availability havebeen identified: two blocks of continuous availablebandwidth of 24 MHz and 16 MHz, respectively(R1); 24 MHz and 8 MHz (R2); 16 MHz and 16MHz (R3); and 16 MHz and 8 MHz (R4) with thesame power constraint that allows for the typicalLTE downlink transmission. A number of sets ofplayers (LTE operators) have been allowed to par-ticipate in the auctions requesting combinations of

1 COGEU is a European7th Framework Programproject (Cognitive RadioSystems for EfficientSharing of TV WhiteSpaces in the EuropeanContext).

Figure 4. Real-world spectrum trading demonstration scenario.

Internet

Player 1 Player 2Spectrum buyers

Regulator

PMSE

Player N

GUIMunichTVWSmaps

SLAVE(secondary)

MASTER(secondary)

WSDWSD

COGEU broker

TVWS BTS

DVB-T receiver(primary)

DVB-T transmitter(primary)

GPS

Broker profitSpectrum policies operationQoS of secondary linkTVWS usage rateInterference evaluationGeo-location database validation

Long-term licenses

(in years) bring stabil-

ity and allow for

long-term planning

of the network

development for the

spectrum buyers,

while short-term

licenses bring flexibil-

ity for the secondary

spectrum market,

make the market

more open to

smaller players, and

allow for the

transfer of CAPEX

into OPEX.


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LTE downlink channels of 20 MHz, 10 MHz, and5 MHz. Here, we present example results for a setof players consisting of four players requesting 10MHz each and four players requesting 5 MHzeach. The probability of accessing the spectrumauction and requesting a particular LTE channelbandwidth has been modeled by the uniform distri-bution with the probability equal to 65 and 85 per-cent for 10 and 5 MHz, respectively. Theprobability of participation in auction for thedefined low, medium, and peak traffic periods hasbeen scaled appropriately as per the daily trafficintensity in cellular networks.

The auction efficiency (i.e., the ratio of the spec-trum bandwidth sold to the available spectrum onthe market) is presented in Fig. 6a. The results con-firm that both types of auction, simultaneous andcombinatorial, are efficient in selling the spectrum,although there are some differences. In peak hoursthe auctions’ efficiencies are the same, but for otherconsidered time periods, the average spectrum uti-lization is higher for the simultaneous auction,because the allocation rule allows for the satisfactionof the players’ demands concerning the time periodsonly partially. The combinatorial auction, on theother hand, is based on the all-or-nothing rule dis-cussed in the previous section. This also has animpact on the players’ satisfaction rates, presented inFig. 6b. The average satisfaction of the players

demanding either 10 or 5 MHz is higher in thesimultaneous auction, but in this auction it is definedin a different way than for the combinatorial auction.A user in the simultaneous auction is satisfied to thedegree his or her frequency and power demands areallocated in the winning set of bids for separate leas-ing periods. In the combinatorial auction, a user issatisfied only if all his or her requests are met.

Finally, some preliminary results have alsobeen obtained in the spectrum value estimation.The spectrum value ranges from approximately€25 (in low populated areas in a low traffic peri-od) to more than €300 (in highly populatedareas and in peak traffic hours) for 1 MHz, 1 kmradius of the considered area, 30 dBm of thetransmit power limit, and 8 h allocation periodin a day, assuming one-year stability in licenseallocation in Germany.

CONCLUSIONSThe secondary spectrum market in TVWS has thepotential to support wireless communication ser-vices of multiple players, including mobile commu-nication operators with continuously increasingspectrum demands. We have proposed a spectrumbroker capable of allocating the spectrum for short-term disposal through trading mechanisms. Thesemechanisms — access to the geolocation TVWS

Figure 5. COGEU online framework for the Munich scenario, Show White Spaces tool (maximum allowed transmit power by a TVWSdevice in Channel 59), available at http://projectos.est.ipcb.pt/cogeu2/


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spectrum database, handling of the spectrum repos-itories and trading policies, the merchant mode ofoperation, and auctions — have been discussed.The real-world demonstration of the spectrum bro-ker and the geolocation database in Munich, Ger-many, show the potential of maintaining the TVWSdatabases, the efficiency of the proposed marketmechanisms, and high performance of the LTEtransmission in the opportunistically accessedTVWS with primary DTV service protection.

ACKNOWLEDGEMENTSThe study presented in this article has been sup-ported by the European Commission, SeventhFramework Program, under the project COGEU(contract no. ICT-248560).

REFERENCES[1] T. W. Hazlett, “Unleashing the DTV Band: A Proposal

for an Overlay Auction,” submission to FCC #26, “ANational Broadband Plan for Our Future,” Dec. 2009.

[2] (Provisional) Final Acts — ITU-R, World Radiocommuni-cation Conf. 2012, Feb. 2012.

[3] C. Bazelon, “Licensed or Unlicensed: The Economic Con-siderations in Incremental Spectrum Allocations,” IEEECommun. Mag., Mar. 2009, vol. 47, no. 3, pp. 110–16.

[4] W. Webb, “An Optimal Way to License the Radio Spec-trum,” Telecommun. Policy, vol. 33, no. 3–4, 2009, pp.230–37.

[5] C. Bazelon, “Next Generation Frequency Coordinator,”Telecommunications Policy, vol. 27, no. 7, 2003, pp.517–25.

[6] P. Crocioni, “Is Allowing Trading Enough? Making Sec-ondary Markets in Spectrum Work,” Telecommun. Poli-cy, vol. 33, no. 8, Sept. 2009, pp. 451–68.

[7] Plum Consulting and Aegis Systems, “AdministrativeIncentive Pricing of Radiofrequency Spectrum,” reportfor the Australian Communications and Media Authori-ty, Oct. 2008.

[8] Y. Shoham and K. Leyton-Brown, Multi-Agent Systems:Algorithmic, Game-Theoretic, and Logical Foundations,Cambridge Univ. Press, 2009.

[9] A. M. Wyglinski, M. Nekovee, and Y. T. Hou, CognitiveRadio Communications and Networks: Principles andPractice (Ch. 17: Auction-Based Spectrum Markets inCognitive Radio Networks), Elsevier, Dec. 2009.

[10] M. Goldstein, “Enhanced Data Collection Could HelpFCC Better Monitor Competition in the Wireless Indus-try,” US Government Accounting Office-10-779, July27, 2010.

[11] Ofcom, “Second Consultation on Assessment of FutureMobile Competition and Proposals for the Award of800 MHz and 2.6 GHz Spectrum and Related Issues,”Jan. 2012.

[12] Australian Government Radiocommunications (Spec-trum License Limits) Direction No. 1 of 2012(F2012L00205 ), Feb. 2012.

[13] M. Parzy and H. Bogucka, “Non-Identical Objects Auc-tion for Spectrum Sharing in TV White Spaces – ThePerspective of Service Providers as Secondary Users,”IEEE DySpAN 2011, 3–6 May, 2011, Aachen, Germany.

BIOGRAPHIESHANNA BOGUCKA ([email protected]) receivedM.Sc. and Ph.D. degrees in telecommunications from Poz-nan University of Technology (PUT), Poland, in 1988 and1995, respectively. Since 1988 she has been employed atPUT, currently in the Chair of Wireless Communications asa professor and deputy dean for research on the Faculty ofElectronics and Telecommunication. She has research inter-ests in the area of wireless communications, and flexible,adaptive, and cognitive radio systems. She is the author ofmore than 100 papers and three handbooks (in Polish) inthe area of radio communications.

PAULO MARQUES received his Ph.D. from the University ofAveiro-Portugal in 2006. He is a senior researcher at theInstituto de Telecomunicações and a professor at CasteloBranco Polytechnic Institute. He is the scientific coordinatorof the European research project FP7 COGEU. His research

interests include the IEEE P1900.6 cognitive radio standard.

TIMOTHY K. FORDE received his Ph.D. degree, which focusedon wireless ad hoc networks, from the University of Dublin,Trinity College, in 2005. He works at CTVR—The Telecom-munications Research Center based at Trinity College,Dublin. His research interests include innovative spectrumaccess regimes, focusing on the economic policy and tech-nical challenges of RF spectrum reform.

JOSEPH W. MWANGOKA received his Ph.D. degree fromTsinghua University, Beijing, China in 2009. Until 2012 hewas a researcher at the Instituto de Telecomunicações,Aveiro, Portugal. His research interests include wirelesscommunications, cognitive radio technology and dynamicspectrum management. He has co-authored a number ofbook chapters and papers on cognitive radio and the useof TVWS.

MARCIN PARZY received an M.Sc. degree in telecommunica-tions from Poznan University of Technology (PUT), Poland,in 2007. In the years 2007–2009 he worked as a radioplanning engineer of GSM and UMTS in Orange, Poland.Since 2009 he is a Ph.D. student at PUT in the Chair ofWireless Communications. He has research interests in theareas of wireless communications, game theory, resourceallocation, spectrum auctions, and cognitive radio systems.

Figure 6. Results of the spectrum simultaneous (SA) and combinatorial (CA)auctions – auction efficiency in a) spectrum selling and b) users’ satisfactionrates.

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