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  • 1. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® COMMUNICATIONS NETWORK ECONOMICS Secondary Spectrum Trading in TV White Spaces Hanna Bogucka and Marcin Parzy, Poznan University of Technology Paulo Marques and Joseph W. Mwangoka, Instituto de Telecomunicações Tim Forde, Trinity College Dublin ABSTRACT ed by TV broadcasters. Indeed, it is argued that in the United States alone, the outright sale of In this article, we discuss a spectrum trading this spectrum could have realized $100 billion mechanism implemented by the spectrum broker for the government in auction fees and generate in TV whitespaces. TVWS are spectrum frequency a further $1 trillion from the resultant services bands unused by DTV, interleaved in both fre- that would ensue [1]. quency and space. Underutilization of these bands With the transition from analog to digital results from the fact that the DTV transmission television (DTV) transmission in Europe, spec- systems now operational in the spectrum from 470 trum above 790 MHz has largely been cleared of to 790 MHz are multifrequency systems employing TV use. This so-called digital dividend (i.e. the high tower and high power network geometries, 800 MHz band between 790 MHz and 862 MHz) and must be managed for interference between is being targeted for licensed cellular use, partic- transmitters. We motivate the use of a spectrum ularly Long Term Evolution (LTE) systems. broker, an entity that manages the TVWS sec- However, with only 72 MHz of spectrum, cellu- ondary spectrum market. Such a TVWS broker’s lar and broadband operators are unlikely to be responsibilities include planning the possible satisfied; the question of how to access more broad uses of the available spectrum in the TVWS; licensed spectrum in the TV frequencies is packaging the spectrum for short-term disposal already being asked [2]. through trading mechanisms; serving the broker’s The transition, which largely finished in the customers, with spectrum-leasing contracts; and United States in 2009 and will finish in Europe by acting as the port of call to handle interference the end of 2012, has also resulted in new kinds of caused by its customers to the primary DTV sys- unused spectrum. Many of the DTV transmission tems or between its customers themselves. We dis- systems that are now operational in the remaining cuss the spectrum broker’s merchant and auction spectrum, from 470 MHz to 790 MHz in Europe, modes for spectrum trading. In the merchant are multifrequency systems employing high tower, mode, the base price is decided by the allocation high power network geometries. Due to the need procedure, which considers various factors influ- to manage interference between these transmit- encing the value of TVWS in a given place. In the ters, the typical network plan creates large pock- auction mode, the customers’ demands and bids ets of unused spectrum which is interleaved in decide the final price of the spectrum. We discuss both frequency and space; a common term for the auction design and show results of the spec- this spectrum is TV whitespaces (TVWS). This trum trading mechanisms, which have been suc- TVWS is complementary spectrum to the cleared cessfully applied in a real-world test scenario in 800 MHz band, and could be readily exploited by the area of Munich, Germany. networks with 700/800 MHz grids. While the regulators’ primary goal is to pro- INTRODUCTION tect the DTV incumbents, their secondary goal is to ensure that the remaining TVWS is used effi- That more wireless capacity will be demanded in ciently. The last decade’s policy debates have the coming decades is not in question. Rather, favored unlicensed use of this spectrum. The the problem faced by policy makers, industry U.S. Federal Communications Commission stakeholders, and wireless networking innovators (FCC) and the UK’s Office of Communications is where the spectrum necessary to provide that (Ofcom), who can be considered the first movers capacity will come from. The spectrum of inter- in this regulatory area, have investigated the use est in this article is the so-called TV spectrum, of new cognitive radio technologies in this space. that is, the spectrum lying between 470 and 862 While the emerging consensus is for the use of MHz. This spectrum is sometimes referred to as unlicensed, but geolocation database-controlled, beachfront property spectrum for its desirable cognitive radios in the TVWS, the possibility of properties; it travels further and penetrates engaging in licensed uses has also been debated, buildings easily, qualities that are already exploit- if not approved thus far. IEEE Communications Magazine • November 2012 0163-6804/12/$25.00 © 2012 IEEE 121C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 2. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® The process of licensing TVWS spectrum is ers. It can operate with multiple databases; how- The operational goal seen as more complex than the licensing of fre- ever, the decision has to be made by one entity quency bands such as the 800 MHz spectrum, in this considered area. Allowing for multiple of the broker is to which covers large territories, has no incumbent databases to supply data to the broker eliminates achieve robust tech- users, and can be sold off in clean, well defined the threat of TVWS data supply monopolization. nical protection of parcels. Licenses in the TVWS would have to Below in this article, we are dealing with describe rights at varying locations and varying Region 1 spectrum, which includes Europe, the incumbent, QoS frequencies, and deal with protection criteria for where the TVWS lay between 470–790 MHz provisioning to the different and difficult neighbors. It has been (DVB-T channels 21 to 60). The allocations for argued that even in light of these perceived diffi- DTV in Region 2 (including the United States) players, and spec- culties, the economic argument for purely unli- differs significantly, but the underlying process trum trading revenue censed use of the TVWS does not stack up [3]. described below remains the same: the broker maximization. Note The concept presented in this article is based on model can be applied for different regulatory the premise that regulators do adopt clear, unam- contexts because the spectrum availability is pro- that the spectrum is biguous rules whic set out clearly the rights and vided by the geolocation database. For instance, the national resource responsibilities of both the incumbent DTV multi- in Europe the DTV standard is DVB-T, which plex operators and any secondary systems that use uses 8 MHz per channel. The U.S. standard and belongs to the the TVWS on a licensed basis. Whatever market ATSC uses 6 MHz per channel. This difference society. Thus, this uncertainty as to the demand for such spectrum, can be accommodated easily by the geolocation revenue maximiza- regulatory uncertainty is anathema to the emer- database, which is populated taking in considera- gence or continuance of any successful market. The tion the regulatory context. tion should translate bones of such systems exist in both U.S. and EU law; the basic frameworks for secondary trading to social benefit. and leasing systems have been legislated for. Fur- TVWS SPECTRUM BROKER thermore, the emergence of technology and service As discussed, the spectrum broker is a central- neutral licensing is ideally suited to more complex ized platform that facilitates TVWS spectrum licensing situations such as the TVWS [4]. trading and its allocation to the interested play- In order for a secondary trading system to ers, such as cellular operators, super-WiFi pro- emerge in the TVWS, an ecosystem that removes viders, and machine-to-machine (M2M) service as many hurdles and uncertainties as possible for providers. It can be a government (spectrum new entrants and extant incumbents must be owner) controlled body or an independent third implemented. Complex spectrum markets do not party whose business is that of spectrum broker- just emerge, they must be nurtured. Facilitating, ing. The players (spectrum buyers) are supposed encouraging, and innovating a market in a space, to be able to make use of the spectrum in flexi- and for a product, that did not previously exist bly assigned TVWS frequency bands, which demands that some entity engages with prospec- means that their core network transceivers and tive market players as an intermediary to assess mobile equipment can operate in multiple bands. and address their needs. While typically this has The spectrum broker controls the manner in been the remit of the regulator, the granularity at which the available resources are assigned to which such a process would occur in the TVWS each user in order to keep the desired quality of would be too fragmented and cumbersome for service (QoS) and interference below the inter- such bureaucracies. In this light, the concept of ference limits through appropriate mechanisms. frequency coordinators has already been proposed The resources for sale in a given trading area for use in situations that require more technical are the available (often fragmented) frequency and cumbersome engagement between the regula- bands, the allowable maximum transmit power tor and the potential spectrum users [5]. in these bands, and the time period for the In this article we motivate the use of a spec- licensing that grants temporary exclusive rights trum broker, an entity that builds on the concept to use the spectrum. The operational goal of the of frequency coordinators. Such a broker is more broker is to achieve robust technical protection than just a listing system or database; the U.S. of the incumbent, QoS provisioning to the play- company Spectrum Bridge acts as both an FCC ers, and spectrum trading revenue maximization. TVWS database manager and an exchange for Note that the spectrum is the national resource bilateral secondary trading of other spectrum. and belongs to the society. Thus, this revenue Rather, the TVWS broker’s responsibilities maximization should translate to social benefit. include planning the possible broad uses of the Moreover, trading of the temporary exclusive available spectrum in the TVWS; packaging the rights (short-term licenses) should naturally spectrum for short-term disposal through trading lower the prices and open the market to smaller mechanisms; serving the end users, who will be players, which also has a social value. These the broker’s customers, with contracts covering aspects impose significant challenges and shape their leasing relationship; and finally, acting as the design choices of the broker system. first port of call to investigate and resolve inter- The spectrum broker and its position in the ference caused by its customers to the primary secondary TVWS spectrum market scenario is DTV systems or between its customers them- illustrated in Fig. 1. Note that it handles only the selves. This is a dynamic and recurring process licensed use of the TVWS spectrum by trading that responds to changing technological needs the short-term licenses to the interested players, and demands in the TVWS space. Note that just although unlicensed use of this spectrum is also one spectrum broker is meant to make decisions possible, as shown in Fig. 1. A regulator should in a considered area (a country or a state or a find a balance in the spectrum partition to com- smaller area), the one who handles the spectrum bine both approaches: unlicensed and licensed market and allocates the resources to the play- use. One of the possible approaches is to handle 122 IEEE Communications Magazine • November 2012C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 3. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® Broker The trading mecha- TVWS nism handles the occupancy Dynamic TVWS allocation repository Policies spectrum transac- repository tions between the Trading Trading and price discovery information broker and the cus- repository WSD Geolocation tomers (players) who spectrum database Registration and validation buy spectrum. The Negotiation protocols main function of the Unlicensed use of trading mechanism is TVWS (spectrum Regulator Player 1 Player N commons) price discovery. The broker aims at selling the spectrum rights to the most valuable user. The best way to achieve this is TV white space area WSD + GPS WSD + GPS through auctioning. Figure 1. Secondary TV white space spectrum market based on a spectrum broker. vacant DTV channels that allow higher transmit The broker, through its trading mechanism and power by the broker for licensed applications price discovery, matches the players’ requirements with QoS requirements, while the vacant DTV with available resources, and thus allocates the channels that allow lower transmit power go to TVWS based on preset rules. The TVWS alloca- the unlicensed regime without guaranteed QoS. tion algorithm aims at the broker’s profit maxi- The broker has the following functional mization while avoiding spectrum fragmentation, blocks: provisioning the required QoS, and guaranteeing • TVWS context repositories fairness in TVWS access. Having determined the • Dynamic TVWS allocation benchmark price of a given band, the broker cre- • Trading and price discovery ates a spectrum portfolio for potential transac- • Registration and validation tions. The portfolio recommends the bandwidth which are described below. and power thresholds as well as geographic areas The TVWS context repositories obtain basic that the band can be used. The portfolio is based TVWS information from the geolocation on spectrum context information analysis by the database. The broker then enhances the informa- broker to best match the needs of potential users. tion by analyzing availability and usage patterns. The trading mechanism handles the spectrum Its database must also contain regulatory policies transactions between the broker and the cus- for the specification of secondary spectrum usage tomers (players) who buy spectrum. The main rights and obligations and prioritization of the function of the trading mechanism is price discov- TVWS access. Moreover, the repositories need ery. The broker aims at selling the spectrum to provide the minimum set of information that rights to the most valuable user. The best way to parties to a spectrum trade must disclose, and achieve this is through auctioning. Besides discov- approaches to the protection of competition, e.g. ering the “willingness to pay” price of the buyer, spectrum aggregation caps that limit monopolis- the broker needs to determine a benchmark price tic entities and promote competition and univer- to start the auction. This ensures profitability, and sal service requirements. Respective repositories limits the chances of collusion where buyers col- store the following information: lude to lower the spectrum prices. • TVWS Occupancy Repository stores the cur- Tracing users of the broker’s service is rent status of secondary networks and real- achieved through a registration and validation time spectrum occupancy of TVWS. mechanism. To support secure spectrum trading, • Trading Information Repository stores trading a security framework is required to prevent information to maximize auction revenue, unauthorized spectrum access. The tracing of spectrum utilization and fairness such as users is also important for conflict resolution. reserve prices and transaction costs. Infor- Negotiation protocols enable the transaction mation about local spectrum demand can be of spectrum between the broker and the user to also stored and used to regulate the market take place efficiently. Through these negotiation in the future for optimal spectrum usage. protocols, the broker maximizes its revenue as • Policies Repository stores regulatory policies well as ensures fairness between players. The for the specification of spectrum usage interfacing signaling between the broker and the rights, prioritization of TVWS access, spec- spectrum user or market player forms the nego- trum caps as well as cross-border policies. tiation protocols. IEEE Communications Magazine • November 2012 123C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 4. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® Negotiation proto- Start cols enable the transaction of spec- Acquisition of spectrum bands (TVWS pool) trum between the Geolocation Phase 1: Preparation and analysis broker and the user database Enhancement of TVWS pool to take place effi- Phase III: Maintenance ciently. Through Estimation of demand these negotiation protocols, the broker Benchmark price estimation based on AIP TVWS occupancy maximizes its rev- (including minimum price/call price for auction) repository enue as well as ensures fairness Spectrum portfolio proposal based on matching between players. algorithm Update of TVWS occupancy repository Estimation of demand Advertise the spectrum portfolio No Yes Demand>offer Spectrum trading policies repositories Announce fixed price per Announce the auction and MHz the minimum / call price Bids Receive TVWS orders Receive and analyze the from secondary users bids Acquisition of spectrum Selection of optimal bid bands (TVWS pool) based on some criteria Merchant mode Auction mode Allocation of TVWS temporary exclusive rights Phase II: Operation Start Figure 2. Spectrum broker functional diagram. Figure 2 shows the spectrum broker function- nomic efficiency; otherwise, the merchant mode al diagram with three phases. Through its main should be used to allocate the TV white spaces. phase II (Operation) the spectrum broker sup- ports the merchant and auction modes for allo- cating spectrum. In the merchant mode, the base OPPORTUNITY COST AND price is decided by the allocation procedure which considers various factors that influence RESERVE PRICE ESTIMATION the value of TVWS in a given place. In the auc- One of the key problems to overcome in devel- tion mode, the auctioned band has a benchmark oping a broker is enabling it to estimate the price, then each demand has an associated price reserve price for TVWS spectrum and possibly to (bid), and the winning bids decide the final identify the opportunity cost of the TVWS spec- price. In the merchant mode, the TV white trum if the merchant mode is to be used. Oppor- spaces are allocated on a first come first serve tunity cost is defined as the highest value basis; whereas in the auction mode, the TV alternative forgone. The opportunity cost of the white spaces are allocated to the winning bid- marginal unit of a good or service in a market ders. As shown in Fig. 2, when the spectrum equals the market-clearing price of an efficient demand is higher than offer (supply), the auc- market. In an efficient market, resources usage tion mode should be used for maximum eco- achieves optimality, and thus contributes to eco- 124 IEEE Communications Magazine • November 2012C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 5. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® nomic growth. Therefore, pricing of the TVWS based on opportunity cost gives diverse stake- Start holders incentives toward more economically efficient usage of the bands. The stakeholders can use price information to choose the alterna- Obtain or calculate the Techno-economic parameters tives that enhance their economic value. If play- benchmark price based to influence opportunity price ers are faced with the opportunity cost of on the method of of spectrum spectrum, they will have incentives to increase/ opportunity cost decrease their interest and spectrum use if they value spectrum more/less than the opportunity cost [6]. In theory, current users would therefore Analyze the various factors be willing to transfer rights to use spectrum if the Terrain, population, location, affecting the spectrum price sharing/exclusive usage, opportunity costs of using spectrum, reflected and establish the modification etc. through administrative incentive pricing (AIP), system for spectrum price accordingly. are higher than the economic value to the user. Different approaches can be used for imple- menting opportunity cost pricing within a band based on the objectives the broker seeks to Based on the analyzed factors, calculate the achieve, which basically is to emulate the efficien- modification coefficient cy properties of a competitive market (auctions) of each factor [7]. When auctions are not used, the derivation of opportunity costs can be achieved through market valuation or direct computation methods. Use the modification coefficient model to Spectrum price for a given MARKET VALUATION METHODS project the benchmark band, region, etc. price of the target Opportunity cost may be derived from market spectrum data in different ways. First, information on the price of spectrum can be observed from auctions or trades in secondary markets. Second, in a Figure 3. The procedure for modifying the benchmark opportunity cost of spec- company owning the spectrum as one of its trum. assets, the value of spectrum is simply the differ- ence between the company’s value and the value of other assets. Lastly, from capacity sales of LTE, the focus is placed on interactive services. spectrum-utilizing services (e.g., sale of digital As indicated above, the value of spectrum terrestrial TV multiplex capacity or sale of whole- depends on some estimation methods based on sale capacity on a mobile network), the value of market data. However, currently, for the TVWS, spectrum would be the capacity price minus the there is a scarcity of data for estimation of the value of other inputs. These approaches are quite value of the TVWS. This is because, so far, white straightforward. For the first method, making space equipment is still not widely available in meaningful comparison of frequency bands and the market. For this reason, it is imperative to market values in different geographic regions and use some practical methods to project the spec- time frames is a nontrivial task. The last two trum cost from other known usage scenarios into methods suffer from the requirements of poten- intended spectrum bands for the region of inter- tially uncertain values of non-spectrum inputs. est while considering constraining conditions. The principle for the adjustment of the DIRECT CALCULATION METHODS benchmark price should enable the conversion In direct calculation methods, the broker acts as a of values from other regions, spectrum bands, bidding company, and then uses the bidder’s and so on into the band of interest intuitively. method of predicting prices to set spectrum price. However, actual usage will depend on other fac- These are standard net present value (NPV) and tors that will be closely tied to the regulatory least cost alternative (LCA) or optimal deprival regime. Figure 3 illustrates the procedures value method (ODV). In NPV, price could be set involved in mapping the benchmark price into based on the standard NPV modeling that firms the intended market scenario. conduct, whereas LCA or ODV is the bid of an The method allows the modification of the average bidder or bidders for multiple-use bands, benchmark price with related modification coef- and only requires the use of cost information. In ficients to obtain the target price. Note that the this case, uncertain revenue projection (as in benchmark price, also called the standard price, NPV) is not required. Using direct calculation is the price of benchmark band – a band that methods requires careful selection of input infor- can easily be evaluated or for which the opportu- mation. This includes equipment (e.g., white nity cost can easily be calculated. In that regard, space devices) cost, equipment lifetime, and the first step is to choose the reference spectrum maturity of the network, among others. The infor- from which to compute the benchmark price. mation has to be chosen such that the values In secondary spectrum trading, the main func- obtained approximate market conditions. tion of the pricing mechanism is to determine the price of the spectrum reflecting its market value. PRACTICAL RESERVE PRICE ESTIMATION In the case where there are not enough players in Even though the TVWS can potentially be the market to conduct an auction, spectrum can applied to many different services in both non- still be directly traded through a merchant mecha- interactive services such as broadcasting, and nism where the price is based on the opportunity interactive services, such as WiFi, WiMAX, and cost. Furthermore, the benchmark price can be IEEE Communications Magazine • November 2012 125C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 6. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® used by the broker to set prices that maximize its trum. Both regulators see that access to sub-1- In the case where revenue. The benchmark price determination GHz spectrum is critical to network operators methodology can also be used to set the initial having the right blend of frequencies to provide there are not price in the competitive auction mode. sustainable competitive services. enough players in An important aspect of auction design, par- ticularly when it deals with multiple non-identi- the market to con- TVWS AUCTION DESIGN cal objects, such as the fragmented spectrum duct an auction, As noted earlier, in cases when the spectrum resources with limited bandwidth, limited allow- spectrum can still be demand exceeds the spectrum supply, the auc- able transmit power, and time availability, is to tion approach is the most effective technique. match these objects to the natural demands of directly traded Auctions have been considered for long-term actual players, and to maximize the broker’s through a merchant and short-term (online) spectrum trading and profit. In our considered case of TVWS spec- mechanism where discussed extensively in many papers. A detailed trum auctions, the objects are 8 MHz DTV chan- compendium on auctions can be found in [8], nels with different maximum power levels and the price is based on while a number of auction models for spectrum availability time, while the considered players the opportunity cost. sharing have been described in [9]. There are (mobile LTE operators) demand LTE channels several problems in spectrum auction design, of various bandwidth (1.4, 3, 5, 10, or 20 MHz) Furthermore, the which must be addressed if it is expected that associated with possible duplex modes (time- benchmark price can this mechanism should provide effective and fair division duplex or frequency-division duplex, in be used by the bro- spectrum allocation to the players. First, the size which additionally a duplex frequency gap is of the market and the spectrum reserve price are required) with particular transmit power levels ker to set prices that economic aspects that must be considered. In characteristic of base stations or mobile equip- maximize its the TVWS spectrum trading business scenario, ment. These demands may also be diverse for mobile cellular (LTE) operators are often con- various considered time periods. Thus, the com- revenue. sidered to be the prime candidates to act as binatorial auction has to be conducted by the players interested in spectrum leasing to support broker to allocate the available resources to the their subscribers’ demands, particularly in peak players with bids indicating combinations of fre- traffic periods. There is a risk of collusion quency and power demands. The combinations between the players that may affect the auction of bids that maximize the broker’s revenue are results. The solution for this threat is the mecha- the winning ones. The question remains, howev- nism of the auction (minimum) reserve price, er, how the leasing time periods (time units con- which is estimated based on the AIP. Another sidered with a certain suitable resolution, e.g., of aspect of the spectrum auction is the so-called a few hours within a day, or years in a decade) winner’s curse, a tendency for the winning bid to are treated. The simultaneous auction can be exceed the intrinsic value of the purchased spec- conducted for a number of leasing periods in trum bandwidth. The risk of such effect may be parallel. In such a case, the auction results for minimized by publishing the auction results for distinct leasing periods are independent and are the players to use them in their learning mecha- announced at the same moment for all these nisms for the future auctions. periods. It may cause the exposure problem; that The aspects associated with the amount of is, some players’ demands concerning the leasing spectrum granted to the players with temporarily periods may be only partially satisfied [13]. The exclusive rights must be analyzed when designing sequential auction also treats the leasing period the auction. Spectrum caps rule restricts the demands independently; however, the distinct amount of spectrum a player can (temporarily) auctions for each period are conducted sequen- hold in a particular geographic area. It may affect tially, and the results are announced after each players’ demands by determining the maximum auction, which allows the players to modify their percentage of the available TVWS spectrum that bids for the next period. Finally, the combinatori- can be granted to each player in this area after al auction considers time demands of the players taking into account the existing licensed spectrum together with their frequency and power already held by each player. Such a mechanism demands, and matches the combinations of these attempts to avert market monopolization and demands to the available resources. This auction ensure some fairness in the resource distribution. is based on the all-or-nothing rule, that is, if the The spectrum cap rule also results in some of the demands of a player cannot be satisfied in all spectrum opportunities being reserved for new requested leasing periods, the bid is rejected entrants to the secondary spectrum market. Note from the broker’s optimization algorithm. that while the FCC removed specific spectrum The problem of optimal determination of a caps for auctions in 2003, it then introduced a combinatorial-auction solution is NP-complete. spectrum screening process which it uses to evalu- However, the number of players and available ate holdings when companies merge with each white spaces in a real-world scenario is usually other or trade spectrum; these screens are con- not high. The auction and related computations ducted on a case-by-case basis whereby the FCC will also not be executed too frequently (once a analyzes the impact of changed spectrum holdings day or even less frequently). Finally, the branch- on the market in question [10]. Other regulators and-cut algorithm, a method of combinatorial maintain that caps are still necessary at the auc- optimization for an integer linear programming tion stage to strike a balance between allowing problem, can be applied (as it has been in our existing players to access more spectrum whilst implementation discussed in the next section), also space for new entrants. Both the U.K. Ofcom which lowers its complexity. [11] and the Australian regulator [12] are impos- The time dimension of the license period is ing spectrum caps on their forthcoming auctions another important aspect of the auction design. of 700 MHz and 800 MHz digital dividend spec- Long-term licenses (in years) bring stability and 126 IEEE Communications Magazine • November 2012C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 7. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® Long-term licenses (in years) bring stabil- DVB-T transmitter GPS (primary) ity and allow for long-term planning of the network SLAVE development for the MASTER (secondary) (secondary) spectrum buyers, DVB-T receiver (primary) while short-term TVWS BTS licenses bring flexibil- ity for the secondary WSD WSD spectrum market, COGEU broker make the market Broker profit more open to Internet Spectrum policies operation QoS of secondary link smaller players, and TVWS usage rate allow for the Player 1 Player 2 Player N Interference evaluation Spectrum buyers Geo-location database validation transfer of CAPEX Munich Regulator into OPEX. TVWS GUI maps PMSE Figure 4. Real-world spectrum trading demonstration scenario. allow for long-term planning of the network ability for different transmit power requirements development for the spectrum buyers, while (Fig. 5). After contacting the database through short-term licenses bring flexibility for the sec- the web-based link, the broker presents the spec- ondary spectrum market, make the market more trum portfolio to the players as described earlier. open to smaller players, and allow for the trans- A number of auction participants have been fer of CAPEX into OPEX. It seems impractical engaged to test the spectrum trading algorithm. to conduct auctions for very short leasing-periods The auction winner is entitled to activate a Mas- (e.g., a single connection time). Rather, the mini- ter and transmit an orthogonal frequency-division mum considered period of time is a couple of multiplexing (OFDM) signal (video streaming) hours to cover at least the peak traffic period in over the assigned TV channels, as shown in Fig. a day. The short-term auction is going to be 4. This signal must be successfully received by the automated (with the automated agents as play- Slave with required QoS while no harmful inter- ers), and repeated in many hundreds or thou- ference has been measured in the DVB-T com- sands of independent TVWS areas for multiple mercial receiver operating in the neighboring leasing periods. Furthermore, a sealed-bid auc- DVB-T channel. Initial measurements have con- tion, rather than an open auction, would seem to firmed successful field trials of such LTE trans- be more advantageous in this case, as it limits the mission over TVWS in the tested area. signaling between the players and the broker. In the considered system evaluation setup, the leasing time granularity is eight hours in a day. The eight-hour periods reflect typical daily variations of REAL-WORLD TVWS SPECTRUM cellular systems telecommunication traffic. Alloca- tion of spectrum with temporary exclusive rights is TRADING DEMONSTRATION cleared, and the auction is repeated every day. The TVWS secondary spectrum market mecha- However, this adopted time granularity is scalable. nisms with the centralized spectrum broker In order to bring predictability to the operators’ described above have been considered within the business models, one-year licenses for specific sites European COGEU project.1 Within this project can be offered. The sealed-bid first-price time- the auction design and spectrum-trading consid- simultaneous and combinatorial auctions have erations described above have been successfully been tested and their results analyzed. Four sce- applied in the real-world test scenario. Figure 4 narios of frequency resources availability have illustrates the COGEU spectrum trading demon- been identified: two blocks of continuous available strator with the main building blocks and actors. bandwidth of 24 MHz and 16 MHz, respectively 1COGEU is a European In the tested scenario, the geolocation database (R1); 24 MHz and 8 MHz (R2); 16 MHz and 16 7th Framework Program consists of the TVWS maps for the Munich area. MHz (R3); and 16 MHz and 8 MHz (R4) with the project (Cognitive Radio These maps have been elaborated based on field same power constraint that allows for the typical Systems for Efficient measurements and advanced propagation-model LTE downlink transmission. A number of sets of Sharing of TV White calculations for the LTE systems and contain players (LTE operators) have been allowed to par- Spaces in the European spatial information about DVB-T channel avail- ticipate in the auctions requesting combinations of Context). IEEE Communications Magazine • November 2012 127C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 8. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® Figure 5. COGEU online framework for the Munich scenario, Show White Spaces tool (maximum allowed transmit power by a TVWS device in Channel 59), available at http://projectos.est.ipcb.pt/cogeu2/ LTE downlink channels of 20 MHz, 10 MHz, and demanding either 10 or 5 MHz is higher in the 5 MHz. Here, we present example results for a set simultaneous auction, but in this auction it is defined of players consisting of four players requesting 10 in a different way than for the combinatorial auction. MHz each and four players requesting 5 MHz A user in the simultaneous auction is satisfied to the each. The probability of accessing the spectrum degree his or her frequency and power demands are auction and requesting a particular LTE channel allocated in the winning set of bids for separate leas- bandwidth has been modeled by the uniform distri- ing periods. In the combinatorial auction, a user is bution with the probability equal to 65 and 85 per- satisfied only if all his or her requests are met. cent for 10 and 5 MHz, respectively. The Finally, some preliminary results have also probability of participation in auction for the been obtained in the spectrum value estimation. defined low, medium, and peak traffic periods has The spectrum value ranges from approximately been scaled appropriately as per the daily traffic €25 (in low populated areas in a low traffic peri- intensity in cellular networks. od) to more than €300 (in highly populated The auction efficiency (i.e., the ratio of the spec- areas and in peak traffic hours) for 1 MHz, 1 km trum bandwidth sold to the available spectrum on radius of the considered area, 30 dBm of the the market) is presented in Fig. 6a. The results con- transmit power limit, and 8 h allocation period firm that both types of auction, simultaneous and in a day, assuming one-year stability in license combinatorial, are efficient in selling the spectrum, allocation in Germany. although there are some differences. In peak hours the auctions’ efficiencies are the same, but for other considered time periods, the average spectrum uti- CONCLUSIONS lization is higher for the simultaneous auction, The secondary spectrum market in TVWS has the because the allocation rule allows for the satisfaction potential to support wireless communication ser- of the players’ demands concerning the time periods vices of multiple players, including mobile commu- only partially. The combinatorial auction, on the nication operators with continuously increasing other hand, is based on the all-or-nothing rule dis- spectrum demands. We have proposed a spectrum cussed in the previous section. This also has an broker capable of allocating the spectrum for short- impact on the players’ satisfaction rates, presented in term disposal through trading mechanisms. These Fig. 6b. The average satisfaction of the players mechanisms — access to the geolocation TVWS 128 IEEE Communications Magazine • November 2012C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®
  • 9. C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND® spectrum database, handling of the spectrum repos- itories and trading policies, the merchant mode of 1.1 operation, and auctions — have been discussed. SA - daily average 1 CA - daily average The real-world demonstration of the spectrum bro- SA - low-traffic period ker and the geolocation database in Munich, Ger- CA - low-traffic period many, show the potential of maintaining the TVWS 0.9 SA - peak-traffic period CA - peak-traffic period databases, the efficiency of the proposed market Auction efficiency 0.8 mechanisms, and high performance of the LTE transmission in the opportunistically accessed 0.7 TVWS with primary DTV service protection. 0.6 ACKNOWLEDGEMENTS The study presented in this article has been sup- 0.5 ported by the European Commission, Seventh Framework Program, under the project COGEU 0.4 (contract no. ICT-248560). 0.3 REFERENCES 0.2 [1] T. W. Hazlett, “Unleashing the DTV Band: A Proposal R1 R2 R3 R4 for an Overlay Auction,” submission to FCC #26, “A Available resources set National Broadband Plan for Our Future,” Dec. 2009. (a) [2] (Provisional) Final Acts — ITU-R, World Radiocommuni- cation Conf. 2012, Feb. 2012. [3] C. Bazelon, “Licensed or Unlicensed: The Economic Con- SA, 10 MHz demands siderations in Incremental Spectrum Allocations,” IEEE 1 SA, 5 MHz demands Commun. Mag., Mar. 2009, vol. 47, no. 3, pp. 110–16. CA, 10 MHz demands [4] W. Webb, “An Optimal Way to License the Radio Spec- CA, 5 MHz demands trum,” Telecommun. Policy, vol. 33, no. 3–4, 2009, pp. 0.8 User satisfaction rate 230–37. [5] C. Bazelon, “Next Generation Frequency Coordinator,” Telecommunications Policy, vol. 27, no. 7, 2003, pp. 517–25. 0.6 [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, “Administrative 0.4 Incentive Pricing of Radiofrequency Spectrum,” report for the Australian Communications and Media Authori- ty, Oct. 2008. 0.2 [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, Cognitive 0 Radio Communications and Networks: Principles and R1 R2 R3 R4 Practice (Ch. 17: Auction-Based Spectrum Markets in Available resources set Cognitive Radio Networks), Elsevier, Dec. 2009. [10] M. Goldstein, “Enhanced Data Collection Could Help (b) FCC Better Monitor Competition in the Wireless Indus- try,” US Government Accounting Office-10-779, July 27, 2010. Figure 6. Results of the spectrum simultaneous (SA) and combinatorial (CA) [11] Ofcom, “Second Consultation on Assessment of Future auctions – auction efficiency in a) spectrum selling and b) users’ satisfaction Mobile Competition and Proposals for the Award of rates. 800 MHz and 2.6 GHz Spectrum and Related Issues,” Jan. 2012. [12] Australian Government Radiocommunications (Spec- interests include the IEEE P1900.6 cognitive radio standard. trum License Limits) Direction No. 1 of 2012 (F2012L00205 ), Feb. 2012. TIMOTHY K. FORDE received his Ph.D. degree, which focused [13] M. Parzy and H. Bogucka, “Non-Identical Objects Auc- on wireless ad hoc networks, from the University of Dublin, tion for Spectrum Sharing in TV White Spaces – The Trinity College, in 2005. He works at CTVR—The Telecom- Perspective of Service Providers as Secondary Users,” munications Research Center based at Trinity College, IEEE DySpAN 2011, 3–6 May, 2011, Aachen, Germany. Dublin. His research interests include innovative spectrum access regimes, focusing on the economic policy and tech- BIOGRAPHIES nical challenges of RF spectrum reform. H ANNA B OGUCKA (hbogucka@et.put.poznan.pl) received _________________ J O S E P H W. M W A N G O K A received his Ph.D. degree from M.Sc. and Ph.D. degrees in telecommunications from Poz- Tsinghua University, Beijing, China in 2009. Until 2012 he nan University of Technology (PUT), Poland, in 1988 and was a researcher at the Instituto de Telecomunicações, 1995, respectively. Since 1988 she has been employed at Aveiro, Portugal. His research interests include wireless PUT, currently in the Chair of Wireless Communications as communications, cognitive radio technology and dynamic a professor and deputy dean for research on the Faculty of spectrum management. He has co-authored a number of Electronics and Telecommunication. She has research inter- book chapters and papers on cognitive radio and the use ests in the area of wireless communications, and flexible, of TVWS. adaptive, and cognitive radio systems. She is the author of more than 100 papers and three handbooks (in Polish) in MARCIN PARZY received an M.Sc. degree in telecommunica- the area of radio communications. tions from Poznan University of Technology (PUT), Poland, in 2007. In the years 2007–2009 he worked as a radio P AULO M ARQUES received his Ph.D. from the University of planning engineer of GSM and UMTS in Orange, Poland. Aveiro-Portugal in 2006. He is a senior researcher at the Since 2009 he is a Ph.D. student at PUT in the Chair of Instituto de Telecomunicações and a professor at Castelo Wireless Communications. He has research interests in the Branco Polytechnic Institute. He is the scientific coordinator areas of wireless communications, game theory, resource of the European research project FP7 COGEU. His research allocation, spectrum auctions, and cognitive radio systems. IEEE Communications Magazine • November 2012 129C qM IEEE M ommunications q qM Previous Page | Contents | Zoom in | Zoom out | Front Cover | Search Issue | Next Page MqM q Qmags THE WORLD’S NEWSSTAND®