The Value of Sensing for TV White Spaces

Vânia Gonçalves IBBT-SMIT, Vrije Universiteit Brussel

Pleinlaan 2, 1050 Brussels, Belgium vania.goncalves@vub.ac.be

Sofie Pollin IMEC

Kapeldreef 75, 3001 Leuven, Belgium sofie.pollin@imec.be

Abstract—The main challenge to allow for use of the so-called TV White Spaces is to achieve a reliable approach for detecting presence of licensed users ensuring that harmful interference to television signals and other incumbent services does not occur. In the current debate, there is a trend towards the use of a geo-location database only, driven by the fear that other techniques fail to achieve the required detection reliability. Therefore, in this paper we intend to assess if the technical and business value of sensing in the context of TVWS should be neglected. Taking in consideration the discussion on the adequate technical requirements currently taking place in Europe and the USA, the cost and performance of the proposed techniques for local sensing, distributed sensing and geo-location database are compared through a simple model. As a result, we conclude that using a distributed sensing solution based on low-cost low-power sensing engines, we could achieve a solution with hardware and energy costs a par with the geo-location database. However, by assessing the costs and business impacts for stakeholders such as manufacturers and White Spaces Service Providers we conclude that in the geo-location database solution, regulators, White Spaces Service Providers, White Spaces Database Providers and consumers may incur additional infrastructure, maintenance and administrative costs compared to a distributed sensing solution. Consequently, we are of the opinion that the distributed sensing solution in the context of TVWS does indeed present value and its business and technical impact should be considered in further research and regulatory activities.

Keywords- Sensing, Geo-location Database, TV White Spaces, Energy Cost, Business Analysis

I. INTRODUCTION The opportunistic access to the so-called TV White Spaces

(TVWS) has been intensely debated over the last couple of years both in the United States and Europe. TV White Spaces is the term used to denominate the portions of the VHF and UHF spectrum, which will become available in specific geographical locations after the digital switchover takes place. This spectrum is considered to have superior propagation and building penetration characteristics offering great coverage compared to other spectrum in higher frequency bands. Many applications are envisioned for TVWS such as wide area coverage in rural areas (IEEE 802.22) or super-WiFi (IEEE 802.11af), enabling more powerful Internet connections. However, access to the TVWS also comes with technical challenges, in particular, White Spaces Devices (WSD) can potentially interfere with existing television signals, wireless microphones, and other radio signals.

To cope with the technical challenges, in November 2008 the United States regulator FCC issued a Second Report and Order (R&O) deciding, not only, to open the TVWS to unlicensed use, but also specifying a number of requirements for the unlicensed use of TVWS spectrum [13]. In this R&O two types of White Space Devices (referred by the FCC as TV band devices (TVBDs)) are introduced: 1) fixed devices, which will operate from a fixed location with relatively higher power and could be used to provide wireless broadband access in urban and rural areas; and 2) personal/portable devices, which will use lower power and could be used to provide wireless in-home local area networks or take the form of devices such as WiFi cards. To avoid interference with services already running on those channels, devices would have to identify unused channels according to specified methods depending on their device type. In specific, a fixed device must employ both geo-location/database access and spectrum sensing capabilities enabling the device to listen for and identify the presence of signals from other transmitters. A personal/portable device must either 1) be under the control of a fixed device or a personal/portable device that employs geo-location/database access and spectrum sensing or 2) employ geo-location /database access and spectrum sensing itself. In addition, in this R&O, the FCC had, however, restricted the commercialization of WSD until the end of the DTV transition, i.e. February 2009, and encouraged field trials with WSD. Up until now, several companies have submitted smart radio prototypes with spectrum sensing technology [14], made field trials with WSD [15][16] and made plans to create and operate a TV bands database [17][18]. At the same time, many have argued about the technical efficacy of spectrum sensing technologies, the high costs of building devices incorporating such technologies, how the sensing requirement would limit innovation and commercial investment, and therefore advocating for a solution relying on geo-location/database access only [19][20].

On September 23, 2010, the FCC issued a Second Memorandum Opinion and Order (MO&O) [20] determining the final rules for the use of WSD. The new rules remove the mandatory requirement that WSD should include sensing technology to detect the signals of TV stations and low-power auxiliary service stations (wireless microphones). The FCC states that the geo-location and database access method and other provisions of the rules will provide adequate and reliable protection for incumbent devices, thus making spectrum sensing not necessary since this mandatory requirement would not best serve the public interest. However, the FCC recognized the value of sensing for TVWS in the following

2011 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN)

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statement: “We continue to believe that spectrum sensing will continue to develop and improve. We anticipate that some form of spectrum sensing may very well be included in TVBDs on a voluntary basis for purposes such as determining the quality of each channel relative to real and potential interference sources and enhancing spectrum sharing among TVBDs”. Although spectrum sensing is no longer a mandatory requirement, the FCC has still defined the technical rules for its use. With regards to the geo-location database, the key specifications are still to be drafted and some key issues clarified, such as how many databases will be in place, who will create and manage them, will they be public or closed, how will they be certified, and so on.

In Europe, work on a pan-European specification for cognitive radio systems (CRS) is taking place within the Electronic Communications Committee (ECC) of the European Conference of Postal and Telecommunications Administrations (CEPT). In September 2010, a draft report [6] was released assessing the appropriateness of the sensing and geo-location techniques to provide protection to the existing radio services in the context of a diversified range of envisaged deployment scenarios for WSD. In this report, the spectrum sensing technique (local sensing in which detection is carried out independently by each device) employed in this study is regarded as not reliable enough to guarantee interference protection for DTT receivers and programme-making and special events (PMSE) systems. In the DTT scenario, spectrum sensing does not guarantee a reliable detection of the presence/absence of the broadcasting signals at the distance corresponding to the interference potential of a WSD. Therefore, the use of a geo-location database appears to be more reliable in this scenario. In the case of the PMSE deployment scenarios, temporal fading caused by multipath propagation is likely to be one of the main factors affecting the ability of WSDs to protect PMSE systems from interference. In some cases, this may lead to very low detection threshold, far below the WSD receiver noise floor, making spectrum sensing techniques quite impractical. Thus, the ECC argues that setting sensing thresholds very low in order to protect incumbent services, would result in increasing device cost and complexity as well as a in reduced number of available channels. In its view, this would limit the commercial deployment of WSD and reduce the potential value to end-users. Therefore, the report concludes about the primary need to employ geo-location/database access, since sensing alone would not guarantee interference protection. In case geo-location/database access can provide sufficient protection to the broadcast service, sensing should not be a requirement, since its potential benefit still needs to be further assessed.

On a country level, the UK regulator, Ofcom, has considered in December 2007 to open the use of interleaved spectrum by license-exempt applications and allow cognitive access to this spectrum, guaranteed that licensed uses are protected [22]. As a follow-up, in February 2009 [21], Ofcom opened a consultation proposing three main approaches to detect unused spectrum including sensing, geo-location database and beacon transmission. As a conclusion [23] to this consultation, Ofcom has discarded beacon transmission as it was considered the least appropriate method. Regarding the use

of sensing, Ofcom accepted respondents’ comments about the possible need to set lower sensing and transmit levels in order to achieve greater levels of protection to licensed users. However, in Ofcom’s view, these requirements would place an excessive additional burden on cognitive devices. Although Ofcom concluded from responses that the most important mechanism to allow access to TVWS would be geo-location, sensing alone methods would also be allowed. But Ofcom considered that the commercial implementation of sensing-only cognitive devices would still be many years away and therefore decided to postpone any decision on setting specific parameters for sensing. It rather decided to proceed on defining the specific technical requirements for the use of a geo-location database to determine available white spaces. A proposal for such requirements has been recently unveiled and a consultation is currently underway [24].

Since the mandatory requirement to use sensing technology has been discarded due to sensing technical efficacy, we aim in this paper to compare the geo-location database, local sensing and distributed sensing techniques from both a technical perspective and related business implications and conclude about the value of sensing techniques for TVWS. All considered techniques are put into the context of the technical rules defined by both the FCC MO&O decision and the CEPT ECC draft report [13][6].

In the following section we review the technical requirements that are being put forward by US and European regulators. In section III we propose a simple model that allows for the comparison of three possible approaches for determining spectrum availability: local sensing, distributed sensing and a database approach. The proposed model aims at modeling all effects that have been determined to be relevant to the spectrum sensing problem: fading, shadowing, noise uncertainty and geo-location errors. The results presented are quantified where possible, but in most cases the comparison is done on a very high level. For instance, comparison of local and distributed sensing in terms of performance, throughput cost and energy consumption is done based on the model. From those results, it can be concluded that the local or distributed sensing techniques are within reasonable bounds, i.e., the impact on user QoS is not too extreme. If the technique can be considered to be within reasonable bounds, it is viable for further analysis. By no means the results presented in this paper are quantified accurately enough to allow for a detailed comparison of techniques with a very similar performance. The goal is to identify extremes. Based on the results of the model, that put forward distributed sensing as an alternative to local sensing, in section IV we compare the business implications of distributed sensing and geo-location database for business stakeholders. Finally, we conclude that a distributed sensing solution based on low-cost low-power sensing engines could encompass hardware and energy costs a par with the geo-location database. However, by assessing the costs and business impacts for all stakeholders involved in the ecosystem, we conclude that some stakeholders may incur additional infrastructure, maintenance and administrative costs compared to a distributed sensing solution.

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II. TECHNICAL RULES The debate about TVWS has been centered around the

technical rules that should be set to allow reuse of the spectrum while ensuring sufficient guarantees towards the licensed users. One of the very first initiatives, the IEEE 802.22 standard, focused its effort on a sensitivity threshold that should be met [1]. For instance, the 802.22 working group specifications require detectors to have a sensitivity of -116 dBm, which corresponds to a SNR = -20 dB for those channels [2]. This corresponds to a design-time worst case safety margin of roughly 20 dB [3]. The fundamental sensing problem is that the channel sensed by a secondary transmitter does not provide all necessary information to that secondary transmitter. This problem is illustrated in Fig. 1. The secondary transmitter can only sense the channel between the primary transmitter and itself. The relevant information is however in the channels towards the receiver, that should be able to receive the primary transmission with sufficient signal quality. To account for this uncertainty, large sensing margins are typically assumed. As a result, the sensing problem is very tough and fundamentally, there seems to be little solutions to this problem when relying on local sensing only. The realization of this has led to research on alternative techniques, such as distributed sensing [4] and the geo-location database approach [5].

Figure 1. Spatial reuse of the TV White spaces requires large safety margins to ensure that the receive contour of the primary transmitter is protected.

The study included in the ECC draft report [6] confirms these results. For the protection of the DTT service a sensitivity requirement as low as -165 dBm is claimed. For the protection of the PMSE, it is claimed that “In some cases, this may lead to very low detection threshold, far below the WSD receiver noise floor, which would make this technical quite impractical”. As a result, for those services, the use of a geo-location database is believed to be the most effective. However, it is also noted that to date it is not clear which information the database should contain.

The use of a geo-location database allows determining offline all relevant information about both unknown channels from any location to any receiver, solving the problem illustrated in Fig. 1. The idea [6] would be to generate a database that includes for each location a list of allowed frequencies and power levels. This information can indeed be determined from offline models or even field measurements. Although to date it is not yet determined how the database would be computed, it is already known that the database also involves some challenges: in particular, the secondary transmitter has to determine its location as accurately as possible. Ofcom takes into account the possibility of poor geo-location capabilities and thus defines a location accuracy of

100m [7]. While this value seems too high and inaccurate compared to the performance of GPS devices, it is realistic since GPS has a very poor performance indoor. Also, it cannot be assumed that ready-to-market WSD would be equipped with an accurate and expensive GPS receiver.

In the next section we propose a simple model that allows comparing the three possible approaches for determining spectrum availability: local sensing, distributed sensing and a database approach. The model proposed aims at modelling all effects that have been determined to be relevant to the spectrum sensing problem: fading, shadowing, noise uncertainty and geo-location errors. The model is inspired by known results and publications both from the regulatory domain [6][7] and academic contributions [2][4][8]. However, it is only designed to be able to analyze the three techniques in terms of power, throughput cost and spatial reuse performance. These comparisons can then be used to determine the value of each of the techniques from a business point of view.

III. SIMULATION MODEL In this section we propose a simplified model forthe

environment that will be the basis for deriving the requirements for local and distributed sensing. These will then be used to derive the cost of sensing in terms of throughput and energy consumption. In addition, this will allow for a rough comparison between the database and both local and distributed sensing. The model takes into account fading, distance-dependent pathloss and shadowing, but is by no means as accurate as the models used in the studies conducted by the FCC and ECC [20][6].

A. Environment Scene In this analysis a total of 1000 random scenes is generated

with 10x10km of size and consisting of a random number of buildings that cause shadowing. In the simulations, the Primary User (PU) is assumed to be in the left lower corner, while the Secondary User (SU) is in the right upper corner. The SU is also assumed to be indoor, hence a building is always placed in the right upper corner. For every scene generated, on average 150 clusters of buildings are placed in the scene (plus the one for the SU). Building groups are 500mx500m in size and for every 1m of building penetration a loss of 0.205 dB is assumed. This means that a building blocking the Line-of-Sight with the PU causes a total loss of 10.25 dB [4]. In addition to shadowing, we compute the pathloss L as a function of the distance d:

(1)

In (1), α=2 and β represents the antenna gains, while Ls refers to shadowing. We assume β=40dB for the secondary transmitter and β=30dB for the primary transmitter. This takes into account the fact that the primary transmitter is higher. We further assume equal antenna gains for the PU and SU (for simplicity). We can hence determine the total pathloss at every grid location for transmissions from the PU and from the SU.

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Fig. 2 shows an example of a simulation scene. The pathloss from the PU is given in Fig. 3 and from the SU in Fig. 4.

Figure 2. Simulation scene with PU and SU location and randomly placed buildings.

Figure 3. Pathloss L from the PU to any point in the scene.

Figure 4. Pathloss L from the SU to any point in the scene.

B. Reuse assumptions From the pathloss model introduced above, the received

signal of a PU receiver can now be easily determined, assuming the PU transmits with a power PPU. In addition, the

received signal-to-interference ratio, SIR, can also be determined when a SU transmits with a power PSU:

(2)

In (2), LPU and LSU represent the pathloss from the PU and SU, respectively. In the rest of this paper, the following assumptions about these values are taken into account: SIR = 20dB, PPU =50dBm (100W), PSU = 10dBm (10mW). Finally, the SIR target of 20dB is assumed to be met within a radius of 200m of the PU.

C. Sensing Requirements and Performance Based on the scene introduced above, we can now

determine the sensing requirements and performance in terms of the spatial reuse that can be achieved. By randomly generating the scene 1000 times, we can determine that for the assumed power levels, the SU would be able to transmit 61% of the time. The target sensitivity to avoid interference to the PU is determined by determining the minimum of the PU power levels received in all the 65% of the cases where no reuse of spectrum is allowed. In these simulations, using a LPU of 184dB, results in a received power level of 50-184 = -134dBm. Assuming a noise floor of -106dBm for a bandwidth of 6 MHz, this gives us a sensing target of -28dB. We then select the probability that a power level below that threshold is measured at the SU. This probability is 13%, which means that the SU can transmit in 13% of the cases.

D. Database Performance To determine the performance of the database approach, we

assess how often a SU can send. There are two possible implementations of the database, a cheap and an expensive one. A cheap implementation only takes into account the pathloss from the PU, and lists locations where the pathloss is above a certain threshold. We assume the same threshold as for the sensing. This means, ideally, a throughput of 13% is achieved. We further assume a localization error of 50m. This means that a SU can only transmit with a certain output power if any location within 50m of that SU would allow a transmission at such output power. This means that if the SU would be inside, an outdoor power level needs to be assumed. From the proposed simulation model it is possible to derive the resulting spatial reuse gain. Due to this localization error, the spatial reuse probability is further decreased to 6% for the scenario above. This is a significant reduction of the spatial reuse probability. To a large extent, this is caused by the fact that the SU is assumed to be indoor (i.e., a building was added at the SU location). However, with the geo-location database, the SU cannot benefit from the indoor location since the 50m error will also account for outdoor SU locations.

We further model the performance of an expensive database approach. This database considers both the pathloss from the PU and from each possible SU. Since more information is taken into account (i.e., all information in Figs. 2 and 3), a better spatial reuse can be obtained. For the simplified simulation model, we determined this to be 31% in this case,

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hence better than sensing and significantly better than the cheap database.

IV. SENSING MODEL Based on the simulation model introduced above we can

determine the sensing requirements based on a distance-dependent pathloss model and a shadowing model. In this section, we derive the resulting sensing cost, assuming also fast fading and noise uncertainty.

A. Local Sensing The sensitivity requirements presented above have been

derived taking into account the distance-dependent pathloss and shadowing. In this section, we will determine the performance of a simple sensing algorithm, and start from that to determine the cost of sensing in terms of throughput and energy consumption. On top of the shadowing already modeled above, fast-scale fading will also be added in the analysis below.

1) A simple model of ideal sensing In the proposed model, the spectrum sensing problem is

modeled as a simple binary Gaussian hypothesis testing. This is the simplest model, where we select a threshold η in order to achieve a target probability of false alarm Pfa which gives the probability of the presence of PU being falsely detected. To meet the probability of detection Pd, the sensing duration or number of samples N can be tuned ideally. Using the Neyman-Pearson criteria [9] we can derive the expression for detection and false-alarm probabilities as follows:

(3)

In (3), A is the received signal amplitude that is a function of the transmitted power and the pathloss, σ2 is the received noise power and N the number of samples.

When multiple SU are present in the house, the probability to harm the PU is increased. Indeed, assuming a single SU can achieve a Pd of 90%, the probability that 10 users all detect the PU is then 0.9^10 = 0.3487. This probability of harmful interference is too large. Therefore, to meet the target probability of creating harmful interference with a group of SUs to a single PU, the local sensing performance should be even better. To meet the same group detection performance Pd,group, an individual user Pd of 1% is then needed. As a result, the probability that 10 users all detect the PU is then 0.99^10 = 0.904 which is better than the target 0.9.

Following this model, Fig. 5 illustrates the required number of samples N as function of SNR for the case where we have a single SU aiming at detecting a single PU. It is also shown that when the number of interferers K is increased to 10, the required number of samples is increased as well. This is a

reflection of the fact that the local sensing performance should be improved in that case.

Figure 5. Number of samples needed to meet sensing performance (Pd,group of 90% and Pfa of 10%) for 1 and 10 interferers.

In ideal cases, can be assumed a SU to operate well below the noise floor, which is -106 dBm in a 6MHz TV channel. From the analysis above, we derived (simulated) that a sensitivity of -28dB would be needed, already taking into account shadowing. Below we will consider additional causes that will degrade the sensing performance: fading and noise uncertainty. To account for those effects, an additional margin on the required sensitivity is required.

a) Fading We assume the small scale multipath fading is flat and

follows a Rayleigh distribution. When a single user is considered, we have to take into account a margin for the case this user is in a deep fade and hence receives the PU signal at a lower power.

b) Noise uncertainty The simple detection model introduced above assumes we

have a perfect knowledge about the noise floor to select the threshold to achieve the target Pfa. In practical systems this is however not a realistic assumption [8]. The noise uncertainty results in an SNR wall below which no reliable detection is possible. In this study, we neglect the SNR wall. We do assume that the noise can be Nu dB higher than the theoretical noise floor, which is caused by the noise figure properties of the receiver hardware. We assume Nu = 5dB.

The impact of shadowing, fading and noise figure on the detection performance is summarized in Fig. 6. If we assume that a certain margin should not be exceeded 10% of the time, we can see from Fig.6 that that margin should be of 9dB.

2) Local sensing at system level Finally, the cost at system level is determined. From the number of samples N we determine the sensing overhead in terms of throughput loss and power cost. The result is given in Fig. 7 for 1 and 10 interferers. It can be seen that when taking into account the full SNR margin, sensing throughput and power loss can be significant.

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Figure 6. CDF of SNR loss encountered due to noise uncertainty and fading. As a result, the SNR operating point in the ideal model should be adjusted

with 9 dB.

Figure 7. System level throughput and power cost as function of SNR operating point. For a considered point of -45dB with 10 interferers or

secondary users that do not cooperate, the throughput overhead is 15% and the lifetime of the sensing functionality is less than a day.

The cost of sensing can then easily be determined based on the number of samples N. The sensing period is assumed to be 100ms, which means that sensing is performed 10 times per second. Assuming further a sampling rate R of 20 Msamples/sec, it can be determined that the throughput cost is 10xN/R.

The energy cost is determined by assuming that the sensing front-end consumes a total of 100mW. From the sensing time N/R the energy cost in Joules can then be determined. The energy cost is then linked to the lifetime of the battery by assuming the battery is an AAA battery giving a total energy of 1000 Joules.

The area or chip cost of the energy detection algorithm can be neglected, since this simple sensing functionality can easily be implemented on any existing transceiver.

B. Distributed sensing As introduced in [4] there are several options to implement

distributed sensing. The most simple approach is to assume energy detection sensing (as modeled also above) combined

with a low bandwidth communication between nodes. This means that radios do not have the bandwidth to communicate samples, but only decisions. In that case, we assume that a false alarm or detection is generated if one of the cooperating radios assumes a primary user to be present. To implement this, in the model above, we only have to update the local Pfa and Pd targets to meet a system level constraint on Pfa and Pd, as follows:

(4)

In (4), K represents the number of interferers and NN the number of cooperating SUs. NN is assumed to be small (10) and it is assumed cooperating SUs are close to each other, seeing the same received power of the PU (i.e., within a building or a room). Once these constraints have been updated, we can compute the throughput and power cost of distributed sensing, neglecting the communication overhead to allow the collaboration. This is possible since we assumed a low bandwidth approach for the collaboration. In [27] an efficient approach based on a mini-slot protocol is presented allowing for sharing distributed sensing results in a network. The overhead of this protocol was shown to be negligible. From the results in Fig. 8 it can be seen that the throughput and energy costs of distributed sensing, per node, become acceptable as well. This means that a user’s QoS experience would not be harmed by the distributed sensing.

Figure 8. Cost of sensing in the cooperative case. The throughput and power cost is significantly improved when collaborative sensing is employed.

For the database, it can be assumed that the throughput and energy overheads will be manageable as well. Accessing a database will cause some extra control overhead, but this would not be very visible to the user. From the technical analysis, we can hence conclude that while local sensing seems hard to achieve within reasonable energy and throughput costs,

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the use of distributed sensing seems acceptable. Although in this paper we do not present absolute numbers for the database approach, the energy and throughput costs of distributed sensing are expected to be of the same order or magnitude of the database approach.

In the next section we look at the business implications of both distributed sensing and geo-location database.

V. BUSINESS IMPLICATIONS One of the main reasons for the considerable interest in the

TVWS from a wide range of business stakeholders is the attractive combination of bandwidth and coverage. TVWS are thus expected to bring citizen and consumer net benefits since they would enable a wide range of new services, for instance, wireless access in the home, local or personal area networks in urban environments, as well as in rural areas, within public spaces or communities and enable automation in certain industrial sectors. Ofcom estimated that giving license-exempt access to TVWS would deliver direct economic benefits of the order of £170-270m net present value over 20 years in the UK [24].

However, consumer and citizen drawbacks can also be identified in case current licensed applications are impacted by interference. For example, disruptions to TV broadcasting may occur in certain regions or interference to wireless microphones may cost disruptions in shows or public events. Therefore, the FCC or Ofcom are being very careful in giving license-exempt access to white spaces and aim at setting the requirements for a mechanism that would represent the lowest risk of harmful interference to licensed users.

Although in all studies conducted by the FCC, Ofcom and ECC, mechanisms such as geo-location/database access, sensing techniques and a combination of both have been considered as possible mechanisms to enable license-exempt access to TVWS, sensing has been discarded as a possible short to medium term deployable solution. Among the main reasons identified by regulators or public consultations’ respondents the following can be highlighted: 1) sensing alone is insufficiently reliable to guarantee interference protection to incumbent licensed services; 2) a combined mechanism of sensing and geo-location database access would increase cost of WSD and delay TVWS deployment, whereas geo-location database alone seems to provide the required protection for licensed users; 3) some years of development are still required until sensing technology achieves the necessary reliability and commercial deployment.

The studies, however, focused primarily on local sensing techniques that indeed suffer from reliability problems. Moreover, these studies did not assess the power and throughput cost of sensing, which are the main drivers impacting the user performance during the lifetime of a device. In addition, none of the studies focused on the drawbacks of the geo-location database, and assumed ideal localization capabilities. Also, the costs and business impacts for stakeholders such as manufacturers and White Spaces Service Providers (WSSP) were also not clearly addressed.

In the following sections, based on the results of the model presented in the previous section that considers distributed sensing as an alternative to local sensing, we aim to compare the business implications of distributed sensing and geo-location database for business stakeholders. Despite having illustrated the limitations of sensing (section IV), and in particular, of local sensing, the objective of this paper is to assess if the technical and business value of sensing in the context of TVWS should be neglected.

A. Business implications of distributed sensing Sensing technology has been widely deployed in many

outdoor RF applications for years, providing information on the presence of signals and noise in individual bands within a selected spectrum of frequencies. Most of them are based on local sensing, but micro-and nano-electronics evolutions have enabled the creation of remarkable low-cost chips so that it becomes possible to envision sensing technology to be present in any wireless communication device. Every transceiver could be equipped with a simple energy detection sensing module, as the one assumed in the technical part of this paper, with a low incremental cost. Such sensing module can achieve a target SNR performance if sensing is performed long enough (assuming operation above the SNR wall). As a result, it is simple to model throughput and power costs of such sensing module, as was discussed in section IV. As shown in Fig. 7, as the number of interferers increases, the sensing throughput overhead increases and battery lifetime drops considerably.

As identified in the previous section, further improvements could be added to local sensing. A simple approach of distributed sensing combines energy detection sensing with low bandwidth communication between nodes, in which radios communicate decisions to neighboring radios. When nodes collaborate, it is possible to relax the sensing requirements and as such achieve the same performance in the network at a lower cost per user (Fig. 8). The communication between nodes could be very efficient and hence should not consume a lot of overhead. In [27] a mini-slot procedure is proposed to achieve such a distributed sensing. We can thus argue this solution (distributed sensing alone) could achieve equivalent reliability performance (compared to the database), with little investment costs in hardware and comparable performance costs.

Regarding deployment, distributed sensing does not require any coordinated activity among licensed and unlicensed users, relieving both sides from administrative burdens and deployment coordination. In a solution with distributed sensing embedded in the protocol, the information about the environment, i.e. frequencies and channels in use by licensed users, is kept at network level and no other form of infrastructure is required to provide this information to licensed or unlicensed users. The incremental cost of keeping a control channel between the unlicensed users is not meaningful, since these would communicate with each other in any case [28]. Therefore, no special infrastructure, maintenance or administrative costs are expected in order to generate or keep information up to date, nor even to have access to the information about the used or free frequencies. Keeping information at network level also means that no single point of

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control (and consequently also single point of failure) exists. However, it can be argued that in addition to licensed users, sensing also protects unlicensed users from interference and this protection is not a requirement (i.e. unlicensed users must accept any interference caused by licensed or unlicensed users). Therefore, performance costs could arise from the information overload present in the system. Still, in the longer term, this awareness of the environment including unlicensed users, could actually become an advantage in case inter-technology interference arises within unlicensed users. For instance, coexistence of several wireless technologies (e.g. Wi-Fi, Bluetooth, ZigBee) in the unlicensed ISM band in specific environments (e.g. industrial setting) creates increasing inter-technology interference because there is no coordination among radios [9][10][11][12], thus preventing a straightforward deployment of wireless technologies and societal benefits. Although distributed sensing could still suffer from fading, pathloss and shadowing effects there are no specific detection errors that distinguish sensing operating indoors or outdoors.

It can also be argued that with distributed sensing harmonization of WSD and the whole solution could be achieved faster and within limited costs, since no special architecture, coordination between stakeholders and additional policy decisions are required. In addition, standardization of WSD and protocols would follow the usual standardization tracks.

Distributed sensing could however suffer from device misconfigurations and security breaches. On one hand, a misconfigured device could wrongly detect free frequencies and report them to neighboring devices. On the other hand, a WSD intruder could, for instance, maliciously report false spectrum detection results, such as reporting a frequency as free or as occupied [25].

Summarizing, the drawbacks of local sensing have been extensively identified in literature and studies. However, a distributed sensing solution could be achieved with low-cost low-power chips delivering database-equivalent performance and no distinguishable indoor/outdoor performance. We anticipate that harmonization and standardization processes could be achieved faster, with limited resources and costs, since no coordination between stakeholders is required or single point of control exists. Business configurations relying on a Sensing Service Manager (SSM), such as the one proposed by Weiss et al. [26], could however be foreseen. The SSM would have global knowledge of all radios operating in a region and thus a superior local knowledge.

B. Business Implications of geo-location database In a geo-location database solution, a WSD selects a

database and sends parameters describing its location and device attributes. The database then returns details of the frequencies and power levels the WSD is allowed to use. At this point the WSD can start transmitting. In a geo-location database solution, WSD would still be more expensive than current wireless devices in the market, as they would need to include a geo-location component. For instance, according to FCC rules, Mode I devices (with no direct connection to the

database) must receive regular signals from Mode II devices (fixed location devices) that previously provided their current list of available channels to verify that they are still in reception range of that device or contact a Mode II device at least once per minute to re-verify/re-establish channel availability. Because of that, geo-location accuracy will be limited, since Mode I devices will have to rely on the geo-location of Mode II devices. In section III, we concluded that all these assumptions and geo-location accuracy (indoors vs outdoors) will have an important impact on spatial reuse performance of the geo-location database. The drawbacks of the geo-location database have, however, not yet been studied extensively.

The database itself contains information about licensed usage, typically the frequencies and channels currently being used by DTT and microphones. How this information is going to be generated it is still not clearly defined by regulators. In addition, if there are multiple White Spaces Database Providers (WSDP) it is important that one entity controls and monitors that every database contains the same information. Updates should also be reported to this central entity and replicated to all databases. This seems to be the chosen approach by Ofcom (Fig. 9). In this case, Ofcom is also proposing to maintain a database with references to all WSDP databases. Therefore, a WSD should contact the Ofcom database first and only afterwards obtain free frequencies from a WSDP database.

Figure 9. Geo-location database access infrastructure proposed by Ofcom

There are however pertinent questions to be answered about a similar architecture to be maintained by a regulator:

• Will WSD need to be registered to be able to have access to the regulator’s list of third-party databases?

• Who will fund a regulator’s database containing the list of databases as well as the DTT coverage and PMSE usage databases? Who will cover the costs of updating information as well as IT operational costs?

• Will WSDP have to pay to be listed in the regulator’s list of databases? Who will insure third-party database availability?

• Will the regulator police if a WSDP is operating the database in accordance with regulation?

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In principle, registration of WSD should not be needed since WSD are operating on a license-exempt basis, thus relieving any obligation for any type of registration. Fixed location WSD (or Master, as in Fig. 9) could however be required to register. Regarding the costs involved with the regulator’s databases, one could argue that these will be supported by the revenues of licenses currently issued to broadcasters and PMSE license holders. Although this assumption seems to be to a certain extent reasonable, one may question why would license holders pay for the provision of a free service to unlicensed users (that some argue may even generate harmful interference). On the other hand, regulators could issue licenses to WSDP interested in providing database services. In this case, the regulator would fund the previously mentioned infrastructure by issuing time-limited licenses or just charge an annual fee. A workaround to ensure availability could consist in having multiple WSDP or forcing WSDP to ensure database redundancy, at the cost of increasing infrastructure and maintenance costs.

In addition, one could question who could and what are the incentives to become a database provider and in that case if the WSDP could charge the TVWS user for accessing the database. In principle, it would be difficult to charge white spaces users (WSU) a subscription or membership to access the database, since these users are unlicensed in nature, and possibly nomad. But, at least four possible scenarios could be envisioned for different types of stakeholders taking the role of WSDP: 1) a closed or small community, such as a university or municipality; 2) a device manufacturer; 3) a telecom operator or a new entrant operator; and 4) independent third-party/broker.

In the first scenario, the WSDP would fund the database and incur infrastructure and maintenance costs typically from the own community’s budget. The database would only contain data related to the geographical locations covered by the network infrastructure and area of the region. The database would be closed for the users of the community and requests to the database would be free, or as part of the cost of the service provided by the community. In this case, the white spaces service provider and WSDP would be the same entity.

The second scenario would also be a closed scenario, but in this case the geographical coverage could be country-specific or even global. The manufacturer would only provide access to its manufactured devices. The manufacturer would also incur infrastructure and maintenance costs but these would be funded by the devices sold to consumers or white spaces service providers. Keeping up with different country rules and administrative costs would in this case be a challenge; therefore, harmonization would play an important role for the success of this scenario.

In the third scenario, a WSSP would provide data or voice services over TVWS. As part of its service solution, the WSSP would also host a white spaces database. Access to the database would only be granted to the WSSP clients. Hence the infrastructure and maintenance costs would be part of the whole service costs and clients would not be charged a separate fee or subscription. Like in the previous case, the WSSP could provide a countrywide service or cover multiple countries. This

scenario could, for instance, on a country-level be extended to include a consortium of different WSSP that together would fund the database costs.

The fourth scenario would be the only open solution, since any WSSP or WSD could access the database. Although this solution would also be neutral in dealing the same way with all types of devices and services, one could argue it is difficult to justify the involvement and interest of a stakeholder, unless there are financial benefits involved1. To be able to cover for infrastructure and maintenance costs, the WSDP could charge consumers or WSSP on the basis of WSD registration, subscription fee or per-query fee. The latter pricing model would probably incur high costs for the consumer or WSSP and discourage TVWS use.

To conclude, although the geo-location database approach has been chosen by US and UK regulators, there are still many questions related to the configuration of the business ecosystem to be answered. Although further research is needed, we expect database performance to also be impacted by performance issues (in particular, originated by location accuracy issues). From a broader business perspective, the geo-location database solution seems to incur in high infrastructure, maintenance and administrative costs spread among several stakeholders.

VI. CONCLUSIONS Both distributed sensing and geo-location database have

benefits and limitations. However, we intended to make clear that a distributed sensing solution would perform better and consume less energy than a local sensing solution. Putting together the hardware costs of white spaces devices and their energy costs, we could claim that for equivalent performance distributed sensing and geo-location have negligible differences. Both techniques can most likely reuse the incorporated transceiver for the wireless communication for the sensing capability or for the database access.

In terms of throughput and power consumption overheads of the distributed sensing and database approach, it is difficult to draw conclusions without a specific instance or implementation in mind. The results would depend on the frequency of sensing/database access. When comparing local and distributed sensing, it is clear that the costs of local sensing could be very high, since the sensing requirements could be too hard to be met with a single sensing device. As a result, it is motivated from a technical point of view that distributed sensing should be considered. With distributed sensing, the throughput and energy overheads become reasonable, thus one could expect this technique to be implemented. The throughput and energy cost of accessing the database is expected to be reasonable as well, since it involves only some more communication overhead for the device. The cost of this can never be so high that the technique becomes unfeasible.

In terms of spatial reuse, it is argued that the efficiency of the database approach should be studied further to compare in more detail the performance of the cheap and expensive database. Preliminary simulation results based on a simple

1 However, Google proposed to operate a white spaces database publicly accessible and searchable at no charge for the public [18].

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model reveal that the cheap database could achieve a much lower spectral efficiency when location accuracy is low. This is especially the case when considering indoor communication. However, for the expensive database implementation (that would require extensive in-field measurements) the spatial reuse opportunities would be higher.

Regardless of how sensing engines and geo-location capabilities are implemented, for the geo-location database solution the regulator (or its associated party) and the White Spaces Database Provider will incur infrastructure costs (setting up database infrastructure, backups, redundancy and IT support capabilities) and maintenance costs (database updates and data accuracy checks). In addition, administrative costs can also be identified: 1) White Spaces Service Providers and consumers could be charged by White Spaces Database Providers a subscription fee, a registration fee for fixed WSD or a per-query fee; 2) White Spaces Database Providers could be charged by the regulator a license fee or an annual fee to have their databases registered in the regulator’s database.

In distributed sensing, there are hardly any infrastructure and maintenance costs, since used frequencies and channels are kept in the network and no other form of infrastructure is required. Since there is no requirement for any coordinated activity among licensed and unlicensed users, WSD users would not incur administrative costs. Hence, we are of the opinion that the distributed sensing solution in the context of TVWS does indeed present value and its business and technical impact should be considered in further research and regulatory activities.

Based on the technical model and the related simulation

results as well as the expected business implications, we summarize and qualify the expected costs in Table I.

TABLE I. QUALITATIVE IDENTIFICATION OF COSTS FOR LOCAL SENSING, DISTRIBUTED SENSING AND GEO-LOCATION DATABASE

Technique Cost

Local Sensing Distributed Sensing

Geo-location Database

Hardware Low (reuse of transceiver)

Low (reuse of transceiver)

Low (reuse of transceiver)

Energy High for sensing performance

Reasonable Reasonable

Throughput High for sensing performance

Reasonable Reasonable

Spatial reuse Depends on sensing performance

Depends on sensing

performance

Depends on geo-location

performance

Infrastructure Low to inexistent

Low to inexistent Reasonable

Maintenance Low to inexistent

Low to inexistent Reasonable

Admnistrative Low to inexistent

Low to inexistent Low/Reasonable

ACKNOWLEDGMENT

The research leading to these results has received funding from IWT under the SBO project ESSENCES and IBBT under the project NGWINETS.

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