Dynamic Radio Resource Management algorithms for an efficient use of TVWS

COGEU D6.1 – Dynamic Radio Resource Management Algorithms for an efficient use of TVWS

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COGEU

FP7 ICT-2009.1.1

COgnitive radio systems for efficient sharing of TV white spaces

in EUropean context

D6.1

Dynamic Radio Resource Management algorithms for an efficient use of TVWS

Contractual Date of Delivery to the CEC: December 2010

Actual Date of Delivery to the CEC: 12 January 2011

Author(s): Athina Bourdena (AEGEAN), George Kormentzas (AEGEAN), George

Mastorakis (AEGEAN), Evangelos Pallis (AEGEAN), Paulo Marques (IT), Joseph Mwangoka

(IT), Jonathan Rodriguez (IT), Jerzy Kubasik (PUT), Hanna Bogucka (PUT), Marcin Parzy

(PUT), Georg Schuberth (IRT), Álvaro Gomes (PTIN), Helder Alves (PTIN)

Participant(s): AEGEAN, PTIN, PUT, IRT, IT

Workpackage: WP6

Est. person months: 18

Security: PU

Nature: Report

Version: 1.0

Total number of pages: 90

Abstract:

This Deliverable, D6.1, reports the results of Task 6.1 which addresses the development of dynamic

radio resource management algorithms for an efficient use of TVWS. In this context, the overall

objective of D6.1 is to design, simulate and evaluate a TVWS allocation mechanism, able to be

adopted in spectrum broker entity of COGEU demonstrator. Towards this main objective, an overall

TVWS allocation process is proposed including matching optimization and spectrum auction. The

evaluation results validated the performance of the proposed algorithms for an efficient allocation of

TVWS in COGEU secondary systems.

Keyword list: TVWS, RRM, Matching algorithms, Spectrum auction, Dynamic Spectrum

Management


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Executive Summary

This deliverable reports the work performed in Task T6.1: “Dynamic Radio Resource Management algorithms”. The main objective of T6.1 is to design, develop and evaluate allocation techniques of TVWS for COGEU secondary systems. This task investigates Radio Resource Management (RRM) algorithms that use context information determining the TVWS bands and power level at which a secondary user should be allowed to operate to avoid harmful interference with incumbent systems, minimize spectrum fragmentation and optimize QoS. The proposed allocation techniques will be integrated in the COGEU spectrum broker demonstrator developed in WP7. COGEU considers the two spectrum sharing regimes for TVWS in Europe: the spectrum commons (unlicensed) and secondary spectrum trading (managed by a centralised spectrum broker). One essential difference between them is that with the spectrum broker a certain quality of service may be guaranteed to the licensee. In fact, QoS guarantee and the profitability of the broker are two important conditions for a viable COGEU business model. This Deliverable concentrates on the RRM issues related to the operation of the spectrum broker. Key achievements and conclusions

A methodology is proposed in order to compute TVWS availability and populate the geo-location database. Preliminary results of the available TVWS channels investigation in Munich area are presented and used as a case study scenario in the performance evaluation of the TVWS allocation techniques. The TVWS are the radio resources to be managed by the COGEU broker.

The COGEU broker is in charge of assigning the access to TVWS spectrum under real time secondary spectrum market regime. It incorporates a process of optimally allocating spectrum to secondary systems taking into account matching optimization methods, spectrum pricing and spectrum auction methods.

A spectrum broker allocation process is designed and presented for an efficient radio resources allocation in COGEU secondary spectrum market. The key blocks of the allocation process are: Internal spectrum broker databases, Benchmark price estimation process, Matching algorithm between demand and TVWS offer and the Auction module.

Two internal spectrum broker databases are identified: The TVWS occupancy repository and the Spectrum policies repository. The TVWS occupancy repository is the unit that contains all the information where TVWS devices may transmit and also contains a database on active TVWS devices and their operational parameters. The spectrum policy database manages the spectrum trading policies of the regulator which include priorities, restrictions, etc.

A part of the overall proposed allocation process is the estimation of benchmark price of the available spectrum based on an Administrative Incentive Pricing (AIP) mechanism. As a result of the benchmark price estimation is the definition of spectrum-unit price in the overall process. In this context, an AIP based algorithm is proposed and developed towards TVWS price definition and methods of estimating opportunity cost are analyzed.

A matching algorithm based on Backtracking process is designed, simulated and evaluated in order to match spectrum supply from a TVWS pool and spectrum demand from secondary systems. This algorithm is applied in cases, where spectrum demand is lower than spectrum supply in the overall allocation process adopted by the COGEU broker. In this case, a fix price per MHz, defined by the benchmark price estimation process is used.

In COGEU broker, the Backtracking algorithm with pruning is used to represent all possible arrangements in the TVWS pool that matches the secondary systems demand. The available solutions are ranked based on the size of contiguous remaining white-spaces and favour those that provide for tighter allocation of services in order to allow for additional services in future deployments.


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A COGEU scenario that adopts preliminary studies of TVWS availability in Munich is considered in order to illustrate and test the matching algorithm. The test scenario includes time dimension, allowing secondary systems to request TVWS access and enter into the allocation process every “Time period”. The performance of the matching algorithm is evaluated by taking into account several metrics such fragmentation, TVWS utilization and complexity.

As secondary systems enter and exit a market place over time, the available TVWS channels become increasingly divided into a collection of discrete fragments. This “spectrum fragmentation” means that a significant portion of spectrum, while free, is effectively unusable because its fragments do not provide the minimum contiguous spectrum range required by new flows of secondary users. This problem is taken into account when investigating matching techniques.

The simulation results obtained confirmed the efficiency of the proposed matching algorithm to optimally allocate TVWS to secondary systems taking into account all constraints defined in the specifications of the use case scenario (allocation time, frequency bandwidth and transmit power). According to simulation results, it is clear that pruning technique provides smaller number of solutions explored, i.e., low complexity, at the cost of some degradation in terms of fragmentation.

The TVWS allocation based on auctions is used for the case of the spectrum demand exceeding the spectrum offer, when not all the requests can be satisfied. This approach selects a subset of the secondary users to be allocated the spectrum, and emphasizes the economic aspect of dynamic spectrum access.

A practical solution, for implementation of the combinatorial auction with non identical objects in TVWS context using the bandwidth and power requirements of the secondary users is proposed. Spectrum allocation problem is defined as an optimization problem where maximum payoff of the spectrum broker is the optimization target. This target can be reached due to the branch-and-cut algorithm presented. The branch-and-cut technique is used to solve the considered auction with non identical objects for contiguous and non contiguous spectrum in order to limit the number of computations. The English sealed-bids first price auction is considered.

The following metrics are used to evaluate the spectrum allocation process with the auction: sum of players‟ demands, spectrum-auction efficiency, spectrum-broker payoff, the value of sold 1 MHz and user‟s satisfaction rate.

Simulations are provided in COGEU Munich scenario for LTE network operators (players) which are interested in buying spectrum for their end users. They may demand 5, 10 or 20 MHz. Simulations results confirmed that this solution is very efficient in case of spectrum broker‟s profit maximization and spectrum efficiency. Although, due to the fragmentation of the available bandwidth the maximum spectral efficiency is hard to reach so the rest of the spectrum for temporary access of other narrow band applications such as M2M and smart metering applications.

Maximum spectrum-auction efficiency is possible only when demands are equal or higher than offered spectrum and desired spectrum products are suited to the available spectrum blocks.

Simulation results showed that user satisfaction rate (defined as number of winning auctions divided by number of auctions with this player) is strictly dependent on the available spectrum (more spectrum, lower competition) and on the valuation of the spectrum.

The presented spectrum allocation process based on auctions may be easily adopted for future players‟ demands based on any wireless standard and application requirements.

Radio Resource Management (RRM) procedures external from the operation of COGEU broker are investigated, i.e. after the allocation process of temporarily exclusive rights of TVWS to secondary users. In particular, RRM associated with the LTE extension over TVWS


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is investigated. The RRM procedure aims at the provision of guaranteed QoS to mobile subscribers of an LTE operator acting as a broker‟s customer and a secondary spectrum user.

Using a joint optimization of radio resources from LTE-TVWS carriers (acquired from the COGEU broker) and LTE-legacy carriers, it is feasible to reduce the number of BS‟s, providing the same or even higher throughput. This is important to both the network operator and the users: to the network operator, because it decreases the CAPEX (Capital Expenditures) and OPEX (Operational Expenditures); and to the users, since they can experience better services.

As the TVWS carriers allocation by the COGEU broker is temporary and location based, new functionalities need to be added to the cellular system to support this dynamic behavior such as: network monitoring, TVWS carrier‟s assessment, request to the broker, carriers allocation and user allocation.

The proposed modifications try to incorporate specific functionalities required to use TVWS carriers with minor changes of the standard LTE 3GPP architecture and protocols.

Two algorithms are proposed where the radio link quality on each band (TVWS and legacy) is periodically monitored and the amount of RRB (Radio Resource Blocks) needed to provide the requested service evaluated. The algorithms decide on which carrier the mobile user \ service will be allocated.

Preliminary simulation results show that TVWS can provide higher capacity and radio coverage for overloaded LTE cells. Namely for the 2 GHz legacy bands the radio coverage probability is 94% and the average throughput 13.01 Mbps, while for 700 MHz (TVWS) the coverage probability is increased up to 100% and the average throughput to 14.75 Mbps, more than 1.74 Mbps on average, which represents a significant increase in system capacity.


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Table of Contents

1- Introduction .......................................................................................................... 8

1.1- Association with other COGEU activities .......................................................................................... 9

1.2- TVWS allocation and RRM integrated in the COGEU reference model ...................................... 10

1.3- Objectives of TVWS allocation ......................................................................................................... 12

1.4- Example of TVWS allocation ............................................................................................................ 14

2- TVWS availability information and protection requirements .......................... 16

2.1- COGEU methodology to compute TVWS availability .................................................................... 16

2.2- Preliminary COGEU results of TVWS availability in Germany ................................................... 17

2.3- How to realize mutual protection between the TVWS secondary users? ...................................... 19

2.3.1- Rules for interference protection .................................................................................. 20

2.3.2- Technical requirements................................................................................................ 20

2.4- Conclusion ........................................................................................................................................... 21

3- Spectrum broker allocation process ................................................................ 22

3.1- Internal spectrum broker databases ................................................................................................. 24

3.1.1- TVWS occupancy repository ....................................................................................... 24

3.1.2- Spectrum trading policies repository ............................................................................ 25

3.2- Benchmark price estimation based on administrative incentive pricing ....................................... 26

3.2.1- Background .................................................................................................................. 26

3.2.2- Spectrum users and congestion .................................................................................. 26

3.2.3- AIP based Algorithm .................................................................................................... 27

3.2.4- The Principle of Opportunity Cost ................................................................................ 30

3.2.5- Methods of Estimating Opportunity Cost ..................................................................... 31

3.2.6- Estimation of opportunity cost for TVWS bands .......................................................... 34

3.2.7- Conclusion ................................................................................................................... 36

3.3- TVWS allocation based on matching optimization ......................................................................... 37

3.3.1- Background of backtracking ........................................................................................ 37

3.3.2- Backtracking implementation ....................................................................................... 42

3.3.3- Backtracking integrated in the spectrum broker allocation process ............................ 44

3.3.4- Performance evaluation ............................................................................................... 45

3.3.5- Conclusion ................................................................................................................... 52

3.4- TVWS allocation based on auctions ................................................................................................. 54

3.4.1- General description and assumptions ......................................................................... 54

3.4.2- Branch-and-cut algorithm for the auction solution ....................................................... 59

3.4.3- Performance evaluation ............................................................................................... 59

3.4.4- Conclusion ................................................................................................................... 66

4- RRM for LTE extension over TVWS .................................................................. 67

4.1- RRM problem formulation ............................................................................................................... 68

4.2- Algorithm formulation ....................................................................................................................... 70

4.3- Simulation framework ....................................................................................................................... 72

4.4- Initial performance evaluation .......................................................................................................... 75

4.5- Conclusion ........................................................................................................................................... 77

5- Conclusions and future work ............................................................................ 78

References .............................................................................................................. 81


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1- Introduction

The objective of Radio Resource Management (RRM) in a general context is to utilize the limited radio spectrum resources and radio network infrastructures as efficiently as possible. RRM also concerns multi-user and multi-cell network capacity issues, rather than point-to-point channel capacity. RRM is especially important in systems limited by co-channel interference rather than by noise, for example cellular systems and broadcast networks homogeneously covering large areas, and wireless networks consisting of many adjacent access points that may reuse the same channel frequencies. In a general context, dynamic RRM schemes adaptively adjust the radio network parameters to the traffic load, user positions, quality of service requirements and are considered in the design of wireless systems, in view to minimize expensive network planning resulting in improved system spectral efficiency.

COGEU spectrum measurements campaign reported in D4.1 resulted in a conclusion, that even in urban areas such Munich there are under-utilized TV spectrum in UHF bands. These channels may be utilized in the context of COGEU for possible usage taking into account methods for an optimum allocation process incorporated in the proposed system as well as methods such as spectrum pooling. In a general context, the notion of spectrum pooling [2] basically represents the idea of merging spectral ranges from different spectrum owners into a common pool. It reflects the need for a completely new way of spectrum allocation as proposed in [3], [4]. From a common spectrum pool hosted by a system, public rental systems may temporarily rent spectral resources. According to this concept rental users obtain access to spectral ranges they have not yet been allowed to use, and the actual spectrum owners can tap new sources of revenue. A multitude of juridical and economic consequences occurs when implementing the idea of spectrum pooling in a real system.

In the context of TVWS, the concept of spectrum pooling will be adopted by COGEU, which makes use of a geo-location database storing information regarding TVWS availability for possible secondary usage. Figure 1 presents an example of available TVWS channels and their maximum allowed transmit power levels in a specific location, i.e., the kind of radio resources to be managed by the techniques proposed in this deliverable.

Figure 1 Example of available TVWS resources in a specific location (COGEU spectrum pool).

This deliverable proposes an overall TVWS allocation process that may be adopted by the COGEU spectrum broker entity in the final demonstrator. Towards validating the proposed approach, TVWS allocation algorithms were designed, simulated and evaluated in order to ensure the provision of an optimal allocation of the available channels to COGEU secondary systems. The objective is to provide QoS for secondary systems operation over TVWS, maximize persistence of allocations avoiding harmful interference with primary systems (i.e. DVB-T and PMSE systems). More specifically, persistence of allocations is essential to the stability of the overall COGEU network architecture, reducing the overhead cost of spectrum allocation system and minimizing the risk for future spectrum thrashing, the need for additional spectrum negotiations and reconfiguration delays. The proposed approach regarding TVWS allocation is adopted in a network topology with a centralized broker, as it is defined in COGEU reference model presented in D4.1.

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TV CHANNEL

Ma

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um

allo

we

d p

ow

er


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Towards this main objective, TVWS allocation problem is formulated as an optimization problem from the technical point of view (matching problem with interference constrains) and as an optimization problem from an economic point of view (maximization of COGEU broker‟s profit). The overall COGEU TVWS allocation process is presented in three phases: preparation and analysis, operation and maintenance. In this process, the key building blocks are identified and specified.

A part of the overall proposed allocation process is the estimation of benchmark price of the available spectrum based on an Administrative Incentive Pricing (AIP) mechanism. As a result of the benchmark price estimation is the definition of spectrum-unit price in the overall process. In this context, an AIP based algorithm is proposed and developed towards TVWS price definition and methods of estimating opportunity cost are analyzed. The key point in this research work is the challenge to set the price of spectrum in order to reflect the social value of the resources as well as the underlying signalling mechanism in order to enable such a framework to operate seamlessly.

A part of the overall allocation process is also a matching algorithm based on backtracking process [12], which was designed, simulated and evaluated in order to match spectrum supply from a TVWS pool and spectrum demand from secondary systems. This algorithm is applied in cases, where spectrum demand is lower than spectrum supply in the overall allocation process adopted in COGEU broker. The implementation of the backtracking algorithm for the COGEU case is presented including all parameters and constraints taken into account. Simulation framework is also presented and algorithms functions are analysed in order to solve the TVWS matching problem.

Additionally to the process based on backtracking, a TVWS allocation based on auctions is presented in the case, where spectrum demand is higher than spectrum supply. General description and assumptions are presented and this problem is formulated taking into account this approach. An algorithm for the auctions solution is designed, implemented, simulated and evaluated.

Additionally, to the overall allocation process, radio resource management procedures external from the operation of COGEU broker are investigated. According to this process, the LTE network takes advantage of TVWS extra carriers acquired through the TVWS allocation mechanism implemented by the COGEU broker. RRM algorithms for combining legacy LTE carriers with extra LTE over TVWS carriers are proposed. A simulation framework is presented and initial results evaluating the impact of extra LTE TVWS carries in the LTE network is analysed and discussed. In this framework, next subsections of this introductory section elaborate on the association of this deliverable with other COGEU activities, RRM issues integrated in the proposed COGEU network architecture, the overall objectives of TVWS allocation in the context of COGEU and an illustrative example of TVWS allocation. These issues are the basis for the work presented in next chapters of the deliverable.

1.1- Association with other COGEU activities

This deliverable D6.1 is associated with the research work undertaken in T3.1 regarding COGEU technical requirements, and T4.1, T4.2 regarding TVWS information and specifications for the geo-location database. The results obtained from these tasks were valuable and gave an input to address the spectrum allocation problem in the spectrum broker entity, which is investigated in T6.1. T2.2. has also been crucial for the work undertaken in this deliverable. TVWS allocation process has to satisfy the spectrum trading policies in the considered area (i.e. state or country) which include regulations, priorities, restrictions etc. The results and the work presented in T6.1 will be important for the implementation of the spectrum broker entity used in the overall COGEU network architecture of WP7. The following diagram presents the association and relevance of this deliverable with other COGEU activities.


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TVWS availability Geo-location

database(T4.1 + T4.2)

COGEU system requirements

(T3.1)

RRM (T6.1)

Implementation RRM Module

(T7.2.3)

TVWS spectrum policies(T2.2)

Figure 2 Association diagram of D6.1 with other COGEU activities

More specifically T3.1 analyses the proposed use-cases and system requirements for COGEU network architectures. In this task three COGEU scenarios are considered because they represent the spectrum sharing regimes investigated by COGEU: spectrum commons, secondary spectrum trading and prioritization mechanisms for public safety applications (see Table 1).

System Spectrum Regime

LTE extension over TVWS Secondary spectrum market

WiFi with cognitive access to TVWS Spectrum commons

Public safety applications with

cognitive access to TVWS

Secondary spectrum market with prioritization

mechanisms.

Table 1 Selected COGEU use-case scenarios

On the other hand, T4.1 characterizes spectrum opportunities in DVB-T bands through spectrum occupancy measurements and interference analysis in different locations. Furthermore, T4.2 specifies the COGEU TVWS database that enables the broker to determine which DVB-T channels are available, before starting the TVWS allocation process investigated in T6.1 and reported in this deliverable. Next subsection presents the COGEU reference model defined in D4.1, analyzing specific modules and elaborating on how RRM issues are integrated in it.

1.2- TVWS allocation and RRM integrated in the COGEU reference model

In COGEU different spectrum regimes will be utilized able to support sharing and/or trading of spectrum resources. More specifically, spectrum commons regimes promote sharing, but do not provide adequate quality of service for some applications. For COGEU applications that require sporadic access to spectrum and for which QoS guarantees are important, licensed spectrum with real-time secondary markets is the best solution. Trading also, allows systems to directly trade spectrum usage rights, thereby establishing a secondary market for spectrum leasing and spectrum


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auction. This model has the potential to enable small companies to enter the spectrum market, have access to TVWS and be charged based on spectrum utilization, thus boosting competition and innovation in the telecommunications sector. Unlike today‟s unlicensed bands, primary and secondary systems would coordinate directly, making it possible to protect the QoS for both primary and secondary ones. In this explicit coordination, the license holder runs an admission control algorithm, which only allows secondary systems to access the spectrum when QoS of both primary and secondary ones are adequate. The license-holder also uses an intelligent frequency assignment algorithm for determining the frequency at which a secondary system should be allowed to operate and the economics of such transactions which provides incentives to maximize spectrum utilization. Secondary systems dynamically request access to spectrum when and only when spectrum is needed. The reference model of COGEU architecture was proposed in D4.1 and is presented again in Figure 3. The geo-location database includes information regarding the number of available TVWS channels and their characteristics in terms of transmission power levels. The geo-location database also includes records regarding channels utilized by incumbent systems such as DVB-T and PMSE, which must be protected from a possible secondary system transmission. In this network architecture, different secondary systems are operating with different priority in terms of TVWS access. For instance, secondary systems may opportunistically utilize the available TVWS channels defined in geo-location database. These systems are treated as low priority systems without supporting guarantee QoS through the “spectrum commons” regime (see Figure 3). It is envisioned that some spectrum will be designated to the “spectrum Commons” and the broker will not deal with managing or trading it. On the other hand, trading of the spectrum for the secondary market occurs through intermediaries such as the spectrum broker (see Figure 3). The spectrum broker as it is proposed by COGEU is in charge of assigning the access to TVWS spectrum under real time secondary spectrum market regime. It incorporates a process of optimally allocating spectrum to secondary systems taking into account optimization methods, spectrum pricing and spectrum auction methods. This deliverable specifically focuses on this radio resource allocation managed by the broker.

TVWS Devices

Geolocation

Spectrum

Database

DVB-T & PMSE

COGEU

Broker

Spectrum Commons

(Unlicensed)

Secondary spectrum

market operation

Regulator Policies

Figure 3 COGEU network architecture concept

As it is anticipated by COGEU, a centralized topology is adopted with a spectrum broker trading with secondary systems. The COGEU network configuration adopting spectrum market model with a centralized broker is shown in Figure 4.


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TVWS

area

BROKER

GEOLOCATION

SPECTRUM

DATABASE

TVWS

AP AP

PLAYER 1

(RRM LTE)

Price discovery strategy

CR + GPS

CR+GPS CR+GPSCR+GPS

CR+GPS

...

Negotiation protocols

PLAYER 2 PLAYER N

TVWS allocation

mechanism

DVB-T

PMSE

Figure 4 Overview of COGEU network configuration. The “pink” blocks are where D6.1 contributions are placed.

In Figure 4 “pink” blocks are where D6.1 contributions are placed. More specifically, “TVWS allocation mechanism” block incorporates the matching algorithm between the TVWS pool provided by the geo-location database and the spectrum demand (players). The “price discovery strategy” block is associated with methods regarding spectrum price discover and spectrum auctions as they are proposed in this deliverable. Finally, in “PLAYER 1 (RRM LTE)” block, radio resource management procedures are adopted which are external from the operation of COGEU broker. These RRM procedures aim at the provision of guaranteed QoS to mobile subscribers exploiting LTE over TVWS carries. In this case, the LTE operator acts as a COGEU broker‟s customer.

1.3- Objectives of TVWS allocation

The first general objectives of the spectrum allocation are the maximization of TVWS utilization and the minimization of spectrum fragmentations. In a general context as secondary systems enter and exit a market place over time, the available TVWS channels become increasingly divided into a collection of discrete fragments. This “spectrum fragmentation” means that a significant portion of spectrum, while free, is effectively unusable because its fragments do not provide the minimum contiguous spectrum range required by new flows of secondary users. This problem will be taken into account when investigating allocation techniques. In the broker model, an important issue that has to be considered is to keep QoS of secondary systems, avoiding interference between secondary systems using TVWS and facilitating peaceful coexistence at secondary level. In this context, Radio Resource Management is responsible of the efficient utilization of TVWS resources and guaranties QoS. An additional target of spectrum allocation is to maximize the economical efficiency of the spectrum broker operation by adopting algorithms able to offer optimized results in terms of spectrum trading. Guarantee QoS to secondary systems over TVWS and the profitability of the broker are two important conditions for a viable COGEU business model. There are a number of parameters/factors which influence spectrum allocation in the context of COGEU use cases. These factors have to be taken into considerations during the TVWS allocation


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process in order to achieve maximum efficiency in secondary spectrum usage. Moreover, these factors reflect different users‟ requirements for wireless access. They include (but not limited to):

emitted power which also influences the overall system electromagnetic radiation – in that case the higher the power the higher the system electromagnetic radiation in-band and out-of its nominal band, and consequently the higher the potential for interference with other users (albeit the incumbent system)

system‟s bandwidth requirements

spectrum price and valuation

path length in relation to RF properties of the system‟s operating frequency

band popularity factor, whether it falls within the frequency “sweet spot” range or not

the congestion tolerance level of the system – whether it can share resources or use them exclusively

the location factor – whether the system is in urban, suburban, or rural areas

the conditions for using the license

priority during the time of emergency

Incentives to encourage or discourage targeted frequency band usage etc [32].

Table 2 represents these individual factors that influence the usage of TVWS in COGEU use cases [32].

Factor/ Parameter Explanation

Min Power Higher availability requires higher radiated power levels, which is an

opportunity cost for other users.

Bandwidth Directly proportional to the link bandwidth in MHz but with a preset minimal

value, e.g. 1kHz or 1MHz.

Spectrum Price Price of each unit link bandwidth: x € per 2 x 1MHz. This spectrum price is

determined based on spectrum demand for a specific frequency and geographical location.

Path length factor

Adjusts license fees to encourage short links to move to higher bands, thus retaining lover bands for longer links that would not be technically possible in the higher bands. The algorithm may differentiate the charges for links with shorter than the minimum path length or over-exceeding the path length.

Fragmentation factor

Fragmentation factor defines the optimal spectrum usage over time in order to avoid that TVWS will be divided into discrete fragments.

Band factor Band usage (Highly popular, Medium popular and Less popular bands). This factor adjusts the license fees to encourage a general use of higher bands.

Congestion tolerance

Measure a user‟s demand for service quality by his or her congestion limit, expressed as the maximum amount of spectrum congestion a user can

tolerate before the value he or she places on employing spectrum falls from some desired value to zero.

Location factor Population (High, Medium, and Low population).

License terms Long-term lease, a scheduled lease, and a short-term lease or spot markets.

Priority (additional adjustments)

Type of antenna used, like directional creates space for other links, so could acquire spectrum at cheaper prices. During disaster, all systems should be in emergency mode, so no charges at that time – hence the adjustment factor

adds some degree of manipulation to the spectrum band – which could increase spectrum efficiency by attracting or repelling services in certain

bands or regions.

Table 2: Explanation of the factors and parameters influencing TVWS usage in COGEU use-cases

The factors above will be considered in the COGEU spectrum allocation approaches based on the intended use, region and time frame.


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Table 3 gives a list of the parameters or factors which influence the COGEU use cases in relation to service requirements and spectrum demand/requirements.

Parameter/ Factor Use cases

WiFi Public Safety LTE

Min Power 100mW 1W 4W

Bandwidth 22MHz 1 MHz 10 MHz

Access Duration

of the 1 day 4 days 2 hours\ day

Spectrum Price Free price K price X

Path length

factor\Coverage

area

Short – medium Medium to long Medium to long

Congestion

tolerance High Very low Flexible

Location factor High population Anywhere Flexible

License terms long-term lease, a

scheduled lease spot markets

long-term lease, a

scheduled lease, and

a short-term lease or

spot markets

Priority

(additional

adjustments)

Best effort

High priority during

emergence

otherwise dormant

Always connected

Table 3: Parameters or factors which influence COGEU use cases in TV white spaces usage (figures for illustrative propose)

1.4- Example of TVWS allocation

This sub-section gives an example to illustrate the TVWS allocation process in general. This process includes 3 phases as described below. In the first phase, the broker gets TVWS availability information from the external geo-location database. Figure 5 illustrates a TVWS pool valid in a specific geographical area. The white spaces opportunities are actually the “no-black” blocks. Each block has a allowed transmit power level represented by its colour. The “red blocks” are those where the allowed transmit power is higher. Note that in this example, channels 44, 45 and 46 are reserved by the regulator for unlicensed use (spectrum commons – free access), therefore they are out of the market. The “black blocks” are not allowed for White Space Devices (WSD) because they are occupied by incumbent systems such as DVB-T and PMSE systems. The second phase is the analysis of spectrum demand. The following systems (market players) are considered in this example, requiring temporally exclusive access to TVWS channels:

Telemetry (smart metering) system operating during the night period (1 MHz);

One FDD LTE carrier (2x5 MHz) during the day period with macro cell coverage;

One FDD LTE carrier (2x10 MHz) continuously with micro cell coverage.


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TV CHANNEL

TIM

E

Not allowed

(Incumbent Systems)

Allowed power level

Channel 44,45 and 46 are

reserved to Unlicensed Use

Figure 5 TVWS supply valid in a specific geographical region (Spectrum pool).

The 3rd phase is the TVWS allocation process based on matching algorithms and price discovery strategies. Frequency, time and power dimension have to be taken into account during the TVWS allocation process. Figure 6 illustrates an allocation profile of TVWS as a possible solution for this optimization problem. For simplicity, the granularity considered in this example is one TV channel (8 MHz). Protection criteria are taken in consideration, e.g., in order to minimize the interference between secondary LTE uplink and primary DVB-T receivers, mobile frequencies should be allocated so that the uplink band is furthest from the DVB-T channels (“black blocks”). The remaining “white blocks” can be allocated by the broker in future developments. Note that after the allocation process the remaining opportunities are more fragmented than the original TVWS pool (Figure 5)

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TIM

E

TV CHANNEL

WiFi

(reserved)LTE DL

Micro cellLTE DL

Macro cell

Unallocated

channel

LTE UL

Micro cellTelemetry

Not allowed

(Incumbent Systems)

LTE UL

Macro cell

Figure 6 Allocation profile of TVWS blocks.

In summary, allocation of the time-frequency-power blocks to the COGEU secondary systems should be done by taking technical requirements into consideration, as well as economical aspects of the spectrum trading. In a general context, dynamic spectrum allocation problem can be considered as dynamic packing of spectrum blocks into a four-dimensional resource, consisting of time, frequency, power and space.


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2- TVWS availability information and protection requirements

This chapter firstly describes how to determine the TVWS radio resources that may be finally managed by the COGEU broker. In this way a methodology to compute TVWS availability is defined. For the secondary spectrum trading model a guaranteed Quality of Service is required, therefore the mutual interference\coexistence of TV White Space devices has to be considered to enable a viable COGEU business model. This chapter addresses protection rules between secondary users to avoid mutual interference.

2.1- COGEU methodology to compute TVWS availability

For protection of primary systems, geo-location database for the moment seems to be the only realistic variant. For broadcast systems and stationary PMSE systems (see deliverable D4.1 chapter 3.7) the database is more or less static whereas for the case of temporary sites local ad hoc databases can be imagined. Basing on that information in combination with other parameters Figure 7 shows the procedure how TVWS are calculated. Only DVB-T coverage areas are used here. In an improved version the databases for PMSE will supply the additional information in parallel to broadcast data.The yellow highlighted parallelograms represent input data for TVWS estimation. The figure may be divided into several logical blocks: In the upper right corner IRTs frequency planning tool FRANSY is used to generate the Location Probability maps for each of the considered COGEU target TV channels 40 – 60. For this, FRANSY accesses DB1 which contains comprehensive raw data required for the calculation (locations of TV transmitters, transmit powers, antenna diagrams, height information, topographical and morphographical data, propagation models etc.). The left hand branch calculates the required safety distance. In a first step the coupling loss is calculated, i.e. the required attenuation of TVWS device signal strength between radiated power at the TVWS device and the received signal at the potential location of a DVB-T receiver. Usually a propagation model is used to estimate the signal attenuation for a given distance. Here in the block “Propagation Model” the model is reverse used to determine the safety distance for a given coupling loss. Fixing the minimum location probability to a given value, e.g. 70 %, determines the coverage areas for broadcast supply. The safety distance is used to “wrap” the safety belt around the coverage areas (within coverage area and safety belt a TVWS device is not allowed to operate at the considered channel). The remaining allowed (white) areas are the “gross TVWS”. Gross TVWS means that the situation in adjacent channels is not taken into account so far. Also the possibility of a TVWS device being interfered in the vicinity of strong broadcast transmitters will be considered. This is done in the next step where the result is the TVWS mapped to area: for each channel a geographical array is available, indicating for each pixel the maximum transmit power allowed for a TVWS device (TVWS pool). These data will be supplied to database DB2. DB2 is the basis for the geo-location database where also other incumbent services (mainly PMSE use) have to be included. The COGEU broker accesses directly to DB2 through a communication protocol. When combining these TVWS mapped to area with the population density in the considered regions a more market oriented number describing the available TVWS is generated: TVWS mapped to population density. These values may for a given location also serve as input parameter for price estimation of the TVWS.


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COGEUBROKER

COGEUBROKER

TVWS pool

Figure 7 Flow chart with the process used to compute TVWS availability

2.2- Preliminary COGEU results of TVWS availability in Germany

Figure 8 shows the “gross TVWS” for channel 59 in Germany. The frame encloses whole Germany, the blue lines indicate the borderlines of German federal states (for orientation: in the upper left is the North Sea, the upper right is the Baltic Sea). Bavaria is the state in the lower right with its capital Munich, located approximately at (4470,5330). The black areas indicate all the locations where broadcast reception in channel 59 is possible with a location probability of 70% or better. All transmitters operating at channel 59 in Germany and adjacent countries are considered. The grey areas show the safety belt around the coverage areas where a TVWS device is not allowed to transmit in channel 59. All the remaining white areas are the “gross TVWS”. The figure does not take into account PMSE use, which may reduce TVWS significantly.


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Figure 8 Gross TVWS in Germany for channel 59.

In COGEU, a spectrum pool will be created according to the results obtained from the measurements of D4.1 around Munich (Ebersberg, Freising, Eurasburg). According to these results it may be identified that the Munich city area is at least 10-20 km away from the closes coverage area of some channels. This is an enough safety distance to consider these channels as unused channels. If we conclude that in Munich area only the found channels (including the weak channels measured in the rural area of Eurasburg) are occupied and accept adjacent channels only for low power WSD (White Space Devices), then the availability of TVWS for channel 40-60 is described by the following figure, (see Figure 9). The actual maximum allowed power for WSD transmission for adjacent and no-adjacent channels is still under investigation, therefore a symbolic notation for y-axis is considered for illustrative proposes. It should be pointed out that this reflects the situation at the time the measurements were made and that PMSE equipment was not considered.

Gauss-Krueger (GK4) coordinates [km]

Gauss-K

rueger

(GK

4)

coord

inate

s [

km

]

Channel 59; Coverage: 70%; Safety Dist 10.00 km

4100 4200 4300 4400 4500 4600 4700

5300

5400

5500

5600

5700

5800

5900

6000

6100

Munich x


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Figure 9 TVWS available in Munich area. Symbolic notation for y-axis:

(0: Channel occupied by DVB-T; Low: Adjacent channel with low power; Max.: Free DVB-T channel)

2.3- How to realize mutual protection between the TVWS secondary users?

When considering TVWS applications a major part of discussion is the protection of the incumbent services (TV broadcast, PMSE, RAS in channel 38 and ARNS in channels 43 to 60), which means avoiding of interference to these services caused by TV White Space devices. However, for the secondary spectrum trading model a guaranteed Quality of Service is required, therefore the mutual interference\coexistence of TV White Space devices has to be considered to enable a viable COGEU business model. COGEU considers the two spectrum sharing regimes for TVWS in Europe: the spectrum commons and secondary spectrum trading (managed by a centralised spectrum broker). One essential difference between them is that with the spectrum broker a certain quality of service may be guaranteed to the licensee. As mentioned earlier, this Deliverable concentrates on the RRM issues related to the operation of the spectrum broker. (Traditionally allocation of resources in the spectrum commons does not need any RRM at the operation-stage, after the protocol-design stage). In a general case, a secondary user requiring a defined QoS may apply to the spectrum broker and if he gets assigned a channel it is registered in the broker‟s database (the broker‟s internal TVWS Occupancy Repository will be further discussed in Section 3.1.). Thus, the TVWS devices do not need to sense for protection of other TV White Space devices. (The TVWS devices accessing the spectrum commons have to sense to detect the transmission of other TVWS devices, including higher priority devices. However, again this is not handled by the spectrum broker, nor is addressed in this deliverable.) Priority The regulator may want to assign some TVWS applications higher priorities than other ones. The application with the higher priority then would be allowed to transmit whereas all lower priority applications (at this time and location) have to stop transmission immediately if the higher priority service is detected. The priority level might be included in the hardware directly, e.g. for emergency calls. However, this concept is rather inflexible and also does not solve the conflict between devices of the same priority. The broker may assign a free channel exclusively to a user at the requested time and location, with a maximum transmit power. This method would allow offering some QoS for this service. The broker might also assign time slices for a channel to serve different users with lower data rates or assign the channels to different users and limit the transmit power if the users do have different locations. In any case the broker has the full control on the system and sets the parameters to avoid or accept (some) interference between TV White Space devices under its supervision.


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The next two subchapters treat some technical requirements which are a prerequisite for the correct operation of TVWS devices. If these (and even further relevant aspects) are not fulfilled the upper layers of the TVWS systems will not work properly. The given aspects may serve as a reminder that even the underlying hardware has to follow its rules.

2.3.1- Rules for interference protection

Device design One preliminary requirement for the overall system to run satisfactory is that all the TV White Space devices fulfill the technical specification and work in a fair and cooperative manner, which is managed by the spectrum broker. The specification must be defined ahead of introduction of such TV White Space device systems and has to fix technical parameters for the TV White Space device, as there are for e.g.

- Maximum transmit power

- Requirements for location accuracy

- Protection ratios

The more accurate the specification is implemented and the less inconsistencies of the specification will be used by some TV White Space devices to get an advantage over other devices, the better the system will work. At run time At run time, measures to be taken to optimize system efficiency could be the following:

- To implement communication between the devices (e.g. through the spectrum broker

mediatory)

- To run a database for the TVWS devices (without registration, database returns free1

channels, but these channels may be used by other TVWS device of the same priority)

- To allow the spectrum broker to exclusively supervise or even limit the number of users

2.3.2- Technical requirements

So far only common aspects of protection requirements have been considered here. In COGEU deliverable D3.1 chapter 4 compatibility requirements for COGEU mobile systems and broadcast services were investigated. The considered concepts are also valid for reducing interference among TV TVWS device:

- In-channel protection ratio (PRco)

- Adjacent channel protection ratio (PRadj)

- Overload threshold (Oth)

- Emission spectrum mask

Closely related to PR are the ACS and ACLR. The adjacent channel selectivity (ACS) describes the behavior of the receiver and the adjacent channel leakage rate (ACLR) the emission mask of the transmitter. The specified emission masks can be found in deliverable D3.1:

- UMTS ch. 2.1.2.6 Operation BW (FDD mode): 5 MHz for UL / DL

- LTE ch. 2.2.2.6 Operation BW: 1.4, 3, 5, 10, 15, 20 MHz, FDD mode

- WiFi ch. 2.3.2.10 Operation BW up to 22 MHz

- DVB-H (DVB-T) Operation BW 7.6 MHz

The parameters have to be measured for each possible combination of TV White Space device, for the considered representatives this is

- LTE interfered with by WiFi

- WiFi interfered with by LTE

1 Free channel here means not used by incumbents


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- Public safety applications interfered with by LTE, WiFi and vice versa (the higher priority of

these applications may be limited to some channel, so LTE or WiFi use on adjacent channels

may be possible)

As there are so far no real cognitive radio devices available for 470 MHz – 790 MHz these systems serve as proxies and measurement of these systems is only an approach. Referring chapter 4 of deliverable D3.1, where protection ratio measurements for DVB-T interfered with by LTE and WiMAX are presented, it must be concluded that the spectrum masks for TV White Space devices have to be more stringent than the masks defined for LTE and WiMAX to enable cooperative operation in adjacent channels. This is because TV White Space device may be operated in short distance especially if the device penetration increases.

2.4- Conclusion

This chapter deals with protection requirements between secondary users. In a first step methods are considered on how the system becomes aware of the available devices. The concepts are quite similar to the ones used for protecting the incumbents, mainly sensing, geo-location with databases or a combination of both. To make sure that protection among the devices is facilitated, three layers have to be considered (see Figure 10):

Figure 10 Protection Layers

The rules and regulation level describes the idea behind the system. A TVWS device not following the rules is more or less like a criminal that violates the laws. In a consequence such devices shall not be allowed to be operated. The software implementation level considers mainly the accuracy the system specification is implemented and the „fairness‟ of the implementation. If the software tries to gain an advantage over other TV White Space devices by using some inconsistencies (if existing) in the specification the total system would work suboptimal. On the hardware level things like quality of filters (determining e.g. protection ratio), linearity of input amplifiers (influencing the overload threshold) maximum transmit power and accuracy in geo-location are relevant parameters. The systems considered so far are only proxies for possible TVWS devices in the future, so measuring its hardware parameters can only supply estimations for TVWS device. A TVWS device not fulfilling the specification at each of the levels may cause interference to other devices. A device, e.g. manufactured for the US-market may violate some rules for Europe and should not be allowed in Europe; a device using some loopholes to gain more bandwidth, higher transmit power or which may vary its location information acts on the cost of the other devices. And a device with a poor hardware design may interfere with others, no matter how careful rules and software are implemented. Coexistence analysis between WSD will be further investigated trough lab tests in COGEU T4.4: “Testbed for coexistence evaluation and TVWS equipment certification recommendations”.


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3- Spectrum broker allocation process

Figure 11 presents the decision making process of COGEU broker for spectrum allocation of TVWS. This figure depicts a general diagram of the centralized dynamic spectrum access schemes that follow two basic approaches, based on optimization and auction theory. The algorithm has three major phases: I. Preparation and Analysis, II. Operation and III. Maintenance. Note that, the algorithm phases are marked in shades of green, and the geo-location data base (presented in Section 2.1-) is external from the broker.

Estimation of demand Section 3.2

START

Geo-location Data Base (DB2)

Section 2.1

Benchmark price estimation based on AIP (including

minimum price/ call price for auction)

Section 3.2

Open demonstration of interest in buying TVWS

Spectrum Portfolio Proposal based on Matching Algorithm

(MA) - TVWS allocation optionsSection 3.3

TVWS occupancyRepository

Section 3.1.1

Acquisition of Spectrum Bands (TVWS pool)

Enhancement of TVWS pool

Demand<=Offer Demand>Offer

Announce fixed price per MHz

Allocation of TVWS temporarily exclusive rigths

Update of TVWS

occupancy repository

Auction (Section 3.4)

TVWS allocation(Select one solution from MA)

Section 3.3

Announce the auction and the minimum/call price

Reception/analysis of bids

TVWS allocation(Select the solution from MA

that maximizes the profit)

Advertise the Spectrum Portfolio

Selection Criteria, e.g.

fragmentation

Phase II: Operation

Phase I: Preparation and Analysis

Ph

ase

III:

M

ain

ten

ance

Reception of SU’s final interest

Demonstration of the SUs interest

START

Bids from the SUs

Spectrum Trading Policies

RepositorySection 3.1.2

Regulator

Figure 11 Spectrum Broker allocation process


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Phase I, Preparation and Analysis, deals with the general recognition of the spectrum availability, secondary users demands and the spectrum valuation. First, the spectrum broker accesses the external geo-location database for the available and marketable TVWS, and then, by receiving information from the internal TVWS occupancy repository, which contains the information of the temporal occupation of the TVWS by Secondary Users (SU) at these locations, updates the actually TVWS pool. It has to be noted that it is important to keep record of the TVWS occupancy by SUs to avoid mutual interference. The next step in this phase is the Estimation of the spectrum demands, which is necessary for the valuation of the spectrum, i.e. the Benchmark Price Estimation. This valuation can be done by the Administrative Incentive Pricing (AIP) mechanism taking the estimated demand, maximum allowed power, population density of the area, etc. As a result of the Benchmark Price Estimation, the benchmark spectrum-unit price is defined, the call-price for possible auction and the minimum price reflecting the valuation of the spectrum by the broker (note, that the broker may have some operational cost, and thus it would be interested in selling the spectrum only if its costs are covered by the minimum price). The next step is the preparation of the Spectrum Portfolio Proposal based on Matching Algorithm (MA). The input to the MA is the TVWS availability as well as the estimated demanded spectrum. The algorithm outputs the possibilities of the allocation of the available spectrum to the SUs taking their coexistence into account (interference maintenance between the SUs). Note, that there may be many such possibilities. The MA can be based on the backtracking algorithm to lower its computational complexity. Phase II, Operation, starts with the Advertisement of the Spectrum Portfolio. This advertisement is necessary to inform the secondary users what products can be sold to them, i.e. what spectrum bandwidth and transmission power typically used by the secondary users requesting the spectrum is available on the market. If the aggregated secondary-users spectrum-demand is lower than the spectrum offer (resulting from the Spectrum Portfolio Proposal preparation stage), further steps of the algorithm are responsible for the spectrum usage optimization. In such a case, all demands will be satisfied, and the price for the spectrum unit is fixed (this price is estimated at the Benchmark Price Estimation stage). This price is announced to all secondary users (at the step called Announce fixed price per MHz). Knowing the price, the SUs demonstrate their final interest in purchasing the spectrum. It is received by the broker in the Reception of the SU’s final interest step. The next step is the TVWS Allocation (Select one solution from MA). At this stage, the spectrum access by the secondary users is formulated as an optimization problem which can be solved utilizing the output of the matching algorithm. In such formulation, the objective is to avoid fragmentation of the allocated spectrum, as it may be useful for the future run of the algorithm. Alternatively, when the spectrum demand exceeds the spectrum offered in the spectrum portfolio, the auction mechanism is implemented. The spectrum-commodity auction can be used to determine not only the spectrum allocation strategies, but also to determine the price spectrum access by the secondary users. First, the auction and the call-price are announced (Announce the auction and the minimum/call price step). Then, the SUs demonstrate their interest in participation in the auction by submitting their bids. The bids are analyzed and processed (in the step named Reception/analysis of bids) for further profit-maximization mechanism (implemented in the step named TVWS Allocation Select the solution from MA that maximizes the profit), which is defined as optimization procedure, with the goal function reflecting the broker‟s economical profit. In short, an optimization-based approach for the case of the spectrum demand not exceeding the spectrum offer allows satisfying all secondary-users requests, and gives more freedom to emphasize the technical-efficiency aspect of the spectrum usage (maximization of the transmission rate, minimization of the spectrum fragmentation). The auction-based approach is used for the case of the spectrum demand exceeding the spectrum offer, when not all the requests can be satisfied. This approach selects a subset of the secondary users to be allocated the spectrum, and emphasizes the economic aspect of dynamic spectrum access. Note, that in both cases the TVWS allocation has to satisfy the spectrum trading policies in the considered area (state or country), which include regulations, priorities, restrictions, etc. The spectrum policies will be investigated in T2.2 and reported in D2.2. After the allocation of the TVWS, the secondary users are granted temporary rights for using the spectrum (in the algorithm stage named Allocation of TVWS temporarily exclusive rights).


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Phase III, Maintenance, involves the Update of the TVWS occupancy repository for recently allocated spectrum with the coverage area of the secondary systems. The algorithm can be run again, when there is still an unused spectrum and a demand from new incoming secondary users or in a periodic basis (the TVWS market opens every day or every week). Finally, the RRM procedures taking advantage of the new spectrum allocated to the secondary users are external from the operation of the broker. These RRM procedures aim at the provision of the guaranteed QoS to the mobile subscribers of the considered secondary player (e.g. a LTE mobile operator acting as a broker‟s customer). On the other hand, the analysis of these procedures helps to understand the SUs demands occurring in various periods of time, which are then considered by the broker. The RRM algorithms for the case of LTE extension over TVWS is reported in Chapter 4-. The key building blocks of the above-described flow-chart are discussed in the following sections of this deliverable:

Internal spectrum broker databases: TVWS occupancy repository and the Spectrum trading policies repository.

Benchmark price estimation method: based on an Administrative Incentive Pricing (AIP) is useful to define the spectrum-unit price of TVWS.

Matching algorithm: used to match spectrum supply from TVWS pool and spectrum demand from secondary systems. This algorithm is applied in cases, where spectrum demand is lower than spectrum supply.

Auction mechanism: utilized in cases where spectrum demand from secondary systems is higher than spectrum supply.

3.1- Internal spectrum broker databases

The description of the TVWS allocation process puts the focus on the commercial aspect (pricing or auction). However for drawing decisions also technical and regulatory aspects have to be taken into account. These two sides shall be considered by the spectrum broker databases. The technical side reflects the “physics” of the system, e.g. possible mutual interference of TVWS devices, and the regulatory side gives the frame i.e. what is allowed by the rules, e.g. maximum transmit power. Considered in an abstract manner, the TVWS maps are as well kinds of rules that define the operational parameters of TVWS, i.e. those locations where possible transmit power is unequal to zero. Figure 11 presented before explains the Spectrum Broker Decision Making Process where two internal databases are included: the TVWS occupancy repository and the Spectrum policies repository. The following sections detail the role of these databases.

3.1.1- TVWS occupancy repository

The TVWS occupancy repository is the unit that contains all the information where TVWS devices may transmit and also contains a database on active TVWS devices and their operational parameters. The TVWS occupancy repository carries all the information required to estimate mutual interference between TVWS devices. One of the fundamental parameters in this database is the spatial resolution (the cell size). It is defined once at the introduction of the system and then kept fix. However, a change to another resolution may not be excluded, even use of different resolutions may be reasonable. The TVWS occupancy repository not only contains the data, it as well hosts the methods to manage the TVWS systems and generate events or reacts on external events relevant for the management of TVWS systems, e.g:

a. Data

- TVWS (channels unused by incumbent services: DVB-T, PMSE…), supplied by the

external database (DB2 in Figure 7 and Figure 11)


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- All TVWS devices in use in the considered area with its describing parameters

b. Methods

- How to fill the database / updating the database (TVWS)

- Management of database (add/modify/remove TVWS service)

- Perform interference calculations / calculate coverage of TVWS devices…{using the

methods of the spectrum policy database, see the link between the two internal databases

in Figure 11}

- Handle prioritized services

c. Events

- Trigger TVWS Update (periodically / on external trigger)

- Start prioritized service

The list is not exhaustive; it is just to describe the idea behind.

3.1.2- Spectrum trading policies repository

The spectrum policy database manages the spectrum trading policies of the regulator. As long as there are no changes in the regulation, no modification is necessary for this database. Like the TVWS occupancy repository the spectrum policy database not only contains some data but also has the functionality to actively treat them

a. Data

- All rules and formulas that enable to calculate coexistence between TVWS applications

- Includes protection rules between secondary systems

- Prioritization rules for public safety applications

- Commercial data, benchmark spectrum prices

b. Methods

- Manage the rules (add, modify, remove)

- Provide the formula/methods to populate the TVWS occupation repository

- To avoid monopolization and spectrum hoarding


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3.2- Benchmark price estimation based on administrative incentive pricing

This section corresponds to the „Estimation of Demand‟ and „Benchmark Price Estimation based on AIP‟ blocks in the general flow chart of Figure 11, which is within the preparation and analysis phase. The spectrum demand estimation is necessary for the valuation of the spectrum. This valuation can be done through estimating the opportunity cost of alternative use of the band. The estimated opportunity cost gives the reference or benchmark price, which will be used as the call-price or the minimum price in the spectrum trading phase. The notion of benchmark pricing through opportunity cost plays a crucial part in ensuring that resources are being used efficiently.

3.2.1- Background

Current spectrum allocation is static and inefficient. Efficient spectrum usage can be achieved through market based spectrum allocation. However, changes from static to dynamic spectrum usage cannot take place overnight. Administrative incentive pricing (AIP) provides a means of providing the gradual change toward spectrum trading. AIP refers to the setting of spectrum price by regulators in order to reflect the current value of the spectrum. Specifically, regulators mimic the market to set the price of spectrum equal to its opportunity cost. AIP is used for the following reasons:

To provide incentives for economically efficient use by assigning the spectrum to those who value it the most.

When markets are absent or do not work well. AIP is needed when spectrum is not tradable, or trading does not work well e.g. because of high transaction costs or towards specific users who are unlikely to respond to opportunity cost like government users.

To avoid hoarding which prevent spectrum from moving to the highest value user; or prevent over consumption due to free usage.

Pricing may also be applied to provide a fair return to the state for use of the resource. In this section, AIP is used in the resource allocation algorithm of the COGEU Broker for setting (estimating) the benchmark price of the TVWS bands (Figure 11). This approach needs to consider dynamic market conditions and static situations, as well as anticipate changes in the long run. Such changes may be an increase or decrease in the demand for spectrum usage due to changes such as newer technologies or shifting of consumer tastes.

The key challenge in this approach is how to set the price of spectrum to reflect the social value of the resource, as well as the underlying signalling mechanism to enable such a framework to operate seamlessly. The pricing mechanism is for the purpose of allocating spectrum to the most valuable user. It can be seen that this problem is multifaceted and will be dealt with in succession in related work packages. In this deliverable, the focus will be on the resource allocation aspect, whereas the pricing mechanism will be dealt with in Task 2.3, and also the related signalling mechanism and protocols will be the focus of Task 6.4.

The value of a frequency band may change depending on potential uses, and hence its usage has to be given to the most valuable user. A key operational question, at the decision maker level (i.e., which could be the policy maker, intermediary entity such as a broker, etc), is how to evaluate, or set the price of the spectrum so as to match its current value, and then allocate to the most valuable user.

Therefore, for a given set of TVWS bands , the task would be to find the reference price of each

band. So, if is a frequency band within the pool, then, following [21], we can define frequency

band as:

=

where integer and . If there are K possible uses for radio spectrum, the allocation

problem may be solved by dividing B into K frequency bands for , which will be valued depending on potential demand.

3.2.2- Spectrum users and congestion


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In the context of COGEU, there are different spectrum users; each defined by a set of technology specific characteristics. The users vary in the minimum amount of bandwidth that each one needs in order to satisfy their service requirements. This is also reflected on the value they place on having their service needs fully satisfied. Because of differences in service needs, users also vary in the extent to which they can tolerate spectrum congestion. Congestion is a difficult term to define, but in the TVWS context it is taken to mean excess (unmet) demand for the spectrum, or for a higher grade of service, at the price which it is offered. Congestion is deemed to exist if any of the following hold:

physical congestion exists, for example in the form of interference; more users would like equivalent spectrum to run similar services; there are potential users who would like the spectrum in order to do something else with it.

An understanding of where congestion exists is necessary in order to identify areas where there is an unmet demand for spectrum and therefore where spectrum pricing should be applied in order to better match supply with demand. In other words, congestion refers to the level of utilization the existing user(s) make of a specific band (and location) given the current level of fee. The degree of congestion determines the easy or difficulty in accommodating new users without causing harmful interference. Hence, for a user requiring a very high quality of service (e.g. video streaming or other applications that require a high and reliable data transmission rate) might find quality unacceptable whenever aggregate demand exceeds 60% of system capacity. On the other hand, users that can tolerate a lower quality of service would find quality acceptable when aggregate demand exceeds a higher percentage of system quality (e.g. 80%) [23]. Congestion in the TVWS in different areas like urban, sub-urban and rural areas could be presented by different weights so as to match the price and the willingness to pay of a spectrum user, and then allocate that spectrum band to them. The user‟s demand for service quality can be measured by his or her congestion limit, expressed as the maximum amount of spectrum congestion a user can tolerate before the value he or she places on employing spectrum falls from some desired value to zero [23], [24], [25]. The metric can further be decomposed into geographical/location, band, etc. In fact, TVWS availability varies considerably in urban, suburban and rural areas. Available reports show that TVWS are present and fragmented [26][27][28]. They are typically more abundant in rural areas, with larger contiguous blocks of unused channels available, as broadcast network planning priorities are linked to population density. COGEU measurements presented in Section 2.1 confirm this.

3.2.3- AIP based Algorithm

3.2.3.1 Background of AIP usage in Spectrum Allocation According to the OFCOM [29], in 1996, the RA commissioned a study by NERA and Smith Systems Engineering Limited looking into the use of spectrum pricing [30]. The NERA Smith approach recommended setting prices according to the opportunity cost of spectrum. The opportunity cost of spectrum represents the benefits that would be derived from the next best alternative use and can be calculated on the basis of the least cost alternative use of spectrum that would enable the same output to be produced. Given the uncertainties that surround the calculation of AIP, these prices are set conservatively at first. In 2002, the Cave Review [31] was commissioned by the Government as an independent review of spectrum management. It recommended greater use of auctions and AIP, and specifically that AIP should be applied at more realistic levels across different spectrum bands. The report strongly suggested that prices should be set at full opportunity cost where spectrum shortages occur in a particular band. In 2003, Indepen [32] was commissioned to update the original analysis and rationale for pricing by NERA Smith and to assess how the methodology could be applied more widely to other areas of spectrum use. The report widened the scope of the marginal value to include the value of alternative uses of a spectrum band in addition to the existing use. This broader methodology for determining AIP


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resulted in changes to license fees for a number of license classes which have resulted in the new fees introduced in 2005, and in further changes now being proposed by OFCOM in [33].

3.2.3.2 AIP Basic Algorithm The spectrum price or value is set based on market observations and other administrative criterions as in [33]. In addition, owners of frequency usage rights to use frequencies in many countries pay recurring user fees. These fees can be based on administrative costs or the opportunity costs of spectrum usage. In the latter case, user fees are usually known as administrative incentive pricing. Administrative incentive pricing is important in a spectrum trading regime since, by imposing a cost on hoarding, they provide incentives to sell and lease underutilised spectrum [34]. AIP is an important mechanism for promoting efficient spectrum management. This is because AIP signals to the spectrum user the opportunity cost of using the resource. The rationale for AIP is to promote the efficient use of spectrum (where it is congested) by allocating it to those who value it most. Those users to whom spectrum is worth more than the AIP fee will keep the spectrum they hold (or buy any that becomes available), and those to whom spectrum is worth less will sell any spectrum they hold. The opportunity cost of a particular block of spectrum is the cost of denying use of the spectrum to any other use or user. If the value of the spectrum to the incumbent use/user is less than the opportunity cost, then the distribution of spectrum can be said to be sub-optimal in the sense that more value would be created by reallocating the spectrum. If users are faced with the opportunity cost of spectrum, they will have incentives to increase/decrease their use if they value spectrum more/less than the opportunity cost [35]. In theory, current users would therefore be willing to transfer rights to use spectrum if the opportunity costs of using spectrum, reflected through administrative incentive pricing, are higher than the economic value to the user. The administrative pricing mechanism based on opportunity costs is consistent with applying higher fees in areas where there is high demand (congestion), and lower fees in areas where there is less demand. In principle, fees set should mimic the operation of a market for spectrum bands since where fees do not reflect market values; this can lead to inefficient use of the spectrum. The notion of opportunity cost can play a crucial part in ensuring that resources are being used efficiently, however there are complexities in determining the appropriate opportunity costs and basing fees on these costs. Therefore, in the context of the usage of TVWS in the COGEU use cases, the proposed algorithm has the potential to:

1. Provide incentives to sell and lease underutilized TVWS through imposing a cost on hoarding. 2. Increase economic value of spectrum usage. 3. Reduce spectrum fragmentation. 4. Ensure QoS of the TVWS user through the consideration of the congestion limit.

Figure 12 shows the algorithm for TV white space allocation based on AIP, where as Table 4 gives an explanation of the factors which are to be considered in setting AIP. An important challenge is setting the reference rate of each band. The reference is determined by through opportunity cost evaluation of a given band as will be presented in the next section.


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Figure 12 The algorithm for allocating TVWS based on AIP [Adopted from [33]]

The factors in Table 4 can be used in setting the AIP of TVWS band for, hence, reflecting the various user needs and changes in spectrum value due to adjustment of considered factors.

Factor/ Parameter Explanation

Reference Rate Reference rate of each unit bandwidth: x € per 2 x 1MHz. This spectrum price is determined based on spectrum demand for a specific frequency and geographical location.

Bandwidth Directly proportional to the link bandwidth in MHz but with a preset minimal value, e.g. 1kHz or 1MHz. The total bandwidth acquired by the user is based on the intended use of the bands.

Area sterilized The area within which another service using the same channel cannot be assigned without harmful interference. Higher availability requires higher radiated power levels, which is an opportunity cost for other users.

Exclusive/ shared use Reduction of 50% when channel is shared – this can be reflected in the pricing algorithm


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Band factor Band usage (Highly popular, Medium popular and Less popular bands). This factor adjusts the license fees to encourage a general use of higher bands. The Band Factor for TV white spaces in UHF/VHF bands is categorized as highly popular for all applications.

Location factor Population (High, Medium, and Low population)

Oth

er F

acto

rs

Path length factor

Adjusts license fees to encourage short links to move to higher bands, thus retaining lower bands for longer links that would not be technically possible in the higher bands. The algorithm may differentiate the charges for links with shorter than the minimum path length or over-exceeding the path length.

Congestion tolerance

Measure a user‟s demand for service quality by his or her congestion limit, expressed as the maximum amount of spectrum congestion a user can tolerate before the value he or she places on employing spectrum falls from some desired value to zero.

License terms Long-term lease, a scheduled lease, and a short-term lease or spot markets.

Type of Antenna

Type of antenna used, like directional creates space for spatial sharing of spectrum, and hence should be encouraged through cheaper prices for TVWS

Priority During disaster, all systems should be in emergency mode and allocate or reserve a preset amount of bandwidth for relief uses.

Table 4 Factors to be considered in setting AIP for TVWS allocation in COGEU use-cases

3.2.4- The Principle of Opportunity Cost

The aim of setting the price of spectrum is to create incentives for spectrum users to provide high-value services at the least cost possible for efficient usage. When the market sets spectrum prices, as in auctions, equilibrium is automatically reached leading to efficient usage of resource. Therefore, artificial price setting, as in AIP, can only emulate the efficiency and incentive effects of market-based pricing. Such market emulating prices are based on the economic principle of opportunity cost.

3.2.4.1 Market efficiency In markets, prices are set by the forces of demand and supply. The price set such that supply equals demand provides market participants with the right incentives to behave efficiently, leading to economic efficiency. Market based economic efficiency can be reflected in allocative, productive and dynamic efficiencies [21]:

Allocative Efficiency: the mix of goods and services that are produced in the economy is

such that no other mix increase the wellbeing of the society.

In terms of TVWS, users of spectrum should be such that the right final mix of spectrum-related products in being made available. In terms of this deliverable, that would mean the right mix of QoS guaranteed spectrum is achieved.


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Productive Efficiency: Production of goods and services ought to be undertaken at the lowest possible cost, whereas cost is measured in terms of inputs.

In terms of TVWS, users of spectrum should choose inputs, such as Capital Expenditure (CAPEX), Operational Expenditure (OPEX) which includes labour and spectrum; in order that production of services is at the lowest overall cost. In terms of COGEU use cases, the TVWS provides a viable alternative for lowering the cost of providing services through the acquisition of the TVWS, and hence benefit from their inherent friendly RF propagation characteristics such as indoor penetration, longer ranges etc. Furthermore, the action mechanism of the COGEU Broker provides incentives for spectrum buyers to acquire spectrum so that the services are offered in a cost effective manner.

Dynamic Efficiency: resources are allocated in a way that encourages the most desirable

level of research, development and innovation.

In terms of spectrum, the right amount of innovation in spectrum use and spectrum-related products should be encouraged to enable substantial supply and demand to interact over time to optimise allocation and technical outcomes. The COGEU project, through research, will contribute in achieving this by developing spectrum allocation mechanisms that enable dynamic interaction of spectrum and supply sides of the spectrum to balance mismatches in spectrum needs. This is achieved through the Broker which offer temporary exclusive rights to spectrum buyers. Therefore, dynamic efficiency can be achieved through market dynamics orchestrated by the Broker.

Thus, the government being the controller of the spectrum resources, has the potential to influence the above mentioned efficiencies.

3.2.4.2 The use of opportunity cost Opportunity cost is defined as the highest value alternative forgone. The opportunity cost of the marginal unit of the good or service in a market equals the market-clearing price of an efficient market. Hence, when regulators seek to emulate the efficiency of the market in allocating spectrum, spectrum should be prices based on opportunity cost. According to [22]:

“The fundamental mechanism by which the spectrum management regime could contribute to economic growth is through ensuring that users face continuing incentives towards more productive use of this resource [i.e., spectrum]. These incentives should be financial and based on the opportunity cost of spectrum use. In this way, spectrum would be considered as any other input into the production process. Price signals about the cost of using spectrum would be disseminated throughout the economy. This information should enable dispersed economic agents to make their own judgements about their use of spectrum and the alternatives open to them to meet their organisational goals.”

Generally, auctions produce better outcomes, in terms of market efficiency, than what regulators could achieve. Normally, in cleared bands or unencumbered bands, allocation through auction would be the best approach. However, in the TVWS, for example, it is less clear how often auctions will be preferred due to timing considerations, optimal license durations and incentives for incumbent so vacate. Hence in similar cases (i.e., where the use of auctions would not be feasible), the use of AIP can serve to encourage efficient spectrum usage through the Broker.

3.2.5- Methods of Estimating Opportunity Cost

Different approaches can be used for implementing opportunity costs pricing within a band based on the objectives the regulator (or broker) seeks to achieve, which basically is to emulate the efficiency properties of a competitive market (auctions) [36], [37]. When auctions are not used, the derivation of opportunity costs can be achieved through market information or direct computation methods.


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3.2.5.1 Market valuation methods Opportunity cost may be derived from market data, including [36], [37]:

Spectrum market transaction: this involves information on the price of spectrum in auctions or trades in secondary markets. The method is simple, but makes it hard to compare meaningfully frequency bands and market values obtained in different geographical regions and from different timeframes.

The value of spectrum owning companies: this uses information on the market value of

companies holding spectrum rights. This information reflects the value of spectrum plus other assets. Therefore, the value of spectrum = company value – value of other assets. The method is impaired by the requirement of potentially uncertain values of non-spectrum inputs/assets and the volatility of share prices.

Capacity sales of spectrum-utilizing services: this uses information on the sale price of

capacity (for example, sale of digital terrestrial TV multiplex capacity, or sale of wholesale capacity on a mobile network) for services which spectrum is an input. This information reflects the value of the spectrum plus the value of other inputs. Therefore, the value of spectrum = capacity price – value other inputs. Similar to company value method (preceding bullet), this method is impaired by the requirement of potentially uncertain values of non-spectrum inputs/assets.

In principle, the above market information can be used to deduce spectrum price. However, further judgement and caution have to be excised so as to avoid inefficient spectrum usage. For example, if auction participants also face AIP based on auction results, then there is an increased incentive to keep auction prices down (by collusion), or lead to lack of transparency in future trades [36], [37].

3.2.5.2 Direct calculation methods In direct calculation methods, the regulator acts as if it is a bidding company, and then use the bidders method of predicting price to set AIP. The methods include:

Standard Net Present Value (NPV): AIP could be set based on the standard NPV modelling that firms conduct, however this is error prone for a regulator.

Least Cost Alternative (LCA) (in the United Kingdom) or optimal deprival value method (ODV) (in New Zealand): this is the bid of an average bidder or bidders for multiple-use bands might be, and only requires the use of cost information, that is, uncertain revenue projection (as in NPV) is not required.

In this case, during price setting, a representative firm has to be carefully selected

3.2.5.2.1 NPV Method:

The NPV of a project is calculated as the sum of future cash flows (revenues) discounted to present values, minus the market values of other inputs, expressed mathematically as follows:

where t is a time index, n is the expected number of years that the investment is expected to last, C is cash flows or revenues, r is discount rate and I is market value of non-spectrum input. As it can be seen, the NPV method requires revenue and cost information; however the ODV method requires only cost information.

3.2.5.2.2 The LCA method

The Smith-NERA least-cost alternative or ODV method, calculates the impact of a hypothetical marginal change in spectrum on the cost of an average firm in the sector assuming the level of output and service quality were kept constant [21], [36], [37]: Suppose operator in one of the COGEU use case which uses spectrum and another additional

equipment (say base station ) produces output . The operator‟s output can be expressed as:


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.

Assume the operator maximizes profit and hence minimizes its costs. If a unit of spectrum is added

to or subtracted from , a compensating change could be made in the amount of the other input

such that the total output is unchanged at . By doing this; the rate of technical substitution between

the two inputs can be assessed. For a , there would be an implied change , and where the latter is multiplied by its price (which is determined on a competitive market) this allows for a monetary representation of the rate of substitution. By applying the same procedure in other sectors, comparisons can be made across sectors using the common unit money (which means comparisons can be made across sectors where different inputs substitute for spectrum).

In Table 5, a hypothetical example illustrating the LCA method in COGEU use cases is presented. The values in the cells are calculated as described in the preceding paragraph. Hence, 100 in use I, frequency band (or white space) x is the value, expressed in monetary terms using the LCA input, of the marginal unit of spectrum. For example, x unit of spectrum may have a price 25. The values in the other cells also represent the value of a marginal unit of spectrum. For productive efficiency to be satisfied, spectrum ought to be allocated across uses so that these values are identical. It can be seen in the table that they are not equal. The example is an illustration of productive inefficiency, that is, there is a possibility of achieving efficiency by re-allocating the TVWS. In other words, if re-allocation of the TVWS were not possible, then the values of Table 5 could be reflecting productive efficiency. It could be impossible, for example, if the operators in each use area differ and the value in the cells represent averages, then productive efficiency would have been achieved in each use area if the price of the white spaces were set equal to the opportunity cost.

Frequency bands

Uses x y z Non radio spectrum input I 100 75 0 0 II 35 60 30 0 III 10 15 15 5

Table 5: The value of different white space frequency bands for different (COGEU) use-cases

Further application of the LCA method leads to recommended prices for radio spectrum consistent with productive efficiency. Consider the values in the row associated with Use I. The marginal value of frequency band x in Use I is 100 and the marginal value of frequency band y in Use I is 75. Note that frequency band y is an imperfect substitute for frequency band x in Use I. However, the marginal value of frequency band y in Use II is 60. So, it will be better off if some of frequency band y were re-allocated to Use I. This is because a marginal unit of frequency band y applied to Use I could produce the same output in Use I while freeing up enough resources to compensate use II (and hence maintain a constant output in Use II) and provide some extra resources for additional production in the economy. The above can be stated in terms of opportunity costs. The opportunity cost of frequency band y in Use II is 75, the foregone saving in terms of LCAs that would arise if the frequency band were used in Use I (the next best alternative). By expressing the value of marginal spectrum in terms of opportunity cost, it is possible to address the issue of pricing TVWS. What should the administrative price be for TV white space band y? This is determined by permitting variation in the white spaces allocated to the COGEU use cases. It is clear that more of frequency band y ought to be allocated to Use I, and more frequency band z should be allocated to Use II. The opportunity cost is used to determine the reference rate in the algorithm in Figure 12. Notice that parameters like maximum transmit power that is allowed per band is reflected in the calculation of the reference rate for AIP fee. Therefore, if a COGEU user wants, let‟s say, a 10 MHz white space where they can transmit 30 dBm over and the protection limits of that band does not allow that much power, then it is possible to substitute this band for another COGEU user who needs 10MHz and operates at 20 dBm maximum transmit.


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3.2.6- Estimation of opportunity cost for TVWS bands

Based on the analysis presentation above, the estimation of opportunity cost and eventually the calculation of the AIP can be summarized as shown in the Figure 13. In our study, we perform the calculation of the opportunity cost for TVWS usage based on the LCA approach. The choice of the LCA method is due to the fact that it only requires cost information whereas uncertain revenue projections are not required. Specifically, the LCA requires the following input information:

Assumptions regarding equipment costs for the COGEU use cases, namely LTE, Public Safety, WiMAX, etc

Equipment lifetime, Maturity of the network and The point at which the user is assumed to switch to the least cost alternative.

Moreover, one-off costs such as investment in equipment need to be converted into equivalent annual values to produce an annual reference rate. This entails assuming a market based weighted average cost of capital (WACC) and a discount period.

Figure 13 Estimation of TVWS reference rate based on opportunity cost

As an example, consider the scenario in Figure 14. Consider a master-slave mobile service system which intends to acquire TVWS from the COGEU Broker via an internet interface. Here, we want to provide an example of estimating the opportunity cost of the TVWS using the LCA method. The example is adopted from [36], [37]. In order to incentivize a spectrum user move to more efficient usage of a given TVWS band, a broker may make the following opportunity cost estimation. Assume the mobile service provider is moving from 25 kHz to 12.5 kHz channels. Furthermore:

For each frequency pair, assume that there are on average 25 slaves.

Assume that all slaves and master transceiver are not capable of being reconfigured to use flexible channelization, and hence have to be changed.

The cost of old and new systems is the same. Therefore, the only costs to be considered are costs of disposing the old equipment.

The old system (25 kHz) is disposed when half way through useful life of 10 years.


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The costs of the master and slaves transceivers are given as follows:

Device Cost

Master Transceiver €3000 Slave Transceiver €1000

Therefore, the cost of the Master Transceiver written off after 5 years is given as:

Device Number Discount rate Years Cost Annualized

Master Transceiver 1 10% 5 €3000 €360 For the Slave Transceiver device, the cost it is written off after 5 years is given as:

Device Number Discount rate Years Cost Annualized

Slave Transceiver 1 10% 5 €1000 €120 Therefore, the opportunity cost for different system categories of single, lightly, average, and heavily loaded system with 1, 5, 25, and 100 numbers of slaves respectively in terms of per kHz per assignment is as shown below:

Type of System

Number of Slaves

Opportunity Cost

(per kHz per assignment)

Single slave system

1 €38

Light loaded system

5 €77

Typical system

25 €269

Heavy loaded system

100 €989

Figure 14 Typical master-slaves mobile service system using COGEU Broker

The opportunity cost of the spectrum can be set at the cost of the „typical‟ system. This will incentivize high value users (heavy loaded systems) to continue using the system since they value the spectrum highly; and encourage the low value users (lightly loaded systems) to change their systems for more efficient technology or venture in other businesses. Therefore, the opportunity cost can be used as reference rate for a given TVWS band. Whereas the opportunity cost can be adjusted based on


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various factors as previously presented, this study is only intended to provide the reference rate of the spectrum based on opportunity cost pricing.

3.2.7- Conclusion

This chapter presents AIP methodology for proving a reference rate for TVWS trading and will be further elaborated in WP2 considering the impact of various factors on the AIP as shown in Figure 15.

Figure 15 Calculation of the AIP by adjustments of the reference rate with various factors

An important parameter is the location factor, it adjusts the reference rate to reflect the actual value of a block of spectrum in the area sterilized relative to the reference location on which the reference rate was based. In this aspect, future work will investigate how to set AIP so as to reflect the effect of population as well as income levels. Note that the COGEU methodology to compute TVWS described in Section 2.1 maps the TVWS availability with the population density. This information will be further utilized to set the location factor. Further work will also be directed in the area sterilized factor. The area sterilized refers to the area within which another services using the same channel cannot be assigned without harmful interference. This is different from coverage area, which is the area within which an acceptable and usable signal is received. Hence, further work involves the investigation of the setting of AIP fees to reflect area denied to other users and uses, e.g. by setting higher fees for higher transmitter power to encourage users to minimize their emission area, or encourage use of directional antennas, etc.

Future work will also consider means to give incentives for TVWS systems to develop technologies that support multiple-bandwidths. This will improve the efficiency of TVWS usage and will also help in reducing frequency fragmentation inherent in the TVWS.


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3.3- TVWS allocation based on matching optimization

This section elaborates on a matching algorithm based on backtracking process [11]. The proposed algorithm was designed, simulated and evaluated in order to match spectrum supply from a TVWS pool and spectrum demand from secondary systems. The implementation of the proposed algorithm in the COGEU spectrum broker context is presented in this section including all parameters and constraints taken into account. A simulation framework is also presented and algorithm‟s functions are analyzed in order to solve the TVWS matching problem. The preliminary results of TVWS pool in Munich is adopted as performance evaluation scenario. In a general context, matching is an optimization problem. Optimization problems involve issues regarding optimizing non-linear and discrete functions, which could not yield adequate solutions easily using conventional methods. In most cases, as the search space is enormous, these techniques will tend to trap in local minima. Moreover, when multi-objective goals are involved, the problem becomes even more complex. Finding the best optimization technique for such type of problems is a very difficult question to answer. An “efficient” method should be able to produce an “optimal” solution or of acceptable quality of solution at the cost of a “reasonable” computing time. Moreover, the “optimal” adjustments of various parameters of an algorithm can be recommended theoretically, but it is often inapplicable in practice due to a prohibitive computing cost. Consequently, the choice of a “good” algorithm, and the adjustments of the parameters of this one, generally calls upon the know-how and the “experience” of the user. Optimization techniques can be broadly divided into three groups: analytical techniques, heuristics and meta-heuristics. Most of the analytical techniques are calculus based and they become intractable when the search domain of the problem becomes larger. Heuristic methods do not guarantee obtaining the optimal solution, but can yield good solutions at a reasonable computing cost. Although, meta-heuristic techniques also do not guarantee optimal solutions, the counter less evidence has shown that most of the time they reach near optimal solutions within a polynomial time. These techniques are based on natural phenomenon and some of the examples include genetic algorithms, backtracking, neural networks, simulated annealing and ant colony algorithms, etc

3.3.1- Background of backtracking

The matching problem between spectrum supply (TVWS pool) and spectrum demand (players seeking for UHF spectrum) is addressed by the COGEU broker utilizing Backtracking model [12]. Backtracking has been utilized to solve several frequency assignment and optimization problems in wireless networks [13]. According to the literature Backtracking is an algorithm for solving a constraint satisfaction problem, which traverses the search graph in a depth-first manner. The order of the variables can be fixed in advance or determined at run time. The backtracking algorithm maintains a partial solution that denotes a state in the algorithm‟s search space. Backtracking has three phases: a forward phase in which the next variable in the ordering is selected; a phase in which the current partial solution is extended by assigning a consistent value, if one exists, to the next variable; and a backward phase in which, when no consistent value exists for the current variable, focus returns to the variable prior to the current variable. As far as channel allocation is concerned, backtracking algorithm considers a number of input parameters (i.e. TVWS availability), as well as demand parameters from secondary systems that require access to TVWS. In every channel allocation problem, a number of constraints exist. In COGEU frequency allocation problem, the following constraints have to be taken into account:

TVWS availability (i.e. the resources to be managed)

Interference among secondary TVWS systems

Maximization of spectrum utilization by reducing spectrum fragmentation

Priorities (e.g. Public Safety systems)

It has to be noted that, in COGEU frequency allocation problem, hard constraints exist (i.e. interference). A secondary system could not operate without taking into account these constraints. There are also soft constraints (i.e. fragmentation). The secondary systems may operate without taking into account these constraints, but the matching process will not provide optimal results. In


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terms of soft constraints, penalties could be given for the systems that do not follow them (i.e. higher price). Backtracking is a systematic way to iterate through all possible configurations of a search space. These configurations may represent all possible arrangements of objects (permutations) or all possible ways of building a collection of them (subsets). In COGEU broker, the Backtracking algorithm is used to represent all possible arrangements in the TVWS pool that matches the secondary systems demand. These arrangements are named as permutations. Other situations may demand enumerating all spanning trees of a graph, all paths between two vertices, or all possible ways to partition vertices into color classes. The problems above have a common target; generating each one, possible configuration exactly once. Avoiding both repetitions and missing configurations means that a systematic generation order must be defined. In this context a combinatorial search solution is modeled, as a vector , where each element is selected from a finite ordered set . Such a vector might

represent an arrangement where contains the element of the permutation. The vector might also

represent a given subset , where is true if and only if the element of the universe is in . The vector can even represent a sequence of moves in a game or a path in a graph, where contains the

event in the sequence. At each step in the backtracking algorithm as it is shown in Figure 16, a given partial solution is tried to be extended by adding another element at the end. After extending it, it must be tested whether a solution exists. If so, the output should be printed or counted. If not, it must be checked, whether the partial solution is still potentially extendible to some complete solution. Backtracking constructs a tree of partial solutions, where each vertex represents a partial solution. There is an edge from to if node was created by advancing from . This tree of partial solutions provides an alternative way to think about backtracking, for the process of constructing the solutions corresponds exactly to doing a depth-first traversal of the backtrack tree. Viewing backtracking as a depth-first search on an implicit graph yields a natural recursive implementation of the basic algorithm.

do

Figure 16 Depth-first search code of Backtracking

Although a breadth-first search could also be used to enumerate solutions, a depth-first search is greatly preferred because it uses much less space. The current state of a search is completely represented by the path from the root to the current search depth-first node. This requires space proportional to the height of the tree. In breadth-first search, the queue stores all the nodes at the current level, which is proportional to the width of the search tree. For most interesting problems, the width of the tree grows exponentially in its height. Among the advantages cited for the depth-first search approach is:

Its simplicity.

Low computer storage requirements.

The ability to obtain both heuristic and optimal solutions to resource-constrained, project scheduling problems.

3.3.1.1 Backtracking algorithm Backtracking ensures correctness by enumerating all possibilities. It ensures efficiency by never visiting a state more than once. Because a new candidates array is allocated with each recursive procedure call, the subsets of not-yet-considered extension candidates at each position will not interfere with each other.


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The application-specific parts of this algorithm consist of five subroutines presented below, whereas the implementation of the Backtracking is appeared in Annex I:

1. – This Boolean function tests whether the first elements of vector from a complete solution for the given problem. The last argument, input, allows us to pass general information into the routine. We can use it to specify the size of a target solution.

This makes sense when constructing permutations or subsets of elements, but other data may be relevant when constructing variable-sized objects such as sequences of moves in a game.

2. – This routine fills an array c with the

complete set of possible candidates for the position of , given the contents of the first positions. The number of candidates returned in this array is denoted by ncandidates. Again, input may be used to pass auxiliary information.

3. – This routine prints, counts, or however processes a complete solution once it is constructed.

4. and – These routines enable to modify a data structure in response to the latest move, as well as clean up this data structure if it is decided to take back the move. Such a data structure could be rebuilt from scratch from the solution vector as needed, but this is inefficient when each move involves incremental changes that can easily be undone.

Finally it is included a global finished flag to allow for premature termination, which could be set in any application-specific routine. The basic Backtracking code is presented in Figure 17 below:

/* found all solutions yet? */ /* candidates for next position */

/* next position candidate count *//* next position candidate count */ /* counter */

/* terminate early */

} }

}

Figure 17 Basic Backtracking code

3.3.1.2 Constructing all permutations, combinations between TVWS and secondary Systems

Counting permutations of is a necessary prerequisite to generating them. There are distinct choices for the value of the first element of a permutation. Once is fixed, there are

candidates remaining (i.e. COGEU secondary systems) for the second position as shown in Figure 18 below, since any value except (i.e. repetitions are forbidden in permutation) is acceptable. Repeating this argument yields a total of distinct permutations.


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This counting argument suggests a suitable representation. Set up an array/ vector of cells. The

set of candidates for the position will be the set of elements that have not appeared in the elements of the partial solution, corresponding to the first elements of the permutation. In the scheme of the general backtracking algorithm, , and is a solution

whenever :

{ /* counter */

/* who is in the permutation? */

} }

Figure 18 Construction of candidates in backtracking

Testing whether is a candidate for the slot in the permutation can be done by iterating through all elements of and verifying that none of them matched. However, it is preferred to set up a bit-vector data structure to maintain which elements are in the partial solution. This gives a constant-time legality check. Completing the job requires specifying process solution and is a solution, as well as setting the appropriate arguments to backtrack. The implementation of basic backtracking functions is presented in Figures below (Figure 19, Figure 20, Figure 21).

/* counter */

Figure 19 Function of process solution

Figure 20 Function of is a solution

/* solution vector */

Figure 21 Function of generate permutations

The C++ code for all procedures of the Backtracking Algorithm is given in Annex I.


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3.3.1.3 Pruning technique: reinforcement to backtracking Backtracking ensures correctness by enumerating all possibilities. Enumerating all permutations of

vertices of the graph and selecting the best one yield the correct algorithm to find the optimal TVWS allocation for COGEU spectrum broker. For each permutation, we could see whether all edges implied by the allocation really exists in the graph G, and if so, add the weights of these edges together. However, it would be wasteful to construct all the permutations first and then analyze them later. Suppose our search started from vertex , and it happened that edge was not in . The next permutations enumerated starting with would be a complete waste of effort. Much better would be to prune the search after and continue next with . By restricting the set of next elements to reflect only moves that are legal from the current partial configuration, we significantly reduce the search complexity. Pruning is the technique of cutting off the search the instant we have established that a partial solution cannot be extended into a full solution. For frequency allocation, the best allocation is investigated, that satisfies all secondary systems of COGEU specific scenario. Suppose that in the course of the search an allocation can be found, whose cost is . Later, a partial solution may be

found, whose edge sum . This means that there is no other reason to continue exploring this node. This is because any allocation with this prefix will have cost greater than allocation , and hence is doomed to be non-optimal. Cutting away such failed partial allocations as soon as possible can have an enormous impact on running time.

3.3.1.4 Further improvements of backtracking The performance of backtracking can be improved by reducing the size of its expanded search space, which is determined both by the size of the underlying search space, and by the algorithm‟s control strategy. The size of the underlying search space depends on the way the constraints are represented (e.g. on the level of local consistency), the order of variables instantiation, and, when one solution suffices, the order in which values are assigned to each variable. Using these factors, two types of procedures have been developed: those employed before performing the search, thus bounding the size of the underlying search space; and those used dynamically during the search and that decide which parts of the search space will not be visited. Commonly used preprocessing techniques are arc- and path-consistency algorithms, and heuristic approaches for determining the variable ordering [14], [15], [16]. The procedures for dynamically improving the pruning power of backtracking can be conveniently classified as look-ahead schemes and look-back schemes, in accordance with backtracking‟s two main phases of going forward to assemble a solution and going back in case of a dead-end. Look-ahead schemes can be invoked whenever the algorithm is preparing to assign a value to the next variable. The essence of these schemes is to discover from a restricted amount of constraint propagation how the current decisions about variable and value selection will restrict future search. Once a certain amount of forward constraint propagation is complete the algorithm can use the results to:

1. Decide which variables to instantiate next, if the order is not predetermined. Generally, it is advantageous to first instantiate variables that maximally constrain the rest of the search space. Therefore, the most highly constrained variable having the least number of values, is usually selected.

2. Decide which value to assign to the next variable when there is more than one candidate. Generally, when searching for a single solution an attempt is made to assign a value that maximizes the number of options available for future assignments.

Look-back schemes are invoked when the algorithm is preparing the backtracking step after encountering a dead-end. These schemes perform two functions:

1. Deciding how far to backtrack. By analyzing the reasons for the dead-end, irrelevant backtrack points can often be avoided so that the algorithm goes back directly to the source of failure, instead of just to the immediately preceding variable in the ordering. This procedure is often referred to as backjumping.


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2. Recording the reasons for the dead-end in the form of new constraints, so that the same conflicts will not arise again later in the search. The terms used to describe this function are constraint recording and learning.

In the context of COGEU, pruning and backjumping procedures were utilized for the purposes of TVWS allocation process.

3.3.2- Backtracking implementation

The implementation of the proposed algorithm is based on ten files with C++ programming language code. The main files are the Backtracking.hpp and Kernel.cpp. All the other files keep information, which is important additionally to the Backtracking.hpp and Kernel.cpp classes. This section elaborates on the description of these classes. The .hpp files include the description of the class, while the .cpp files include the implementation of the class. Below a short description of each file can be found.

- Backtracking.hpp: This file includes only the class backtrack. This is the basic algorithm.

- Kernel.cpp: The functions of the class Kernel, include the important functions that backtrack calls. Some of these functions are, the IsSolution, ProcessSolution, PickCandidates, fits, inSpectrum, isStrictSolution, is RelaxSolution. These functions are next presented in more detailed.

- Kernel.hpp: This file includes just the description of the class Kernel.

- Main.cpp: This is the main class of the program and initiates the program and all the other classes.

- Service.hpp: This file gives the appropriate information for each secondary system (i.e bandwidth, priority, power, etc.)

- Slot.hpp: This file is used when a COGEU secondary system assigns to a channel/slot. The class slot returns the information of the secondary system.

- Spectrum.cpp: This file plays the role of the geo-location database. All channels are saved here with the initial state. (i.e. which channels are used from primary system and which are available, maximum transmission powers, bandwidths, transmission power from primary systems, etc.)

- Spectrum.hpp: This file includes just the description of the class Spectrum.

- CSVParser.cpp: In this file the GUI (Graphical User Interface) is implemented, which is taking inputs from the users about the number and the types of secondary systems that want to be assigned in one or more channels.

- CSVParser.hpp: This file includes just the description of the class CSVParser.

Figure 22 describes the logical diagram of the TVWS allocation algorithm based on Backtracking. Backtracking takes an input a partial solution. This partial solution results from the initial arrangement of COGEU primary systems. Therefore, the procedure “IsSolution” is checking for two conditions:

- if the secondary system, that requires access, has been allocated with a free channel or

- if the spectrum is full

If the procedure “IsSolution” is true, then the procedure “ProcessSolution” is called, in order to check whether the solution belongs to “Strict” or “Relax” type of solution. As it was already mentioned above, the solution that may serve all the secondary systems is named as “Strict”, while the solution that may serve a part of the secondary systems, is named as “Relax”. Both of these types utilize a buffer that saves the solutions.


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If the procedure “IsSolution” is false, then the procedure “PickCandidates” is called. In this step an iterative process begins until a solution to be found. According to backtracking, a solution consists of

candidates, which present the number of both primary and secondary systems of COGEU scenario. Also the allocation of the primary systems is already known by the partial solution, so it is only a need to find the secondary systems potential allocations. In every round of the iteration, the backtracking is checking to match the availability of TVWS with secondary systems demand, by taking into account all the specified constraints. Backtracking calls the procedure “InSpectrum”, in order to check if the current secondary system has already assigned to the spectrum. Also, backtracking calls the procedure “Fits”, in order to check if the current secondary system has the appropriate minimum power for the current channel of the spectrum and if more than one channel is needed. This procedure also checks if the current channel is utilized by a primary system. If so, then the algorithm moves to next channel and performs the same checks. If the candidate is not already assigned to a channel and if the candidate fits in current channel, then a solution appears and the process “IsSolution” is called. As it was already mentioned above the algorithm stores the solutions in two buffers regarding the congestion. For this reason the algorithm is enhanced with a decision making process, which is responsible to evaluate how much efficient is each solution based on the fragmentation score.

IsSolution

Partial Solution

PickCandidates

ProcessSolution

Add to “strict” buffer

YES

NO

IsStrictSolution

Candidate is already InSpectrum

Candidate Fits in SpectrumAdd to “relax” buffer

YESNO

NO

NO

YES

YES

Frag. Score[i] < Frag.Score[i-1]

Best Solution

Figure 22 Logic Diagram of Backtracking Process


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3.3.3- Backtracking integrated in the spectrum broker allocation process

The general structure of the spectrum broker allocation process, initially presented in Figure 11 is presented again in Figure 23, with the backtracking modules integrated and highlighted in red. The backtracking algorithm is used in Phase I and Phase II of the allocation process. Phase I starts with the external database providing the TVWS availability information in a specific geographic area, then the TVWS pool is updated with information provided by the internal TVWS repository (Section 3.1.1). Based on open demonstration of interest, the demand of TVWS is estimated and utilizing Administrative Incentive Pricing, the reference price of the TVWS is estimated (Section 3.2). Backtracking process in Phase I take into account the TVWS pool and the demand (system requirements) to create a spectrum portfolio, i.e. all possible matching solutions between secondary systems and the available TVWS. More specifically, in this process, a number of combinations of the available TVWS spectrum allocation to the secondary systems is provided. Some of them are not valid by taking into account the system requirements. Therefore, the next step is responsible to discard these combinations. At this point, it has to be noted that backtracking creates spectrum portfolio utilizing two buffers with possible solutions. The first buffer is named strict buffer and contains all possible solutions that satisfy all constraints. Since it is possible, not all constraints to be satisfied, backtracking creates a second buffer, namely relax buffer, which includes all solutions that do not satisfy all constraints. In every case, backtracking, chooses, one of two buffers to be considered as the spectrum portfolio. In the case where there is one or more strict solutions, the spectrum portfolio is considered the strict buffer. In Phase II and in case the TVWS demand is higher than offer, the auction mechanism will select the solution from the portfolio that maximize the broker profit (this will be detailed in Section 3.4). In case where demand of TVWS is lower than offer, Phase II includes again the backtracking process. At this stage, the backtracking algorithm is operating in order to allocate TVWS avoiding fragmentation of spectrum. More specifically, backtracking process provides all the possible solutions, either complete or partial, for service allocation on the available spectrum. For the case of partial ones all solutions are ranked based on services priority and the solution that includes the highest priority services is selected. In case there are multiple complete solutions, these are further analyzed and the one that provides the least fragmentation in the spectrum is selected. The available solutions are ranked based on the size of contiguous remaining white-spaces and favor those that provide for tighter allocation of services in order to allow for additional services in future deployments. Ranking is determined according to how much fragmented is the spectrum after the allocation process. COGEU uses the “smart” formula [20] to evaluate fragmentation factor. Finally, the ranking module provides the best solution regarding the frequency allocation. Note that, since demand is lower than offer, a fix price defined by the Benchmark Price Estimation step is used.


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Estimation of demand Section 3.2

START

Geo-location Data Base (DB2)

Section 2.1

Benchmark price estimation based on AIP (including

minimum price/ call price for auction)

Section 3.2

Open demonstration of interest in buying TVWS

Spectrum Portfolio Proposal based on Matching Algorithm

(MA) - TVWS allocation optionsSection 3.3

TVWS occupancyRepository

Section 3.1.1

Acquisition of Spectrum Bands (TVWS pool)

Enhancement of TVWS pool

Demand<=Offer Demand>Offer

Announce fixed price per MHz

Allocation of TVWS temporarily exclusive rigths

Update of TVWS

occupancy repository

Auction (Section 3.4)

TVWS allocation(Select one solution from MA)

Section 3.3

Announce the auction and the minimum/call price

Reception/analysis of bids

TVWS allocation(Select the solution from MA

that maximizes the profit)

Advertise the Spectrum Portfolio

Selection Criteria, e.g.

fragmentation

Phase II: Operation

Phase I: Preparation and Analysis

Ph

ase

III:

M

ain

ten

ance

Reception of SU’s final interest

Demonstration of the SUs interest

START

Bids from the SUs

Spectrum Trading Policies

RepositorySection 3.1.2

Regulator

Creation of buffers with channels for each Secondary System

The algorithm utilizes buffers and the number of Secondary Systems in order to build the potential combinations of allocation

The algorithm removes the invalid combinations of allocation. Bac

ktra

ckin

g P

roce

ss

Allocation of channels based on constrains:

Power Level , Bandwidth, Priority

Ranking based on fragmentation

Best Solution

Bac

ktra

ckin

g P

roce

ss

Figure 23 Backtracking integrated in the spectrum broker allocation process

3.3.4- Performance evaluation

This section elaborates on the evaluation of the matching algorithm based on several performance metrics. Following a COGEU scenario is presented to illustrate and test the Backtracking algorithm.

3.3.4.1 COGEU evaluation scenario Figure 24 shows a COGEU scenario with three LTE cells and one Public Safety network willing to operate in TVWS. The Public Safety system operates with high priority and requires 1 MHz bandwidth. In the same area, there are three LTE systems asking for 5MHz, 10 MHz and 20 MHz contiguous bandwidth. Uplink and downlink connections of LTE are both served with the same priority, (i.e. medium) The LTE systems are FDD, therefore duplex gap constrains are considered (minimum frequency separation between Uplink and Downlink channels). Table 6 details the secondary systems requirements in this scenario. The time dimension is considered allowing secondary systems to request TVWS access and enter into the allocation process in specific Time periods. In this scenario four time periods are considered, when secondary systems may enter or exit into the allocation process of COGEU spectrum broker. For instance the LTE 1 system, utilizing 20MHz bandwidth is accessing TVWS for two consecutive time periods. The time period during which the secondary systems are able to operate was defined to be 1 hour. The scenario which was simulated includes four time periods (1 hour each). In the case where a secondary system requests to operate more than one hour, then the allocation process assigns TVWS for the requested number of time periods.


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Service Type Power (Watt) Bandwidth (MHz) Priority Time period start Time period end

LTE 1 1 20 Medium 0 2



Public Safety 0.1 1 High 1 4

Table 6 Secondary systems requirements used in the validation scenario

It must be noted that if TVWS spectrum has been allocated in a specific time period to a secondary system and during this period a new system enters into the allocation process, then the previous one is not bothered unless its time period ends. It is assumed that the already TVWS spectrum is occupied and the algorithm is not allowed to assign it to other secondary systems. The information regarding the TVWS already booked is stored in the internal TVWS repository described in Section 3.1.1. Additionally, it is important to note that that the smallest part of TVWS spectrum able to be allocated to a secondary system is 1MHz (spectrum granularity). In order to avoid interference between secondary systems, a guard band of 1MHz is considered.

COGEU SPECTRUM BROKER

GEOLOCATION

SPECTRUM

DATABASE

TVWS

Cognitive

Radio Base

Station

LTEoperator

(20 MHz)


CR + GPS + CR+GPS


TVWS Matching Algorithm

DVB-T

LTEoperator

(10 MHz)

Public Safetyoperator

(1 MHz)

CR + GPS

CR+GPS

CR + GPS CR+GPS

Cognitive Radio

Base StationCognitive

Radio Base

Station

CR + GPS

TV White

Spaces Area

Cognitive

Radio Base

Station

LTEoperator

(5 MHz)

PMSE

Figure 24 COGEU scenario with cellular and Public Safety networks operating in TVWS area.

Figure 25, represents a diagram with the available TVWS channels in Munich area after real conditions measurements conducted by IRT and reported in D4.1. This TVWS pool is used in this section to evaluate the performance of the backtracking algorithm.


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TVWS available in Munich area. Symbolic notation for y-axis: (0: Channel occupied by DVB-T; Low: Adjacent channel with low power; Max.: Free DVB-T channel)

Figure 25 TVWS available in Munich area (from COGEU D4.1).

3.3.4.2 Example of the TVWS matching process According to the COGEU evaluation scenario presented in Table 6 , the allocation process starts at time zero with only one secondary system requesting access to TVWS, i.e. the LTE 20MHz. Figure 26 shows the results after the first allocation process performed by the Backtracking algorithm. According to the results, the LTE uplink is allocated in channel 50, 51 and part of channel 52 (green bar in Figure 26). The LTE downlink is allocated in channel 58, 59 and part of channel 60 (light blue bar in Figure 26). A band guard of 1 MHz (black bar in figure below) is included to avoid interference with other future systems that may operate in adjacent frequencies.

Figure 26 Initial TVWS allocation example (LTE 20 MHz)

The second request for access to TVWS is done by an additional secondary system (Public Safety system) to the one that already has been granted access to the available spectrum (LTE system in 20MHz) according to the COGEU scenario defined. The initial system is an LTE one, still operating utilizing 20MHz and the new one is requesting access for first time with high priority. Figure 27, represents the results of second allocation phase, where the LTE system from the initial allocation remains in the same channels. The new secondary system (i.e. Public Safety) has been accepted by the Backtracking algorithm to operate in channel 49 (red bar in figure below) with a high priority. In this case of allocation a low power channel was assigned by backtracking to Public Safety, rather than to waste a max. power channel. Also in this case a guard band (black bar in figure below) is allocated to avoid possible interference with adjacent systems.


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Figure 27 Second phase of TVWS allocation example (LTE 20 MHz + Public Safety 1 MHz)

The 3rd

request for access to TVWS is done by an additional LTE system requesting 10MHz. At this time, the first LTE system (20MHz) stops to operate releasing the frequencies allocated to it by the 1

st

allocation process but the Public Safety system is still operating using the frequency assigned to it from the 2

nd allocation process. Figure 28, represents the results from the 3

rd allocation process,

where Public safety system remains in channel 49. Additionally, the allocation process assigns to the LTE 10 MHz system, channel 41 and a part of channel 42 for uplink (purple bar in the figure below). Channel 58 and a part of channel 59 is allocated for the LTE 10 MHz downlink (yellow bar in the figure below). A guard band of 1 MHz is included for interference avoidance.

Figure 28 Third phase of TVWS allocation example (Public Safety 1 MHz + LTE 10 MHz)

The 4th request for additional TVWS spectrum usage is done by a new LTE system operating in 5MHz.

At this time, the Public safety system and the LTE 10 MHz system from previous allocation phases are still operating. Figure 29, represents the results of the 4

th allocation process, where the new LTE

system has been assigned 5MHz of the available spectrum. The new spectrum assigned is the left part of channel 42 for the uplink and part of channel 46 for its downlink. Again, the appropriate guard intervals are taken into account for both downlink and uplink, in order to avoid possible interference with future systems that may operate utilizing adjacent frequencies.


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Figure 29 Fourth phase of TVWS allocation example (Public Safety 1 MHz + LTE 10 MHz + LTE 5 MHz)

3.3.4.3 Evaluation of TVWS fragmentation

An important metric utilized in order to evaluate the efficiency of the allocation process, is the fragmentation score. Fragmentation score proves/depicts how fragmented TVWS spectrum becomes after the allocation process. Since most of technologies require contiguous spectrum assignments, fragmentation can undermine future allocations and the overall broker performance. Fragmentation score varies from 0 up to 1, where 0 denotes no fragmentation and 1 denotes full fragmentation. Selection of the optimum solution regarding the TVWS allocation is determined according to how much fragmented the spectrum is after the allocation process. In a general context, there are several ways to evaluate fragmentation factor [18], [19]. To evaluate the Backtracking algorithm, the “power fragmentation method” is adopted. The Power fragmentation method utilizes the “smart” formula in comparison with the other methods available in literature [20]. “n” is the number of fragments, fi is the size of i-th fragment. fi is raised to the power of p . The fragmentation scores is given by,

[20].

Figure 30, shows the fragmentation score obtained by the Backtracking algorithm with pruning and no pruning technique and for the COGEU scenario presented in Table 6. The fragmentation score (F) is computed after each time period (allocation process). According to the results, it is clear that Backtracking with pruning technique provides higher values of fragmentation compared with no pruning. This is mainly because pruning technique is more heuristic and fast in the search of a local optimum solution.


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Figure 30 Fragmentation score computed with the power fragmentation method for COGEU scenario.

3.3.4.4 Solutions explored by the algorithm

Simulations also provide results regarding the number of solutions that were searched/explored by the proposed algorithm in order to find the best/optimum solution. Next figure below depicts the number of solutions explored either using pruning or no pruning technique of backtracking. It is clear that pruning technique provides smaller number of solutions explored, i.e., low complexity, at the cost of some degradation in terms of fragmentation as showed in Figure 30.

Figure 31 Solutions explored during simulations for different time periods, COGEU scenario.

3.3.4.5 TVWS utilization TVWS utilization is defined as a percentage of the spectrum used after the allocation process over the maximum available spectrum, excluding protected channels (used by incumbent systems) as well as frequencies left intentionally out of the allocation process used as guard bands in order to minimize interference between COGEU secondary systems. This metric can be formulated as:

,

0,64

0,66

0,68

0,7

0,72

0,74

0,76

0,78

0,8

0 1 2 3

Frag

me

nta

tio

n

Sco

re

Time Periods

Pruning

No Pruning

0

100

200

300

400

500

600

700

0 1 2 3

Solu

tio

ns

Exp

lore

d

Time Periods

Pruning

No Pruning


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where n is the number of secondary systems, S is the number of TVWS channels used after the allocation process and f denotes the overall number of the available TVWS channels. Figure 32 represents the simulation results of TVWS utilization with pruning and no pruning techniques of backtracking process after each allocation process. These results are produced based on the COGEU validation scenario presented in Table 6. It can be observed that according to this scenario, TVWS utilization is not affected by the use of pruning or no pruning techniques, since the demand is quite low in comparison to the spectrum offer. In this case, all secondary systems are satisfied.

Figure 32 TVWS Utilization for the COGEU validation scenario.

3.3.4.1 Algorithm complexity

In a general context, algorithms can be classified by the amount of time they need to complete compared to their input size. There is a wide variety: some algorithms complete in linear time relative to input size, some do so in an exponential amount of time or even worse, and some never halt. Additionally, some problems may have multiple algorithms of differing complexity, while other problems might have no algorithms or no known efficient algorithms. There are also mappings from some problems to other problems. Owing to this, it was found to be more suitable to classify the problems themselves instead of the algorithms into equivalence classes based on the complexity of the best possible algorithms for them.

Moreover, in mathematics a combination is a way of selecting a number of elements out of a larger set, where (unlike permutations) the order of these elements does not matter. More formally a k-combination of a set S is a subset of k distinct elements of S. If the set has n elements the number of k-combinations is equal to the binomial coefficient

and the set of all k-combinations of a set S is sometimes denoted by .

Combinations can consider the combination of n things taken k at a time without or with repetitions [17]. In the above example repetitions were not allowed.

With large sets it becomes necessary to use mathematics to find the number of combinations. In this context, backtracking belongs to combinatory algorithms category and the complexity is:

)

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

30,00%

35,00%

0 1 2 3

TVW

S U

tiliz

atio

n (

%)

Time Periods

Pruning

No pruning


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Where 'k' is the number of secondary systems requesting access to the available spectrum during different time periods and 'n' is the number of free frequencies (slots of 1MHz). In the COGEU

validation scenario there are 17x8=136 MHz available in Munich area. For instance for two Public Safety secondary systems, 136! / (2! * (168-2)!) = 9180 are the possible valid solutions. In fact possible solutions may be less since guard intervals of 1 MHz have also to be considered. When algorithm complexity is evaluated - i.e. O() – guard intervals are considered. For 3 public safety secondary systems, the number of possible solutions is 410040 and for 4 such systems the number of possible solutions is 13633830. Figure 33 represents the algorithm complexity, i.e. O(), of the proposed allocation process when a single secondary system is requesting access to TVWS, i.e., a Public Safety system (1 MHz), a LTE (5 MHz), a LTE (10 MHz) and a LTE (20 MHz). It has to be noted that for a single Public Safety system requesting 1 MHz, 136 solutions have to be considered (i.e. the same number as the number of available frequencies). For other secondary systems such as LTE (5 MHz) where 5 subsequent frequencies are required, possible solutions are less but the complexity increases since the algorithm has to additionally consider assigning contiguous channels. The last column of Figure 33 shows the complexity associated to the allocation process of the five secondary systems requesting access to TVWS together. The algorithm complexity increases dramatically in this case since the process has to satisfy more requests by taking into account several constraints as they are defined in order to avoid interference etc.

Figure 33 Algorithm complexity

3.3.5- Conclusion

In this section, a optimization algorithm based on Backtracking process is designed, simulated and evaluated in order to match spectrum supply from a TVWS pool and spectrum demand from secondary systems. This algorithm is applied in cases, where spectrum demand is lower than spectrum supply in the overall allocation process adopted by the COGEU broker. In this case, a fix price per MHz, defined by the Benchmark Price Estimation step is used. Backtracking is a systematic way to iterate through all possible configurations of a search space. These configurations may represent all possible arrangements of objects (permutations) or all possible ways of building a collection of them (subsets). Pruning is the technique of cutting off the search the instant we have established that a partial solution cannot be extended into a full solution. Pruning accelerates the matching algorithm. In COGEU broker, the Backtracking algorithm with pruning is used to represent all possible arrangements in the TVWS pool that matches the secondary systems demand. The available solutions

0

2E+36

4E+36

6E+36

8E+36

1E+37

Public Safety LTE 5MHZ LTE 10MHz LTE 20MHz ALL Systems

136 3.6E+8 4.3E+18 4.5E+27

1.1E+37

Alg

ori

thm

Co

mp

lexi

ty

Secondary Systems


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are ranked based on the size of contiguous remaining white-spaces and favor those that provide for tighter allocation of services in order to allow for additional services in future deployments. A COGEU scenario that adopts preliminary studies of TVWS availability in Munich is considered in order to illustrate and test the matching algorithm. The test scenario includes time dimension, allowing secondary systems to request TVWS access and enter into the allocation process every “Time period”. The performance of the algorithm is evaluated by taking into account several metrics such fragmentation, TVWS utilization and complexity. In particular, an important metric utilized in order to evaluate the efficiency of the allocation process, is the fragmentation score. Fragmentation score proves/depicts how fragmented TVWS spectrum becomes after the allocation process. Since most of technologies require contiguous spectrum assignments, fragmentation can undermine future allocations and the overall broker performance. The simulation results obtained confirmed the efficiency of the proposed algorithm to optimally allocate TVWS to secondary systems taking into account and respecting all constraints defined in the specifications of the use case scenario (allocation time, frequency bandwidth and transmit power). According to simulation results, it is clear that pruning technique provides smaller number of solutions explored, i.e., low complexity, at the cost of some degradation in terms of fragmentation. A complimentary process to the one presented above (i.e. backtracking process), is the process of TVWS allocation based on auctions. This process is presented in the next section of this deliverable taking into account the cases, where spectrum demand is higher than spectrum supply.


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3.4- TVWS allocation based on auctions

The matching optimization-based approach, presented before in Section 3.3, for the case of the spectrum demand not exceeding the spectrum offer allows satisfying all secondary-users requests, and gives more freedom to emphasize the technical-efficiency aspect of the spectrum usage (minimization of the spectrum fragmentation). The auction-based approach is used for the case of the spectrum demand exceeding the spectrum offer, when not all the requests can be satisfied. This approach selects a subset of the secondary users to be allocated the spectrum, and emphasizes the economic aspect of dynamic spectrum access. Note, that in both cases the TVWS allocation has to satisfy the spectrum trading policies in the considered area (state or country), which include regulations, priorities, restrictions, etc. In this section, the TVWS allocation is presented, which is based on spectrum auctions that aim at the broker‟s economical profit maximization. The contribution presented below, addresses the case of the secondary users‟ spectrum demand being higher than the spectrum offer from the flow-chart presented in Figure 11 (when general procedure of spectrum allocation is presented). In other words, the possibilities of allocating the available TVWS after considering the coexistence of the secondary users do not cover the demands declared by them. In this case, the spectrum auction is the proper approach to select a subset of the secondary users whose offered price would maximize the economical benefit of the broker. To be precise, this section address the block named “Auction” in the abovementioned flow-chart as well as specific properties of the “Spectrum Portfolio Proposal based on Matching Algorithm” in the case, when the spectrum demand exceeds the offer. In the considered approach, the players competing for the spectrum resources are secondary users, i.e. the mobile communication operators or other service providers, who have special incentives to use the spectrum available in TVWS for their own profit maximization. These players have their own users and they have to assure them services with guaranteed QoS requirements. When the traffic intensity increases in a particular area and at the particular period of time, e.g. during the day peak-hours, these players would be interested in leasing the amount of resources, with which they would be able to assure connectivity and the QoS of their own users (subscribers). For these players we consider an auction of resources conducted by the broker. The auction results depend on the frequency-power resources available (TVWS pool stored in the geo-location data-base, as well as in the broker internal TVWS occupancy repository) and the bids submitted by the competing players. These bids in turn depend on the players‟ valuation of the available resources. In the next section this situation is described in detail. This approach is based on combinatorial auction. More about combinatorial auction may be found in [38], [39], [40].

3.4.1- General description and assumptions

Let us consider a system where the spectrum broker is responsible for leasing the spectrum to the auction participants (the players). The number of players is denoted by K. The total available spectrum is denoted by B, and is a multiple of the multiplex DVB-T channel bandwidth (8 MHz). The commodity of the auction is the spectrum which may be sold in segments having different sizes and power requirements. The goal of the spectrum broker is to sell the spectrum into auction winners in segments, which can have different or the same size. (Note that the broker‟s profit maximization may result in multiple auction winners, not just one.) Let us denote the segment size in MHz by bi and number of segments as I. As an example a situation may be considered when we have I = 3 available segments with well-defined transmit-power limitation, size of the first one is b1 = 5 MHz, the second one is b2 = 10 MHz and the third one is b3 = 20 MHz and the total available spectrum 24 MHz (which will be redistributed between users demanding sizes b1, b2 and b3 e.g. two users demanding 5 MHz and one user demanding 10 MHz). It has to be mentioned, that segment size is connected with minimum transmit power which must be provided for transmission (secondary system requirement). The segments‟ sizes may be related to typical frequency-channels requirements for contemporary or future radio communication systems, e.g. LTE, UMTS, WIMAX channels. The total spectrum may be sold to N participants, each one receiving an exclusive subset of segments. The final distribution of these segments depends on the bids of all players and the profit maximization function.


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Let us formulate the problem, and our auction rules. The auction is conducted by the spectrum broker which informs the players about the segments which can be bought. This information originates from geo-location database and form the broker‟s internal spectrum repository, in which the spectrum occupancy information is stored as the maximum allowable transmit-power level in a given frequency channel for a given period of time. This information does not have to be so detailed for the purpose of presenting the spectrum-offer, therefore it is processed by the broker and transmitted to the players as available segments of spectrum that can be used by typical services offered by the players (secondary users). After this information is received by the auction participants, they send their bids for the segments which they want to buy. A basic bid consists of two pieces of information: the spectrum segment of interest (the bandwidth and power regardless, the central frequency), and the offered price. The broker collects all bids and adds them to one of the lists of bids that is created for a given segment of spectrum, and sorts the lists in descending order. When two players send the same bid then the time decides which bid is on higher position on the list. There are a number of possible auctions that can be conducted, the most popular and relevant ones being: an English or Dutch auction, a sealed first-price or a sealed second-price (Vickrey) auction. The sealed auctions eliminate the overhead traffic necessary to inform the bidders on the current status of bids. Moreover, they eliminate the impact of the random delay in communication between the bidders and the broker on the auction results. Therefore, below, we consider and provide results for the English sealed-bids first price auction. In the second year of COGEU project, other auctions we will be also considered. The bidders define their bids using their own optimization tools. When auction ends the bids are used to identify the ones that maximize the payoff (the profit of the broker) and that are finally accepted for the spectrum assignment to the players. To summarize, the auction-based procedure of spectrum assignment was defined in 3 major phases as shown in Figure 11, (Note that the auction process is part of the overall COGEU broker flow chart presented in Figure 11:

Auction announcement when the spectrum demand exceeds the spectrum offer,

Submission of bids by the players, reception and analysis of these bids by the broker

Auction solution (optimization of the broker‟s benefit) and spectrum assignment.

This process is presented in Figure 34 In the considered scenario, the broker decides on the TVWSs allocation only for the next time period (slot). It is still an open question how often the auction will be run and what is the minimum time period for which the spectrum will be allocated. One can envision that the auctions will take place not more often than once per day and the allocation of the spectrum (or spectrum reservation within 24 hours) to a given player will be possible for no less than a few hours. Allocation time may be also longer, such as week or month. In a certain moment of time, the auction may be solved (and resources may be allocated) for the next time period. Two types of spectrum assignment are considered below. The first one uses contiguous spectrum size for auction, the second one uses non-contiguous spectrum. The first implication of this difference in the spectrum availability is in spectrum-segments definition and presentation to the players (the first phase of the algorithm). The problem formulation is similar but its complexity is different in both cases. In first case, one contiguous spectrum block has to be distributed among the bidders. In the second case fragmented spectrum blocks are divided. This terminology has nothing common with the players‟ demands because they demand only continuous spectrum segments for their applications (LTE, 3G, or Public Safety). LTE or 3G channels may request fragmented spectrum, if the whole segments are assigned to the uplink and downlink channels.


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Figure 34 Sealed-bid auction algorithm for TVWS allocation

3.4.1.1 Problem formulation for the contiguous TVWS block

The process starts from the first case‟s description, which is easier to understand. This system works on only one contiguous spectrum range B which is available for trading. From the geo-location database and from its repository, the broker gets the information about B and the maximum allowed power P in this segment. The available segment sizes were introduced as bi and connected with it the minimum power which is denoted by Pi,min. So now using B, bi and Pi,min new variable si can be defined, called maximum bids taken from each list to payoff calculation given by:

min, i

i

i

PPif

b

Bs

(1)

To better understand that value let us consider the previous example, resulting in: s1 = 4, s2 = 2, s3 = 1, what means that only 4 bids are taken from the list of bids for 5 MHz segment, 2 bids from the 10 MHz segment list and 1 bid from 20 MHz segment list for the calculation and optimization the payoff function. If there are fewer bids than si then all bids are taken to payoff calculation. It is expected that in rural areas (where there is a lower number of user and business activity is also smaller) there will be less bids on each list, and in the cities (where spectrum is more valuable) there will be a higher number of bids on each list for spectrum segments. Let us denote the bids values taken from each lists as Pri,j where i is the number of list (from 1 to I) and j is the position on the list. Moreover, there is the minimum price condition for each list (denoted by Pri,min) which must be fulfilled and can be written as:

jiiPi

,min, Prr 0

(2)

This minimum-price condition assures the minimum profit of the spectrum broker. It can be also added for some services which are defined by the market regulator or may be based on Administrative


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Incentive Pricing which is described in Section 3.2. As an example the situation with no minimum price for public safety applications but with the minimum price for mobile telephony services may be considered. It is natural that the minimum price should be established rationally because some players may not be interested in taking part in auction. The auctioned spectrum bandwidth BA must not be larger than the available spectrum bandwidth B:

BBA (3)

The broker is interested in maximization of the payoff function which describes the profit of the broker in some monetary units given by:

I

i

m

j

jii

i

m1 0

,Pr (4)

where mi is the number of bids accepted from the i-th list of bids. The values of mi are integer values and are limited by the following condition:

ii sm 0 (5)

Moreover for all Pi,min the following condition is sufficient and it can be rewritten (3) in the following way (it will simplify the process of finding the auction solution):

BbmBI

i

iiA 1

(6)

Using the presented notation a user‟s bid is a pair of a given segment (and the list) number i and the

price that player k is going to pay for it ki, , where ki, belongs to the list of bids for this list:

jiki ,, Pr (7)

which means that n-th user is interested in obtaining the spectrum having bandwidth bi, in transmitting

with power no higher than Pi, and wants to pay for this spectrum ki, monetary units.

Having all variables defined in the previous sections we can define the result of the auction as a solution of the following optimization problem:

Zm

Pi

BB

sm

m

i

jii

A

ii

I

i

m

j

jii

i

(4)

Prr (3)

(2)

0 (1)

subject to

Pr

maximize

,min,

1 0

,

(8)

where Z is the set of integers. This problem is a linear programming problem with integer unknowns. This is an NP-hard problem and efficient tools should be used for minimization the dimension of the problem. Let us note that the Matching Algorithm based on backtracking (described in Section 3.3) can provide some potential solutions for the spectrum allocation that can be further considered for the broker’s benefit maximization. Still a number of possible solutions in the case, when the spectrum demand exceeds the spectrum offer, may be very high and some techniques narrowing the number of analysed solutions are required. For contiguous spectrum with homogeneous power this problem may be reduced due to the second condition from (8) may be replaced with (6) and branch and cut technique [41] is then more efficient. This technique, which is described later, may be also used for non contiguous spectrum. Consideration of various available power-constraints in the contiguous spectrum causes that the auctioneer has to consider this contiguous spectrum as non contiguous spectrum, because some spectrum ranges available for one product may overlap other spectrum ranges available for other spectrum products. It increases the number of options to be examined by the broker during optimization because for each combination of bids the broker has to check not only one mathematical relation (condition (6)) but also has to fit those spectrum products in the spectrum axis.


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Finally, two spectrum segments can be assigned to the players if the interference occurring between them is kept at the acceptable levels defined by the respective transmission schemes. In general, if such interference occurs, this potential auction solution must be disqualified.

3.4.1.2 Problem formulation for non-contiguous TVWS Previously, the case of one contiguous spectrum block, i.e. with assumption about the available bandwidth B but in one piece, was discussed. Now we extend our considerations to the case of having the same total available spectrum bandwidth, but in N fragments. We can describe it by the following equation:

N

k

nBB1

(9)

where Bk is the bandwidth of one spectrum fragment (one white space). Each Bn is connected with Pn which is the maximum transmit power in this segment. The first main difference between the contiguous and non-contiguous spectrum availability models is in the calculation of the values si. To perform this step the broker must sort all fragments of the non-contiguous spectrum according to their bandwidths in a descending order. Without the loss of generality we can describe it in the following way: B1 , B2 , … , Bn , … , BN Thus, using the available segment sizes as bi for each spectrum fragment the broker must calculate the values ni in the following way:

N

n

nii

in

i

nni

ss

PPif

b

Bs

n

1

,

min,

,

(10)

where Pn is maximum allowed power in the n-th block Bn obtained from the geo-location database In the above considerations it has been assumed that all players demand the spectrum in one block. This situation would reflect one of the COGEU scenarios, in which the players are 3G or 4G operators and use the TDD transmission scheme, e.g. LTE in the TDD mode. However, in our COGEU scenarios, the players operating in the FDD mode are also considered, e.g. in LTE FDD. In such a case, the duplex frequency gap between uplink and downlink must be provided. This frequency gap is usually defined by the standard and takes the transmitter emission power and the receiver power-sensitivity into account. In our simulations presented in the next sections, we have considered just the TDD case, i.e. single spectrum segment request, however for the future work, FDD case will also be examined. Here, below, we only give the theoretical description of the problem, when the spectrum is demanded by all players operating in the FDD mode. In such a case, relation (10) can be formulated in the following way (the spectrum broker must check the possibility of providing the duplex frequency gap):

2/

2/

1

,

min,

,

N

n

nii

in

i

nni

ss

PPif

b

Bs

n

(11)

The rest of calculations are the same as in the previous situation when the TVWS spectrum is available in one contiguous segment (in which N = 1 and relation (11) is not needed). The problem formulation is similar but the spectrum broker has to check if the considered combination of products fits the available spectrum due to the fact that we have spectrum segments having different sizes and the size of each leased spectrum product must be considered in auction solution (assumption (6) cannot be used in this case).


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3.4.2- Branch-and-cut algorithm for the auction solution

As it was mentioned in the previous section, the problem formulated in (8) is the integer linear programming problem. Without the assumption from (6) for contiguous spectrum we have to check the following number of possible combinations of mi to find the best solution (for all cases):

I

i

isS1

1 , (12)

However, using that assumption, and the branch-and-cut technique, in a proper way, the problem dimension decreases. We also have to mention that the dimension of the problem (the number of combinations of values of mi to check for the optimization, i.e. payoff-maximization procedure) is strictly dependent on three factors: the number of segments, the total size of the available bandwidth and the sizes of segments. The role of the first two factors is obvious. The third factor has even stronger influence on the number of combinations to check. If the segment sizes are smaller then the spectrum broker has to check dramatically more values because si is getting higher. The branch-and-cut technique is a method of combinational optimization for integer linear programming concepts with all the unknowns being the integer values. The problem is split into smaller problems in the tree form. If the solution satisfies the criteria of optimization it becomes a potential solution (leafs on the tree), and will be used in the final checking of the utility function. Each level in the tree represents the possible values of the optimized variable. This method allows cutting the branches which are not used in our problem due to the conditions in optimization problems and we have less possible combinations of searched variables. The branch-and-cut technique is used to solve the considered auction with non identical objects for contiguous and non contiguous spectrum in order to limit the number of computations. The complete algorithm of the auction has been presented in Figure 34. Let us remind the following algorithm of the auction for spectrum segments:

1. The spectrum broker gets data from geo-location database (DB) about the available TVWS in the selected area.

2. The spectrum broker is processing the information from the DB and broadcasts the information about available spectrum segments‟ sizes (bi,si), the allowable transmission power in these segments, and the minimum price for each segment using some specified broadcast channel.

3. The players send their bids to participate in the auction (the players interested in more than one segment send more than one bid for different segments).

4. Once the bids are submitted, the spectrum broker solves the auction using the branch-and-cut technique.

The broker assigns spectrum to players and broadcasts this information. To coerce submission of bids with adequate valuation two measures are used. First was described earlier (minimum price constraint). In auction process the first price rule is used so it means that the spectrum is sold with the highest offered price. Alternatively the second-price auction rule may be used so it means that spectrum is sold to those players who sent the highest bids but with the second highest offered price. Because Vickrey proved that in the second-price auction the players submit their bids with valuation close to the real value of the auctioned object. Thus, it may be more convenient to use this auction rule in our case. Using Vickrey auction, payments which must be paid to the broker will be lower but the valuation of the spectrum will be closer to real market value for auction participants.

3.4.3- Performance evaluation

3.4.3.1 Integer programming – the problem dimension Formula (12) describes the number of combinations which must be checked to find the optimal solution of the problem formulated in (8). In this section, the problem dimension of optimization process is discussed using the following example. The total available bandwidth, which was considered and mentioned earlier, is the multiplicity of the 8MHz (DVB-T channel bandwidth). COGEU is mainly focused on TVWS channels from 40 to 60 but our approach may be easily enhanced into other spectrum bands. In selected areas it is expected that the total available TVWS can be up to 100 MHz. This spectrum is expected to be also fragmented. Here we consider the influence on the


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problem dimension of the following channel sizes which are connected with LTE channels (5MHz, 10MHz, 20MHz), UMTS (5MHz, 10MHz), public safety application (1MHz). In Figure 35 we present the problem dimension of the considered auction, i.e., the total number of possible combinations of integer variables which provide the maximization of the auction outcome and which must be checked to obtain this result.

.

Figure 35 Possible auction results to be considered by the spectrum-broker payoff-maximization procedure

3.4.3.2 Branch-and-cut examples In this section the branch and cut tree, is presented with all combinations of searched variables, which illustrates the branch and cut technique. Figure 36 shows complete tree for segments having the following sizes b1 = 5, b2 = 10, b3 = 20 MHz (LTE channel sizes) and total available bandwidth for trading B = 24 MHz. The broker is searching the best combination of m1, m2 and m3 which maximizes the broker utility and spectrum usage. We assumed that the whole available bandwidth satisfy the minimum power condition. As we proved earlier this technique reduces the number of combinations of searched integer variables (here from 30 to 10). Below we listed all possible integer values which can be solution of our problem (Figure 36 and Table 7). Which one is the solution it depends on the offers for particular spectrum segments. This technique is effective due to the second condition from (8).

Figure 36 Branch and cut tree – example


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In Table 8 we show only selected results of segments‟ sizes and their influence on the number of different mi combination which must be checked (combination with all searched variables equal to 0 is not interested for us but is starting point of this technique). It is also assumed that minimum power constraint is satisfied. We can observe that the use of very small segment sizes, as we predicted, increases computational complexity, and explains why we treat this auction complexity as NP-hard problem. Although in our application (spectrum selling) the number of possibilities to check is acceptable what is illustrated in second column of Table 8. We suppose that in real environment, the number of bids for the narrowest spectrum segment will not be high (we can add this limitation to reduce the optimization complexity). In the considered COGEU scenario this narrowest segment is 1 MHz, therefore in our simulations 1 MHz indicates the granularity of the auction but in general it may be any value. As an example of this limitation we can introduce special procedure for 1 MHz segment sizes. We can decide that the smallest available spectrum segment cannot be smaller than 5 MHz (problem simplification). We consider three options. First, we can solve an auction and use the rest of the spectrum for assignment to the players which demand only 1 MHz. The second option is prioritization (due to the policy requirements) and we can decide that 5 MHz will be reserved for users which want to use spectrum for Public Safety applications. Then we can solve an auction. Third, we can limit in auction phase number of bidders for 1 MHz to 5, solve an auction and the rest of the spectrum assign to other players which demand only 1 MHz spectrum block. Results with the third option are presented in Table 8.

m1 [5 MHz]

m2

[10 MHz] m3

[20 MHz] BA

[MHz]

0 0 1 20

0 2 0 20

2 1 0 20

1 1 0 15

0 1 0 10

4 0 0 20

3 0 0 15

2 0 0 10

1 0 0 5

0 0 0 0

Table 7 Possible Allocation Options – Example

Segment sizes in MHz, total available bandwidth for trading

Number of mi to check using

branch and cut

technique

Maximum number of mi to check (ni for each segment)

b1 b2 b3 b4 b5 B Lsimplified n1 n2 n3 n4 n5 L

5 10 - - - 24 9 4 2 - - - 15

5 10 - - - 48 30 9 4 - - - 50

5 10 - - - 72 64 14 7 - - - 120

5 10 - - - 96 110 19 9 - - - 200

5 10 20 - - 24 10 4 2 1 - - 30

5 10 20 - - 48 44 9 4 2 - - 150

5 10 20 - - 72 120 14 7 3 - - 480

5 10 20 - - 96 250 19 9 4 - - 1000

1 5 10 - - 24 110 24 4 2 - - 375

1 5 10 - - 48 595 48 9 4 - - 2450

1 5 10 - - 72 1732 72 14 7 - - 8760

1 5 10 - - 96 3795 96 19 9 - - 19400

5 10 20 22 - 24 11 4 2 1 1 - 60

5 10 20 22 - 48 59 9 4 2 2 - 450

5 10 20 22 - 72 192 14 7 3 3 - 1920

5 10 20 22 - 96 448 19 9 4 4 - 5000


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1 (5 bids)

5 10 20 22 24 59 5 4 2 1 1 360

1 (5 bids)

5 10 20 22 48 319 5 9 4 2 2 2700

1 (5 bids)

5 10 20 22 72 1020 5 14 7 3 3 11520

1 (5 bids)

5 10 20 22 96 2480 5 19 9 4 4 30000

Table 8 Auction Complexity

3.4.3.3 Simulation scenarios This section presents the simulation parameters and setup chosen for the initial tests of the auction-based TVWS allocation for the COGEU broker. The following parameters and the simulation setup cases have been chosen:

12 and 9 players competing for this spectrum are considered:. 4 (3 for 9 players) LTE TDD operators demanding 20 MHz TDD of the spectrum each, 4 (3) LTE operators demanding 10 MHz TDD each, 4 (3) LTE operators demanding 5 MHz each TDD;

LTE TDD scenario is used, due to there is no need to provide the duplex gap between uplink and downlink frequencies, LTE FDD scenario will be added in future COGEU deliverables;

No additional protection bands are considered due to the following assumptions: o the coexistence between the secondary LTE users is provided by channel sizes which

have lower measurement bandwidth than the channel size – see Table 14 in Deliverable D3.1,

o it is impossible to assign the whole spectrum to channel sizes having size 5, 10 or 20 MHz in TVWS fragments having size equal to the multiplicity of 8 MHz (in 8 MHz block the spectrum broker can assign maximum 5 MHz, in 16 MHz block – 15 MHz, in 24 MHz block – 20 MHz, in 32 MHz block – 30 MHz) the spectrum broker has a possibility to add some extra guard bands,

o in the case, when the additional guard (protection) bands are required, the spectrum broker will add guard bands to players demands and will use redefined demands in finding the auction solution;

Peak-hours have been only considered with the highest telecommunication traffic, i.e. when the spectrum demand usually exceeds the spectrum supply, and the spectrum allocation is done for a certain period of time. We do not consider how long this period is, although the assumption is that it covers the telecommunication traffic peak-hours, because auctions are run occasionally, when the spectrum demand is higher than offer. In the future work the time axis will be also considered, i.e. the players will be able to bid for a spectrum bandwidth, transmission power and for the anticipated time period, for which they may be granted the temporary exclusive rights;

Valuation of 1 MHz is similar for all users (normal distribution was used with the following parameters):

o G ~ (1.0, 0.2) for LTE (where the first number denotes the expected value, and the second – the standard deviation of the Gaussian distribution).

The probability of accessing the auction has uniform distribution, but with different parameters (random-variable ranges) for different users:

o LTE (20 MHz) – with 60% probability of the interest in accessing the auction, o LTE (10 MHz) – with 75% and o LTE (5 MHz) – with 90% probability respectively,

The number of players, the auction-access probability and the valuation of the spectrum unit corresponds to the incentives of all players interested in spectrum leasing;

Monte Carlo method was used with 10 000 runs of auction for each value of the available spectrum size.

Two scenarios of TVWS availability are presented o Scenario 1: Limited resources scenario (24 and 16 MHz TVWS blocks; demands > offer), o Scenario 2: Munich area scenario (24, 24, 24, 8 MHz TVWS blocks, demands > offer)

presented on Figure 9 we consider only the high power opportunities, suitable for LTE cellular operation.


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3.4.3.4 Simulation results In this section the simulated results are presented. The following metrics have been used to evaluate the spectrum allocation process with the auction (all metrics are relevant to one auction):

sum of players’ demands - is the sum of all demands sent to the spectrum broker by the all players interested in buying spectrum;

spectrum-auction efficiency – defined as the percentage of the leased spectrum with respect to the available spectrum (the fraction between 0 and 1);

spectrum-broker payoff – defined as the sum of accepted bids (in virtual monetary units);

the value of sold 1 MHz – defined by the broker profit (the accepted bids) divided by the auctioned spectrum (again in virtual monetary units);

user’s satisfaction rate - defined as number of winning auctions divided by number of auctions with this player.

Figure 37 shows the histogram of the sum of players‟ demands for Scenario 1 and Figure 39 for scenario 2. Figure 38 shows the histogram of the spectrum-auction efficiency (for Scenario 1 and Figure 40 for Scenario 2. Figure 41 shows the histogram of the spectrum-broker payoff for Scenario 1 and Figure 42 for Scenario 2. Figure 43shows the histogram of the price of one sold spectrum unit, i.e. of 1 MHz for Scenario 1 and Figure 44 for Scenario 2.In Table 9 we present the average of those metrics obtained from simulation for both scenarios. Table 10 presents user‟s satisfaction rate (for each user) defined as: number of winning auctions divided by number of auctions with this player for both scenarios.

Figure 37 The histogram of the sum of the players’ demands – Scenario 1 (12 players)

Figure 38 The histogram of spectrum-auction efficiency – Scenario 1 (12 players)

Figure 39 The histogram of the sum of the players’ demands – Scenario 2 (12 players)

Figure 40 The histogram of spectrum-auction efficiency – Scenario 2 (12 players)

On Figure 37and Figure 39 it is shown the sum of player‟s demands for both scenarios. These values are discrete due to the players‟ demands which are multiple of 5 MHz (auction products are 5, 10 and


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20 MHz). Some values are more probable than others due to the parameters of simulation model (the probabilities of the interest in accessing the auction for each type of player).

Figure 41 The histogram of an auction payoff – Scenario 1 (12 players)

Figure 42 The histogram of the value of sold 1 MHz – Scenario 1 (12 players)

Figure 43 The histogram of an auction payoff – Scenario 2 (12 players)

Figure 44 The histogram of the value of sold 1 MHz – Scenario 2 (12 players)

Simulation results confirm that combinational auction can guarantee high spectrum-auction efficiency due to the maximization of the payoff of the auctioneer. Because the users demand contiguous spectrum, the broker cannot get the maximum efficiency of selling the available spectrum for Scenario1 and Scenario 2. Maximum efficiency (equal to 1) is possible only when demands are equal or higher than offered spectrum and desired spectrum products are suited to the available spectrum blocks.

Available spectrum [MHz] Scenario 1 (9 players)

Scenario 1 (12 players)


Sum of all players demands

73.9206 96.4859 103.8871

Leased spectrum 34.8125 34.9840 64.1941

Spectral efficiency 0.8703 0.8746 0.8024

Auction payoff 37.9146 39.3001 68.1194

Value of sold 1 MHz 1.0885 1.1232 1.0603

Table 9 Simulation Results - summary

More interesting results are presented in Table 10. User satisfaction rate is strictly dependent on the available spectrum (more spectrum better satisfaction rate, lower competition) and on the valuation of the spectrum (in our simulations, the valuation of spectrum by different players is similar, because the players are similar, i.e. LTE mobile operators, but in general players‟ valuation may influence the auction payoff and thus, also the auction efficiency). For some fragmentation patterns of the available spectrum the players demanding fewer spectrums are in better position (especially when available fragment has size of 8 MHz). For others combinations players demanding more spectrum are in better position (especially when fragments have sizes 24 MHz).

Available spectrum [MHz]




User

LTE 1 (20 MHz) 0.4411 0.3121 0.6693

LTE 2 (20 MHz) 0.4271 0.3047 0.6850

LTE 3 (20 MHz) 0.4242 0.3227 0.6606

LTE 4 (20 MHz) - 0.3175 0.6744

LTE 5 (10 MHz) 0.5174 0.4107 0.6745

LTE 6 (10 MHz) 0.5166 0.3982 0.6715

LTE 7 (10 MHz) 0.5194 0.4000 0.6691

LTE 8 (10 MHz) - 0.3904 0.6773

LTE 9 (5 MHz) 0.5574 0.4507 0.6428

LTE 10 (5 MHz) 0.5647 0.4368 0.6403

LTE 11 (5 MHz) 0.5506 0.4358 0.6358

LTE 12 (5 MHz) - 0.4475 0.6386

Table 10 User Satisfaction Rate

Example allocation scheme is shown on Figure 45, in which we can observe that all available TVWS with maximum allowable power have been allocated to the LTE players, TVWS with low allowable power have not been allocated because the LTE players are not interested in using them, and we have not considered other players at this stage. In first TVWS block (consist of 3 TV channels, 24 MHz) there are resources for one LTE 20 MHz TDD player. In second TVWS block (1 TV channel, 8 MHz) there are resources for one LTE 5 MHz TDD player. In third TVWS block (3 TV channels, 24 MHz) there are resources for one LTE 10 MHz TDD player and for two LTE 5 MHz TDD players. In fourth TVWS block (3 TV channels, 24 MHz) there are resources for one LTE 20 MHz TDD player.


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Figure 45 Example of allocation of TVWS in Munich scenario

3.4.4- Conclusion

In this chapter we proposed a practical solution, for implementation of the combinatorial auction with non identical objects in TVWS context using the bandwidth and power requirements of the secondary users. Spectrum allocation problem has been defined as an optimization problem where maximum payoff of the spectrum broker is the optimization target. This target can be reached due to the branch-and-cut algorithm which was presented. The branch-and-cut technique is used to solve the considered auction with non identical objects for contiguous and non contiguous spectrum in order to limit the number of computations. The English sealed-bids first price auction is considered. Simulations were provided for LTE TDD players which are interested in buying spectrum for their end users. They may demand 5, 10 or 20 MHz. Simulations results confirmed that this solution is very efficient in case of spectrum broker‟s profit maximization and spectrum efficiency. Although, due to the fragmentation of the available bandwidth the maximum spectral efficiency is hard to reach (in optimum case should be equal to 1) so the rest of the spectrum for temporary access of other narrow band applications such as M2M and smart metering applications. Maximum efficiency is possible only when demands are equal or higher than offered spectrum and desired spectrum products are suited to the available spectrum blocks. The following metrics have been used to evaluate the spectrum allocation process with the auction (all metrics are relevant to one auction): sum of players‟ demands, spectrum-auction efficiency, spectrum-broker payoff, the value of sold 1 MHz and user‟s satisfaction rate. Simulation results showed that user satisfaction rate (defined as number of winning auctions divided by number of auctions with this player) is strictly dependent on the available spectrum (more spectrum better satisfaction rate, lower competition) and on the valuation of the spectrum. The presented spectrum allocation process based on auctions may be easily adopted for future players‟ demands based on any wireless standard and application requirements. Also Wi-Fi providers, DVB-H providers, and Public Safety users may be competitors and buy spectrum for their exclusive use. This algorithm is easy in implementation not only for TVWS but other available white spaces. There are still open issues, which will be addressed at the next stage of research in WP6, such as improving the branch-and-cut algorithm for LTE FDD access with a minimum duplex gap, adding time axis to players‟ demands and including more complicated scenarios of the players of various types, between which more difficult coexistence issues arise.


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4- RRM for LTE extension over TVWS

A key characteristic of LTE technology is its ability to operate in different bandwidths, ranging from 1.4 MHz to 20 MHz. This flexibility gives a certain degree of freedom to operators in order to deploy scalable solutions for the spectrum occupancy. However, performance is closely related with the available bandwidth, because less bandwidth means less subcarriers and thus less radio resources (RRs), or more bandwidth means more subcarriers and thus more RRs to carry the service. The flexibility is also related with the duplex modes, and for LTE, the frequency division duplex (FDD) and time division duplex (TDD) are both considered. In practical terms, the actual performance achievable with LTE depends on the allocated bandwidth for services, and not the choice of the spectrum band itself. This gives operators considerable flexibility in their commercial and technical strategies. Deployed at higher frequencies (few GHz), LTE is attractive for strategies focused on network capacity, whereas at lower frequencies (hundred MHz) it can provide ubiquitous cost-effective coverage. Using the TVWS (8 MHz of bandwidth per free channel) and the COGEU market approach it is feasible to reduce the number of BSs in a LTE network because the coverage area is bigger, providing the same or even higher throughput. This is important to both the network operators and the users: to the network operators, because it decreases the initial investments in the number of base stations (eNBs in LTE) and the backhaul infrastructure (CAPEX) and with less equipment less operational costs are required (OPEX); and to the users, since they can experience better services and reach the network almost anywhere (due to higher coverage). Despite the benefits that clearly come from extending the LTE use to TVWS, in terms of coverage and capacity, the minimum quality of service (QoS) per service must be taken into account when it comes the time to decide which services will use the TVWS or legacy carriers. Furthermore, the service level agreement (SLA), between operator and user, shall also be respected. Consequently, the allocation of the TVWS carriers shall ensure the exclusivity of the spectrum usage and low interference levels in order to guarantee the QoS. Regarding this, the secondary spectrum market proposed by COGEU is a suitable regime to guarantee QoS for LTE systems whenever they extend their services over TVWS. In this sense, new Radio Resource Management (RRM) procedures may be adopted. These procedures shall be seen as joint RRM that take advantage of the new portion of the spectrum, TVWS, which can be allocated to the secondary users. These RRM procedures are implemented at operator‟s network (and not in COGEU system) and aim to optimize the available RRs provided by legacy carriers and TVWS carriers (admitting that operator already acquired TVWS channels), and thus the coverage and capacity, without compromising QoS. In Figure 46 player 1 block represents the LTE operator and the RRM entity. This block has the responsibility to provide QoS, capacity and other parameters for each terminal. Moreover the network must have the ability to calculate how many TVWS channels need to request to the Broker trough the negotiation protocol, which is done based on measurements either provided by the terminal, eNB or both.

TVWS

area

BROKER

GEOLOCATION

SPECTRUM

DATABASE

TVWS

AP AP_N

PLAYER 1

LTE

RRM


CR + GPS

CR+GPS CR+GPSCR+GPS

CR+GPS

...


PLAYER 2 PLAYER N

TVWS allocation

mechanism

DVB-T

PMSE

Figure 46 The LTE RRM module integrated in the overall COGEU architecture.


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4.1- RRM problem formulation

The 3GPP LTE is a new radio access technology that will begin to be largely deployed during 2011. The LTE is a disruptive system in the sense that do not use the 3GPP radio interface evolution based on the WCDMA technology but adopted a new approach based on Orthogonal Frequency Division Multiplexing (OFDM), characterized by its flexibility, two duplex modes (TDD and FDD), several possible bandwidths and inter-cell interference coordination mechanisms. These are important features in order to guarantee the success of the system on an overcrowded radio spectrum. It is the first cellular system that depends mainly of already used bands for its deployment, which means that the LTE deployment will be done on the spectrum released by other systems. The TV spectrum and particularly the TVWS can be an opportunity not only to deploy the LTE but also to do it into an innovative way; in the framework of a new spectrum management paradigm proposed by COGEU. The TVWS will increase the pool of available radio resources (RRs) that are able to provide LTE services. However, in order to take full advantage of these new and valuable resources it is necessary manage them on the general context of a joint RRM, i.e., new TVWS carriers and legacy LTE carriers. Basically a joint RRM should at each moment guarantee the QoS (e.g. bit rate, delay, jitter), the network Key Performance Indicators (KPIs) and at the same time targeting the highest system capacity. In order to achieve this, the RRM entity should be able to allocate TVWS or legacy RRs; only with this approach it is possible to optimize the usage of RRs. Furthermore, the traditional planning phase must now consider the new carriers provided by TVWS. It is possible to enhance RRM in LTE systems with cognitive features and with the application of RRM algorithms to optimize the sub-carriers‟ assignment, power allocation and adaptive modulation. Furthermore, exploiting the capabilities of OFDMA access technology the network is capable of properly adapt to the environment conditions. On the other hand, cognitive features can be used to provide the system with knowledge that derives from past interactions with the environment. As a result, the system will be able to apply already known solutions in timely manner when identifying a problem that has been already addressed in the past [48]. Nevertheless, despite the optimization gains that is possible to achieve using such work frame, these RRM algorithms are generic and do not take into consideration a fundamental aspect of TVWS on the COGEU context: a shared spectrum (primary and secondary users) used on demand. For that a new work frame is needed. As analyzed in D2.1 and D3.1, the LTE extension over the TVWS is particularly suitable to provide additional capacity on overloaded radio networks. The network traffic is not constant and typically varies according a daytime pattern: more during morning and the afternoon and decreases on the end of the afternoon (see Figure 47). These traffic variations can even display monthly patterns and are strongly related with the location environment (dense urban, urban, suburban, rural).

Figure 47 Typical fluctuation of traffic over a day, [42]

http://www.google.pt/url?sa=t&source=web&cd=8&ved=0CGIQFjAH&url=http%3A%2F%2Fwww.its.monash.edu.au%2Fstaff%2Fmanagement%2Fstatements%2Fkpi%2Farchive%2F2003%2Foctober.html&ei=kG_iTNu7H8-Vswb9ov3wCw&usg=AFQjCNHQmyjCh2RnGDKulb7lR01mG9girQ&sig2=19Xv6xdpUnX_C4VpSV-vPw


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During traffic peaks the use of extra carriers over TVWS is more than welcome in order to provide extra capacity and keep the QoS above the minimum value. The mobile operator has a Service Level Agreement (SLA) that should be taken into consideration and which defines the minimum quality that operator should provide to its customers. But, beyond the minimum QoS, today, customers and regulatory bodies keep track of the quality provided by each network operator and broadcast this information to customers. Because users take this information into consideration to choose the best service provider, operators follow this issue with concern. Considering that TVWS carriers are allocated in a permanent way (as legacy carriers) it is a good exercise to determine if there is any gain in terms of capacity and coverage if the operator adopts to deploy its network based only on TVWS carriers. In this sense, simulations were done for frequencies of 2 GHz and 700 MHz (legacy and TVWS frequencies). In this exercise, it is considered the same environments (urban) and network configuration and parameterization, on both evaluations, and a bandwidth of 5 MHz.

Figure 48 : LTE over 2GHz band, Urban

Figure 49 : LTE over TVWS at 700MHz, Urban

The simulation results (Figure 48) show that TVWS can provide higher capacity and radio coverage than legacy carriers, thus higher spectral efficiency. Namely for the 2 GHz scenario the radio coverage probability is 94% and the average throughput 13.01 Mbps, while for 700 MHz scenario the coverage probability is increased up to 100% and the average throughput to 14.75 Mbps, more than 1.74 Mbps on average, which represents a significant increase in system capacity. However, as the TVWS carriers allocation by the COGEU broker works in a temporally basis, new functionalities needs to be added to the cellular system to support this dynamic behaviour. This means that according to the COGEU model, TVWS are shared in the time domain by different players competing for spectrum. This innovative approach on the spectrum management requires the definition of the following functionalities: network monitoring, TVWS carrier‟s assessment, network parameterization & configuration and RRM. Network monitoring – The network monitoring is a basic functionality provided for any mobile system that basically informs the operator about the network status: equipment malfunction, traffic volume, provided service quality, etc. On the particular context of COGEU the monitoring will trigger the request of a new carrier (TVWS), if some quality requirements are no met. Basically, the network defines a set of Key Performance Indicators (KPIs), normally the call blocked rate (CBR) or the minimum bit rate for best effort services, which are always checked and if some of those are not met (according to a defined threshold), a TVWS carrier maybe requested to solve the situation. TVWS Carrier’s assessment – Based on the KPI system calculates how many carriers will be required to provide the required QoS. Given that several carriers may be required in different cells at the same time, there is a need to estimate how many carriers will be required from the broker or, possibly, one of the carriers already in use can be reused in various cells. This evaluation requires a dynamic process of network planning. A request of carrier to the broker should include several other parameters beyond the number of carriers and bandwidth as time and location. Each carrier is “rent” for a specific location but if

COGEU D6.1–Dynamic Radio Resource Management Algorithms for an efficient use of TVWS

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the network already has these TVWS carriers but wants to re-use them should send a new request to the broker. Network parameterization & configuration – The provided TVWS and legacy carriers are allocated to the eNBs in a interference minimization manner. Each carrier can be delivered to one or several eNBs according to the network planning. If the number of TVWS carriers that are delivered is less than the requested, the network shall decide if it only uses the delivered TVWS carriers or requests for more.

RRM – an optimized Radio Resource Management should take into consideration the radio TVWS availability and particularly decide what is the best carrier (or amount of subcarriers) to provide the service on which case, what means guarantee the QoS and optimize the system capacity. Figure 50 shows the operational and maintenance (O&M) block, that processes the control and allocation of the TVWS and legacy carriers. Summarizing, "Radio Network Monitoring" receives the measurements from terminals and the eNBs and sends the information to the module "Number of carrier's Assessment" to process the number of TVWS that are required and make the request to the broker. In response, the broker delivers to the "Network Configuration & Parameterization" module the TVWS carriers. This module processes the allocation of the carriers and checks if there is a need for more carriers. Finally, the carriers are delivered to the RRM of each eNB.

TVWS

area

BROKER

GEOLOCATION

SPECTRUM

DATABASE

TVWS

O&M


CR+GPS

TVWS allocation

mechanism

DVB-T

PMSE

Radio Network

Monitoring

Number of

carrier‟s

Assessment Network Parameterization

&

Configuration

RRM

eNB

...

CN

E-UTRAN

COGEU

Request to

the Broker

Response of

the Broker

Figure 50 TVWS carrier’s attribution and RRM on LTE

4.2- Algorithm formulation

As proposed in previous section, the use of LTE in the TVWS implies four phases: network monitoring, TVWS carrier‟s assessment, carrier‟s allocation and RRM. To implement this solution, only the last phase, where the user allocation or user scheduling happens, should be dynamic and automatic, while the other phases can be carried out manually. Nevertheless, make all the process cognitive is the more


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correct approach to reach an optimum use of the available spectrum. The WWRF-WG6 [43] proposes a functional architecture for the management of spectrum and radio resources in adaptive/reconfigurable systems. Namely the Dynamic Network Planning and Management (DNMP) for assess the number of carriers needed and how this will allocate to the different cells/sectors in order to minimize the interference and maximize capacity and coverage. It is out of scope of this deliverable investigate any DNMP solution or radio planning automated mechanism but for simulation purpose a simple algorithm is used to identify the TVWS carriers (number and sector location) in order to avoid high interfering situations. In [44] an algorithm is proposed that allocates carriers at higher frequencies to users with higher Channel Quality Indicator (CQI) and the remaining ones at lower frequencies that on our case mean TVWS to other users. Some other approaches [45] have been studied as LTE carriers‟ aggregation and coordinated multi-point transmission that can leverage the TVWS value. The rest of the section describes the user allocation phase and particularly the algorithms responsible for Radio Resource Management (RRM). The algorithm should take into consideration the TVWS availability and decide which is the best carrier, legacy or TVWS, what means guarantee the QoS and optimize de system capacity. Two algorithms are proposed and evaluated. Algorithm1 – The algorithm 1 is very simple: the TVWS carriers are only allocated to the user when legacy carriers are fully occupied. There is no particular evaluation based on QoS. It is assumed that a previous evaluation concerning particularly the TVWS coverage was already done. This is also the simplest solution from the network implementation point of view: The user always requests the service using the legacy carrier but if it is fully occupied, the network informs the user that a TVWS carrier will be used. The Radio Resource Blocks (RRBs) that are required to deliver the service in each carrier plus the number of the already used ones must be less or equal to maximum number of RRBs provided per carrier. The Algorithm1 pseudo code is presented next:

IF (Clegacy_usedRRB + Clegacy_needRRB <= Clegacy_RRBmax) THEN Allocate Legacy carrier to user ELSE IF (Ctvws_usedRRB + Utvws_needRRB < Ctvws_RRBmax) THEN Allocate TVWS carrier to user ELSE User blocked END

Algorithm 1: A simple algorithm for the allocation of LTE or TVWS carriers In the second algorithm the user terminal should take measures on both carriers concerning the received signal quality and send this information to the network side that evaluates on which carrier less radio resource will be needed to provide the service and select it. If the RRBs are the same for both carriers, the user is allocated to legacy‟s carrier leaving RRBs in TVWS for a user that cannot have its service on legacy bands; it is assumed here that the legacy bands are at high frequency than TVWS. Algorithm2 – The algorithm presented in this section concerns the user allocation phase and intends to provide the user the contracted QoS to the network operator an optimized use of the system capacity. The use of radio resources needed to provide a service is dependent on radio signal quality and particularly the SNR in the reception; as SNR decreases the number of resource blocks increases what means less resources will be available for the remaining users. On the limit new user will be blocked. The allocation of resources to the user is not a linear process but increases on steps that are basically dependent on Modulation and Coding Schemes (MCS) supported per each system. What means in some cases that lower SNR demands more RRBs or that higher SNR requires less RRBs. For example, if the SNR already provides the higher MCS, minimum RRBs, any increase of SNR will be useless. The SNR, or CQI, are valuable information for user allocation over several frequency bands. The user is allocated to a legacy carrier if the number of RRBs needed to provide the requested service is the same or less than the TVWS carriers; if there is no capacity available on TVWS the user is also allocated to a legacy carrier. On the other hand if less RRBs are needed in TVWS than in legacy carriers, the TVWS will be selected. Algorithm2 pseudo code:


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IF Ulegacy_needRRB ≤ Utvws_needRRB THEN IF Clegacy_usedRRB + Ulegacy_needRRB ≤ Clegacy_RRBmax THEN Allocate Legacy carrier to user ELSE IF Ctvws_usedRRB + Utvws_needRRB ≤ Ctvws RRBmax THEN Allocate TVWS carrier to user ELSE Block user END ELSE IF Ctvws usedRRB + Utvws needRRB ≤ Ctvws RRBmax THEN Allocate TVWS carrier to user ELSE IF Clegacy usedRRB + Ulegacy needRRB ≤ Clegacy RRBmax THEN Allocate Legacy carrier to user ELSE Block user END

Algorithm 2: Optimized algorithm for the allocation of LTE or TVWS carriers These algorithms are implemented on the simulator described in the next section and its performance evaluated is shown in Section 4.4- At least on the first years the LTE network will be used to provide the same services as HSPA today, but at highest bit rates, basically best effort services. In fact this kind of service is the most suitable for TVWS due its relaxed QoS requirements. Anyway, given that the network is continuously monitoring the radio link quality any degradation observed on the TVWS beyond the minimum QoS required by the service will trigger a new radio resource allocation and the user is “moved” to other sub-carriers and in the limit to the “legacy” bands.

4.3- Simulation framework

Radio Network Planning (RNP) tools have always played a significant role in the daily work of network operators. When business requirements for service demands are specified based on business plans, the task of network planners is to fulfil the given criteria with minimal capital investment. Typically, the input parameters include requirements related to quality of service, capacity and coverage. Most 2G networks have only offered voice services. In 3G and beyond networks, there are various service types (voice and data) and a variety of different services, which may all have different requirements. Thus 3G planning tools play an even bigger role in the detailed network planning phase than in the case of 2G networks. It is necessary to find an optimum trade-off between quality, capacity and coverage criteria for all the services in an operator‟s service portfolio. One or more tools should assist the network planner in the whole planning process, covering the dimensioning and detailed planning and, finally, pre-launch network optimisation. Typically, a single tool alone cannot support all the phases of the planning process. Instead, one tool is dedicated to dimensioning, another to network planning, a third to optimisation. In modern applications, all the tools required are typically integrated seamlessly into one package, which consists of a suite of tools. If this integration is performed properly, the network planner is unaware of actually using several tools when performing the planning and optimisation activities [46]. There are several LTE RNP professional tools available on the market that provide to mobile operators all the features needed for the radio network planning and optimization and that supports thousands of sites, very detailed parameterization of each network element, maps, pre-defined propagation models, etc, Nevertheless, and despite its extensive configuration possibilities, they are designed focused on standardized systems and “closed” to modifications, thus not suitable for research activities. Therefore, for COGEU, it was developed a LTE simulator in MatLab using the tool GUIDE to support the development of graphical user interface (GUI) and an object-oriented scripting language provided by MatLab itself. Simulations were possible for legacy frequencies and TVWS frequencies. The LTE simulator simulates the LTE radio behaviour mainly in terms of radio coverage and system capacity and its associated statistics (e.g. call block rates (CBR), coverage probability) based on the


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definition of very complete scenario. The starting point is then the definition of the simulation scenario: base stations, mobile stations, network and environmental characteristics. Some of the inputs to the simulator and scenario definition are: Base station – location, number of sectors, antenna radiation pattern, antenna height, antenna gain, transmitted power, UL and DL scheduling, carriers bandwidth and frequencies. Mobile station – antenna radiation pattern, antenna height, antenna gain, transmitted power, UL and DL bit rates. Environment – propagation model urban, sub-urban rural. Figure 51 presents the architecture of LTE simulator. The blue blocks are input parameters of the radio Network Planning (green block), where the base station as its main features, the TX power, the location and type of antenna; the mobile station as bitrates, TX power and type of antenna; and link loss calculation based on the propagation model is calculated, which will help to define the cell range and coverage threshold. There are some important parameters which greatly influence the link budget, for example, the sensitivity and antenna gain of the mobile equipment and the base station, the cable loss, the fade margin etc. Based on the digital map and the link budget, computer simulations will evaluate the different possibilities to build up the radio network part by using some optimization algorithms. The central block, the Radio Network Planning (RNP), processes all the parameters of the base station, the terminal and link loss calculation, and the results are displayed in the GUI (grey block). The goal is to achieve as much coverage as possible with the optimal capacity, while reducing the costs, also as much as possible. The coverage and the capacity planning are of essential importance in the whole radio network planning. The coverage planning determines the service range, and the capacity planning determines the number of to-be-used base stations and their respective capacities.

RNP

Schedulling (DL, UL),

Link Budget,

Admission control

Antena Pattern

E,H plane

Gain

Base Station

E.g Location, HW

Characteristics, Tx power

Mobile Station

E.g Power, Bitrate (UL, DL),

Tx power, antenna gain

Network parameters

Maps

Propagation models

Link Loss calculation

GUI

Output Collector

Figure 51 LTE over TVWS simulator architecture

The figure below illustrates the LTE simulator GUI interface.


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Figure 52 GUI interface of the LTE over TVWS simulator

The simulation is carried out in all considered area, on the example above, 19 sites with 3 sectors each. But in order to avoid incorrect measurements that came from border sites (e.g. high coverage areas) the collected measurements are only concerned with the sites on the center of the simulation area (blue) (Figure 53).

Figure 53 Simulation area

The simulator implements the algorithms 1 and 2 described on the previous section, Figure 54 shows some aspects of the GUI.

Figure 54 GUI interface of the LTE over TVWS simulator


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4.4- Initial performance evaluation

In order to obtain evaluation results, algorithm 1 and algorithm 2 were simulated according to a heavily overloaded urban scenario, consisting of 19 sites with 3 sectors per site and a random distribution of users. More details on simulation parameters are presented in Table 11.

Table 11 Network parameters

BS TxPower 38 dBm

BS antHeight 35 m

BS antType 120deg

BS Cable Losses 2 dB

Duplex mode FDD

CarrFreqDL (legacy) 2110-2115-2120 MHz

CarrFreqUL (legacy) 1920-1925-1930 MHz

BW 5 MHz

TVWS carriers 630, 635, 640.MHz.

PropModel COST 231, Okumura-Hata

Cell Radius 0.75 Km

Sectors/site 3

UE txMaxPower 23 dBm

UE antHeight 1.5 m

RbDL 1Mbps

RbUL 128 Kbps

UE antType Omnidirectional

The first simulation was carried out only on the 2GHz band in order to identify the overloaded sectors (network monitoring), with CBR higher than 2%, then it is evaluated how many carriers are needed (carrier assessment). This process is out of the scope of this document and was utilized a very simple rule to minimize the interference and evaluate the number of carriers needed: If a TVWS carrier is allocated to a sector, the same carrier cannot be used on the neighbour sectors. This is a very conservative solution but guarantees a low level of co-channel interference. After these results and assuming that the requested carriers to the brokers are provided (request to broker phase) the TVWS allocation plane of the carrier assessment phase can be implemented (network parameterization & configuration). Otherwise a new radio network plan should be initiated in order to guarantee the best service possible with the available TVWS carriers. After allocate the TVWS carriers to sectors with a CBR higher than 2%, according the plan, it is evaluated the algorithm 1 and 2 performance measuring, the Radio Resource Blocks (RRBs) needed for provide the service to all the users. In OFDMA, users are allocated a specific number of subcarriers for a predetermined amount of time. These are referred to as radio resource blocks in the LTE specifications. RRBs thus have both a time and frequency dimension. Allocation of RRBs is handled by a scheduling function at the base stations (eNBs). A RRB is defined as consisting of 12 consecutive subcarriers for one slot (0.5 ms) in duration. A RRB is the smallest element of resource allocation assigned by the eNB scheduler. Figure 55 shows the amount of RRBs used on legacy (2 GHz) and TVWS bands per sector with respect of algorithm 1 and 2. The average number of RRBs per user with algorithm 1 is 4558 and with algorithm 2 is 4356, so 202 RRBs less. In conclusion, with the algorithm 2 it is possible to optimize the RRBs and achieve higher capacity using the same RRBs.


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Figure 55 RRB needed per sector using algorithm1/algorithm2

Despite the capacity gain of the algorithm, there are some implementation issues that should be analysed in detail. With algorithm 1 the carriers, TVWS or legacy, are allocated to the user only considering the availability of capacity on the carrier and does not have to periodically evaluate the number of needed RRBs (based on CQI). However, because each terminal reports an estimate of the instantaneous channel quality (CQI) to the base station, the downlink scheduler can assign resources to users, taking the channel quality into account. In principle, a scheduled terminal can be assigned an arbitrary combination of 180 kHz wide resource blocks in each 1 ms scheduling interval [47]. The periodic report of CQIs implies that the user should take measurements in TVWS and legacy bands what can be a drawback for the use of algorithm 2 on real implementation. This technology limitation needs to be carefully analysed.

Figure 56 Downlink scheduling resources per user [47]

3900

4000

4100

4200

4300

4400

4500

4600

4700

4800

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Nu

mb

er

of

RR

B

BS Sector number

Urban scenario

Algorithm 1 Algorithm 2

Average - Algorithm 1 Average - Algorithm 2


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On this section it was proposed and evaluated two algorithms that implement a joint radio resource management and the preliminary evaluation results show a clearly advantage of following this approach, particularly the algorithm 2.

4.5- Conclusion

Radio Resource Management (RRM) procedures, which are external to the operation of COGEU broker, have been investigated, i.e. after the allocation process of temporarily exclusive rights of TVWS to secondary users. In particular, RRM associated with the LTE extension over TVWS is investigated. Using a joint optimization of radio resources of TVWS carriers (acquired from the COGEU broker) and legacy carriers (bought from the regulator), it is feasible to reduce the number of BSs, while providing the same or even higher throughput. This is important to both the network operator and the users: to the network operator, because it decreases the investment and operational costs; and to the users, since they can experience better services in a higher area. As the TVWS carriers allocation by the COGEU broker are temporal and local basis, new functionalities need to be added to the cellular system in order to support this dynamic behaviour: network monitoring, TVWS carrier‟s assessment, network parameterization & configuration and RRM. The proposed modifications take into consideration the LTE standard proposed by 3GPP and try to incorporate specific functionalities required to use the TVWS with the minor possible changes but without compromising the enhancement of performance that TVWS can bring to the cellular system. It is presented a system that takes advantage of network monitoring capabilities to start the network planning and management processes. Nowadays this process is basically manual but it is expected in future systems to become automated and dynamic (DNMP). In fact, for the simulations all the process is automated; what means that identification of overloaded cells, evaluation of the spectrum bands to request to COGEU broker and the TVWS carriers‟ allocation to cell/sectors is automated. It is clear the benefits of a DNMP approach for the network operator in terms of OPEX. Preliminary simulation results showed that TVWS can provide extra capacity for overloaded LTE cells. This preliminary evaluation was first carried out on a limited but representative scenario: urban environment, considering a Guaranteed Bit Rate (GBR) service class (DL: 1Mbps, UL: 128 kbps) where the TVWS is used by LTE with very simple technical solutions what means without significant modifications on LTE architecture and protocols. Basically, in the algorithm 1 the TVWS radio resources are only attributed to a connection if the there is no capacity on the legacy bands. On the other solution, algorithm 2, the radio link quality on each band is periodically monitored and evaluated the amount of radio resource that are needed to provide the requested service; the service is provided by the band that consumes less radio resources. Despite the highest performance of algorithm 2, its implementation can be more complex because monitoring on both bands is required simultaneously in order to evaluate the radio link quality. The radio link quality (CQI) is used to compute the amount of radio resources needed to provide the service. In this sense, to implement a solution based on algorithm 2 a new radio interface devoted exclusively to TVWS or a hybrid solution can be considered. Hybrid solution mean here an interface with different physical levels sharing the same MAC, also called a Multi-Radio Access (MRA). The MRA is usually considered on the context of different Radio Access Technologies (RATs), for example HSDPA and WiFi but here only addresses LTE. The MRA solution can provide all the functionalities to implement algorithm 2 and give the additional benefit of supporting simultaneous allocations of radio resources in both bands. To obtain a more complete assessment of algorithm 2 (and even of the MRA approach) new simulations will be carried out and functionalities will be added to simulator: new scenarios (suburban or rural) and new service classes in line with LTE service classes. These functionalities are required in order to provide more realistic traffic evaluation. Furthermore, a complementary view based on the traffic analysis will be carried out on the scenarios previously addressed.


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5- Conclusions and future work

This Deliverable, D6.1, reports the results of Task 6.1 which addresses the development of dynamic radio resource management algorithms for an efficient use of TVWS. In this context, the overall objective of D6.1 is to design, simulate and evaluate a TVWS allocation mechanism, able to be adopted in spectrum broker entity of COGEU demonstrator. Towards this main objective, an overall TVWS allocation process is proposed including matching optimization and spectrum auction. The evaluation results validated the performance of the proposed algorithms for an efficient allocation of TVWS in COGEU secondary systems. Following the main conclusions of this work are listed. Main conclusions

A methodology is proposed in order to compute TVWS availability and populate the geo-location database. Preliminary results of the available TVWS channels investigation in Munich area are presented and used as a case study scenario in the performance evaluation of the TVWS allocation techniques. The TVWS are the radio resources to be managed by the COGEU broker.

The COGEU broker is in charge of assigning the access to TVWS spectrum under real time secondary spectrum market regime. It incorporates a process of optimally allocating spectrum to secondary systems taking into account matching optimization methods, spectrum pricing and spectrum auction methods.

A spectrum broker allocation process is designed and presented for an efficient radio resources allocation in COGEU secondary spectrum market. The key blocks of the allocation process are: Internal spectrum broker databases, Benchmark price estimation process, Matching algorithm between demand and TVWS offer and the Auction module.

Two internal spectrum broker databases are identified: The TVWS occupancy repository and the Spectrum policies repository. The TVWS occupancy repository is the unit that contains all the information where TVWS devices may transmit and also contains a database on active TVWS devices and their operational parameters. The spectrum policy database manages the spectrum trading policies of the regulator which include, priorities, restrictions, etc.

A part of the overall proposed allocation process is the estimation of benchmark price of the available spectrum based on an Administrative Incentive Pricing (AIP) mechanism. As a result of the benchmark price estimation is the definition of spectrum-unit price in the overall process. In this context, an AIP based algorithm is proposed and developed towards TVWS price definition and methods of estimating opportunity cost are analyzed. The key point in this research work is the challenge to set the price of spectrum in order to reflect the social value of the resources as well as the underlying signalling mechanism in order to enable such a framework to operate seamlessly.

A matching algorithm based on Backtracking process is designed, simulated and evaluated in order to match spectrum supply from a TVWS pool and spectrum demand from secondary systems. This algorithm is applied in cases, where spectrum demand is lower than spectrum supply in the overall allocation process adopted by the COGEU broker. In this case, a fix price per MHz, defined by the benchmark price estimation process is used.

In COGEU broker, the Backtracking algorithm with pruning is used to represent all possible arrangements in the TVWS pool that matches the secondary systems demand. The available solutions are ranked based on the size of contiguous remaining white-spaces and favor those that provide for tighter allocation of services in order to allow for additional services in future deployments.

A COGEU scenario that adopts preliminary studies of TVWS availability in Munich is considered in order to illustrate and test the matching algorithm. The test scenario includes time dimension, allowing secondary systems to request TVWS access and enter into the allocation process every “Time period”. The performance of the matching algorithm is evaluated by taking into account several metrics such fragmentation, TVWS utilization and complexity.

As secondary systems enter and exit a market place over time, the available TVWS channels become increasingly divided into a collection of discrete fragments. This “spectrum fragmentation” means that a significant portion of spectrum, while free, is effectively unusable because its fragments do not provide the minimum contiguous spectrum range required by new flows of secondary users. This problem is taken into account when investigating matching techniques.


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The simulation results obtained confirmed the efficiency of the proposed matching algorithm to optimally allocate TVWS to secondary systems taking into account all constraints defined in the specifications of the use case scenario (allocation time, frequency bandwidth and transmit power). According to simulation results, it is clear that pruning technique provides smaller number of solutions explored, i.e., low complexity, at the cost of some degradation in terms of fragmentation.

The TVWS allocation based on auctions is used for the case of the spectrum demand exceeding the spectrum offer, when not all the requests can be satisfied. This approach selects a subset of the secondary users to be allocated the spectrum, and emphasizes the economic aspect of dynamic spectrum access.

A practical solution, for implementation of the combinatorial auction with non identical objects in TVWS context using the bandwidth and power requirements of the secondary users is proposed. Spectrum allocation problem is defined as an optimization problem where maximum payoff of the spectrum broker is the optimization target. This target can be reached due to the branch-and-cut algorithm presented. The branch-and-cut technique is used to solve the considered auction with non identical objects for contiguous and non contiguous spectrum in order to limit the number of computations. The English sealed-bids first price auction is considered.

The following metrics are used to evaluate the spectrum allocation process with the auction: sum of players‟ demands, spectrum-auction efficiency, spectrum-broker payoff, the value of sold 1 MHz and user‟s satisfaction rate.

Simulations are provided in COGEU Munich scenario for LTE network operators (players) which are interested in buying spectrum for their end users. They may demand 5, 10 or 20 MHz. Simulations results confirmed that this solution is very efficient in case of spectrum broker‟s profit maximization and spectrum efficiency. Although, due to the fragmentation of the available bandwidth the maximum spectral efficiency is hard to reach so the rest of the spectrum for temporary access of other narrow band applications such as M2M and smart metering applications.

Maximum spectrum-auction efficiency is possible only when demands are equal or higher than offered spectrum and desired spectrum products are suited to the available spectrum blocks.

Simulation results showed that user satisfaction rate (defined as number of winning auctions divided by number of auctions with this player) is strictly dependent on the available spectrum (more spectrum, lower competition) and on the valuation of the spectrum.

The presented spectrum allocation process based on auctions may be easily adopted for future players‟ demands based on any wireless standard and application requirements.

Radio Resource Management (RRM) procedures external from the operation of COGEU broker are investigated, i.e. after the allocation process of temporarily exclusive rights of TVWS to secondary users. In particular, RRM associated with the LTE extension over TVWS is investigated. The RRM procedure aim at the provision of guaranteed QoS to mobile subscribers of an LTE operator acting as a broker‟s customer and a secondary spectrum user.

Using a joint optimization of radio resources from LTE-TVWS carriers (acquired from the COGEU broker) and LTE-legacy carriers, it is feasible to reduce the number of BS‟s, providing the same or even higher throughput. This is important to both the network operator and the users: to the network operator, because it decreases the CAPEX (Capital Expenditures) and OPEX (Operational Expenditures); and to the users, since they can experience better services.

As the TVWS carriers allocation by the COGEU broker are temporary and location basis, new functionalities needs to be added to the cellular system to support this dynamic behaviour such as: network monitoring, TVWS carrier‟s assessment, request to the broker, carriers allocation and user allocation.

The proposed modifications try to incorporate specific functionalities required to use TVWS carriers with minor changes of the standard LTE 3GPP architecture and protocols.

Two algorithms are proposed where the radio link quality on each band (TVWS and legacy) is periodically monitored and the amount of RRB (Radio Resource Blocks) needed to provide the requested service evaluated. The algorithms decide on which carrier the mobile user \ service will be allocated. Preliminary simulation results show that TVWS can provide extra capacity for overloaded LTE cells. This preliminary evaluation was first carried out on a limited but representative scenario: urban, considering a GBR (Guaranteed Bit Rate) service class (DL: 1Mbps UL 128kps) where the TVWS is used by LTE without significant modifications on LTE architecture and protocols.


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Future work The work reported in this deliverable will be extended in T2.3. (“Economic models for spectrum market”), efforts will be focused on investigating the spectrum price discovery mechanisms and models of the spectrum valuation by the secondary users. Administrative Incentive Pricing and auction mechanism will be extended as tools for price discovery and important elements of the relevant economic models to be defined in T2.3. Backtracking matching algorithm and auction mechanism will be also extended. In particular, the auction mechanism will incorporate the time axis in the spectrum demands for the final spectrum allocation. In addition, learning techniques as a part of the auction bids submission process will be investigated, i.e. when guessing the value of the spectrum unit, the player must learn from the past experience, what will be the valuation of the other players. Spectrum trading policies to enable efficient TVWS sharing, including prioritization and anti-monopoly, will be investigated in T2.2 and integrated in the spectrum broker allocation process in the near future. Other possible use-cases and scenarios, i.e. LTE FDD with the minimum duplex gap will be considered for the auctions. Frequency gap between the spectrum used for the uplink and downlink transmission is an additional requirement/constraint which will be added to the backtracking algorithm and to the auction-based approach. (Possible allocation of LTE FDD channels with the duplex gap in Germany in Digital Dividend is shown on Figure 57). Research will be also conducted to merge the backtracking algorithm with auction algorithm for improving spectrum allocation procedure. Some learning techniques may also be useful in the process of allocation the spectrum to the players to predict possible spectrum interest in a certain moment of time and to adjust the minimum price or spectrum trading price. To obtain a more complete assessment of the RRM algorithms proposed for the LTE extension over TVWS new simulations will be carried out and new functionalities will be added to the LTE simulator. All the outputs obtained from this document and from T2.3 will be used in T6.5 which will bring simulation tools for system level evaluation and in T7.2.3 which will bring the spectrum broker implementation.

Figure 57 Frequency arrangement in Germany in Digital Dividend – after auction for LTE spectrum [49]


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[29] OFCOM Modification to spectrum pricing: A consultation on proposals for setting wireless

telegraphy act licence fees, 6, September, 2006, http://www.jfmg.co.uk/pages/Docs/pricing06.pdf

[30] Study into the use of Spectrum Pricing, Study for the Radiocommunications Agency, NERA and

Smith System Engineering Limited, April 1996,

http://www.ofcom.org.uk/research/radiocomms/reports/independent_review/spectrum_pricing.pdf

[31] Review of Radio Spectrum Management, Study for Department of Trade and Industry and Her

Majesty‟s Treasury, Martin Cave, March 2002 http://www.ofcom.org.uk/static/archive/ra/spectrum-

review/2002review/1_whole_job.pdf

[32] 2004 report on an economic study to review spectrum pricing by Indepen, Aegis Systems and

Warwick Business School.


[33] OFCOM, SRSP: The revised Framework for Spectrum Pricing, Proposal following a review of our

policy and practice of setting fees, OFCOM, March, 2010

[34] Patrick Xavier, Dimitri Ypsilanti, Policy issues in spectrum trading, Info, Vol. 8, no. 2, 2006, pp: 34-

61

[35] Pietro Crocioni, Is allowing trading enough? Making secondary markets in spectrum work,

Telecommunications Policy, Volume 33, Issue 8, September 2009, Pages 451-468.

http://www.jfmg.co.uk/pages/Docs/pricing06.pdf


http://www.ofcom.org.uk/static/archive/ra/spectrum-review/2002review/1_whole_job.pdf

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[36] Plum Consulting and Aegis Systems, Administrative Incentive Pricing of Radiofrequency Spectrum, report for ACMA, October 2008

[37] Australian Communications and Media Authority (ACMA), Opportunity Cost Pricing of Spectrum,

Public consultation on administrative pricing for spectrum based on opportunity cost, April 2009

[38] S. de Vries, R. V. Vohra. Combinatorial Auctions: A Survey, 2000

[39] V. Krishna, Auction Theory. Academic Press, 2002.

[40] N.Nisan, Bidding and Allocation in Combinatorial Auctions, In Proceedings of the 2nd

ACM Conference on Electronic Commerce, 2000

[41] Applied Mathematical Programming by Bradley, Hax, and Magnanti (Addison-Wesley, 1977)

[42] Mobile Internet Traffic: Analysing Global Usage Trends, Informa UK Ltd, 2010

[43] “Cognitive Radio and Management of Spectrum and Radio Resources in Reconfigurable

Networks”, WWRF-WG6, Lars Berlemann, George Dimitrakopoulos, Klaus Moessner, Jim Hoffmeyer.

[44] “Spectrum Aggregation with Multi-Band User Allocation over Two Frequency Bands”, F. Meucci ,O.

Cabral, F. J. Velez.

[45] “LTE-Advanced – Evolving LTE towards IMT-Advanced” , Stefan Parkvall, Erik Dahlman, Anders Furuskär, Ylva Jading, Magnus Olsson, Stefan Wänstedt, Kambiz Zangi.

[46] “Radio Network Planning and Optimisation for UMTS”, Jaana Laiho, Achim Wacker and Toma´ sˇ

Novosad

[47] “3G Evolution HSPA and LTE for Mobile Broadband”, Erik Dahlman, Stefan Parkvall, Johan Sköld and Per Beming

[48] Cognitive Radio Resource Management for Improving the Efficiency of LTE Network Segments in the Wireless B3G World, Aggelos Saatsakis, Kostas Tsagkaris, Dirk von-Hugo, Matthias Siebert, Manfred Rosenberger,Panagiotis Demestichas

[49] R. Brugger (IRT), O. Kluth (WDR), 800 MHz Auctions and Implementation of the DD in Germany,

EBU ECS Workshop „Implementation of the Digital Dividend in European Countries” EBU, Geneva, 7 October 2010


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List of Tables

Table 1 Selected COGEU use-case scenarios ........................................................................................ 10

Table 2: Explanation of the factors and parameters influencing TVWS usage in COGEU use-cases .... 13

Table 3: Parameters or factors which influence COGEU use cases in TV white spaces usage (figures for illustrative propose) ...................................................................................................................... 14

Table 4 Factors to be considered in setting AIP for TVWS allocation in COGEU use-cases .................. 30

Table 5: The value of different white space frequency bands for different (COGEU) use-cases ............ 33

Table 6 Secondary systems requirements used in the validation scenario ............................................. 46

Table 7 Possible Allocation Options – Example ....................................................................................... 61

Table 8 Auction Complexity ...................................................................................................................... 62

Table 9 Simulation Results - summary ..................................................................................................... 65

Table 10 User Satisfaction Rate ............................................................................................................... 65

Table 11 Network parameters .................................................................................................................. 75


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List of Figures

Figure 1 Example of available TVWS resources in a specific location (COGEU spectrum pool). ............. 8

Figure 2 Association diagram of D6.1 with other COGEU activities ........................................................ 10

Figure 3 COGEU network architecture concept ....................................................................................... 11

Figure 4 Overview of COGEU network configuration. The “pink” blocks are where D6.1 contributions are placed. ............................................................................................................................................... 12

Figure 5 TVWS supply valid in a specific geographical region (Spectrum pool). ..................................... 15

Figure 6 Allocation profile of TVWS blocks. ............................................................................................. 15

Figure 7 Flow chart with the process used to compute TVWS availability ............................................... 17

Figure 8 Gross TVWS in Germany for channel 59. .................................................................................. 18

Figure 9 TVWS available in Munich area. Symbolic notation for y-axis: .................................................. 19

Figure 10 Protection Layers ..................................................................................................................... 21

Figure 11 Spectrum Broker allocation process ........................................................................................ 22

Figure 12 The algorithm for allocating TVWS based on AIP [Adopted from [33]] .................................... 29

Figure 13 Estimation of TVWS reference rate based on opportunity cost ............................................... 34

Figure 14 Typical master-slaves mobile service system using COGEU Broker ...................................... 35

Figure 15 Calculation of the AIP by adjustments of the reference rate with various factors .................... 36

Figure 16 Depth-first search code of Backtracking .................................................................................. 38

Figure 17 Basic Backtracking code .......................................................................................................... 39

Figure 18 Construction of candidates in backtracking ............................................................................. 40

Figure 19 Function of process solution .................................................................................................... 40

Figure 20 Function of is a solution ........................................................................................................... 40

Figure 21 Function of generate permutations .......................................................................................... 40

Figure 22 Logic Diagram of Backtracking Process .................................................................................. 43

Figure 23 Backtracking integrated in the spectrum broker allocation process ........................................ 45

Figure 24 COGEU scenario with cellular and Public Safety networks operating in TVWS area. ............ 46

Figure 25 TVWS available in Munich area (from COGEU D4.1). ............................................................ 47

Figure 26 Initial TVWS allocation example (LTE 20 MHz) ...................................................................... 47

Figure 27 Second phase of TVWS allocation example (LTE 20 MHz + Public Safety 1 MHz)................ 48

Figure 28 Third phase of TVWS allocation example (Public Safety 1 MHz + LTE 10 MHz).................... 48

Figure 29 Fourth phase of TVWS allocation example (Public Safety 1 MHz + LTE 10 MHz + LTE 5 MHz)........................................................................................................................................................... 49

Figure 30 Fragmentation score computed with the power fragmentation method for COGEU scenario. 50

Figure 31 Solutions explored during simulations for different time periods, COGEU scenario. .............. 50

Figure 32 TVWS Utilization for the COGEU validation scenario. ............................................................. 51

Figure 33 Algorithm complexity ................................................................................................................ 52

Figure 34 Sealed-bid auction algorithm for TVWS allocation ................................................................... 56

Figure 35 Possible auction results to be considered by the spectrum-broker payoff-maximization procedure .......................................................................................................................................... 60

Figure 36 Branch and cut tree – example ................................................................................................ 60

Figure 37 The histogram of the sum of the players‟ demands – Scenario 1 (12 players) ....................... 63

Figure 38 The histogram of spectrum-auction efficiency – Scenario 1 (12 players) ................................ 63

Figure 39 The histogram of the sum of the players‟ demands – Scenario 2 (12 players) ....................... 63

Figure 40 The histogram of spectrum-auction efficiency – Scenario 2 (12 players) ................................ 63

Figure 41 The histogram of an auction payoff – Scenario 1 (12 players) ................................................ 64

Figure 42 The histogram of the value of sold 1 MHz – Scenario 1 (12 players) ...................................... 64

Figure 43 The histogram of an auction payoff – Scenario 2 (12 players) ................................................ 64

Figure 44 The histogram of the value of sold 1 MHz – Scenario 2 (12 players) ...................................... 64

Figure 45 Example of allocation of TVWS in Munich scenario ................................................................ 66


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Figure 46 The LTE RRM module integrated in the overall COGEU architecture. ................................... 67

Figure 47 Typical fluctuation of traffic over a day, [42] ............................................................................. 68

Figure 48 : LTE over 2GHz band, Urban .................................................................................................. 69

Figure 49 : LTE over TVWS at 700MHz, Urban ....................................................................................... 69

Figure 50 TVWS carrier‟s attribution and RRM on LTE ........................................................................... 70

Figure 51 LTE over TVWS simulator architecture .................................................................................... 73

Figure 52 GUI interface of the LTE over TVWS simulator ....................................................................... 74

Figure 53 Simulation area ........................................................................................................................ 74

Figure 54 GUI interface of the LTE over TVWS simulator ....................................................................... 74

Figure 55 RRB needed per sector using algorithm1/algorithm2 ........................................................ 76

Figure 64 Downlink scheduling resources per user [46] .......................................................................... 76

Figure 57 Frequency arrangement in Germany in Digital Dividend – after auction for LTE spectrum [49]........................................................................................................................................................... 80

Figure 58 Backtrack class ........................................................................................................................ 89

Figure 59 Function IsSolution ................................................................................................................... 89

Figure 60 Function ProcessSolution ........................................................................................................ 89

Figure 61 Function PickCandidates ......................................................................................................... 90

Figure 62 Function fits .............................................................................................................................. 90

Figure 63 Function inSpectrum ................................................................................................................ 90

Figure 64 Functions isStrictSolution and isRelaxSolution ........................................................................ 90


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List of Abbreviations

ATSC Advanced Television Systems Committee

3GPP 3rd Generation Partnership Project

4G Fourth Generation

CEPT Conference of European Postal & Telecommunications

CR Cognitive Radio

DSM Dynamic System Management

DVB-H Digital Video Broadcasting – Handheld

DVB-T Digital Video Broadcasting - Terrestrial

DTV Digital Television

DwPTS Downlink Pilot Time Slot

ETSI European Telecommunication Standards Institute

EU European Union

FCC Federal Communications Commission

FPGA Field-Programmable Gate Arrays

GSM Groupe Spécial Mobile (also, Global System for Mobile communication)

IEEE The Institute of Electrical and Electronics Engineers

ICT Information and Communications Technologies

IMT International Mobile Telecommunications

IPR Intellectual Property Rights

ISM Industrial Scientific and Medical (band)

ITU International Telecommunication Union

LAN Local Area Network

LTE Long Term Evolution

MAC Medium Access Control

MIMO Multiple-Input Multiple-Output

OFCOM Office of Communications

OFDM Orthogonal Frequency Division Multiplexing

PAPR Peak to Average Power Ratio

PMSE Programme Making and Special Events

PWMS Professional Wireless Microphone Systems

QoS Quality of Service

R&D Research and Development

RF Radio Frequency

RRC Regional Radiocommunication Conference

RRM Radio Resource Management

RSPG Radio Spectrum Policy Group

SAP Services Ancillary to Programme making

SDR Software Defined Radio

TV Television

TVWS TV White Spaces

TVWS-OR TVWS Occupancy Repository

UHF Ultra High Frequency

UMTS Universal Mobile Telecommunications System

US Unites States of America

USRP Universal Software Radio Peripheral


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VHF Very High Frequency

WCDMA Wideband Code Division Multiple Access

WiMAX Worldwide Interoperability for Microwave Access

WiFi IEEE 802.11

WLAN Wireless Local Area Network

WP Work Package

WPAN Wireless Personal Area Network

WWRF Wireless World Research Forum


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Annex I. Backtracking Algorithm Code

#ifndef BACKTRACKING_H_ #define BACKTRACKING_H_ #include <vector> template <class Solution, class Kernel> void backtrack(Solution& s, Kernel& kernel, size_t step=0) { std::vector<typename Solution::value_type> candidates; if (kernel.IsSolution(s, step)) kernel.ProcessSolution(s, step); else { kernel.PickCandidates(candidates, s, step); for (size_t i=0; i < candidates.size(); ++i) { typename Solution::value_type stored = s[step]; s[step] = candidates[i]; backtrack(s, kernel, step+1); s[step] = stored; } } } #endif //BACKTRACKING_H_

Figure 58 Backtrack class

bool Kernel::IsSolution(const Spectrum& spectrum, size_t step) const { return services.empty() || step == spectrum.size(); }

Figure 59 Function IsSolution

void Kernel::ProcessSolution(const Spectrum& spectrum, size_t step) { if (isStrictSolution(spectrum) || isRelaxedSolution(spectrum)) solutions.push_back(spectrum); }

Figure 60 Function ProcessSolution

void Kernel::PickCandidates(std::vector<Slot>& candidates, const Spectrum& spectrum, size_t step) const { //-- make sure that I don't split if (step > 0 && spectrum[step -1].GetService().GetBandwidthSize() > 1) { size_t i; for (i=std::max(0, (int)(step - spectrum[step -1].GetService().GetBandwidthSize())); i < step; ++i) if (spectrum[i].GetService() == spectrum[step -1].GetService()) break; if (step - i < spectrum[step -1].GetService().GetBandwidthSize()) { candidates.push_back(spectrum[step -1]); return; } }


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//-- pick all possible candidates for (std::vector<Service>::const_iterator it = services.begin(); it != services.end(); ++it) if (!inSpectrum(*it, spectrum) && fits(*it, spectrum, step)) candidates.push_back(*it); candidates.push_back(spectrum[step]); }

Figure 61 Function PickCandidates

bool Kernel::fits(const Service& service, const Spectrum& spectrum, size_t index) const { for (size_t i=0; i < service.GetBandwidthSize(); ++i) if (index +i >= spectrum.size() || !spectrum[index+i].IsEmpty() || spectrum[index+i].GetPower() < service.GetPower()) return false; return true; }

Figure 62 Function fits

bool Kernel::inSpectrum(const Service& service, const Spectrum& spectrum) const { for (Spectrum::const_iterator it = spectrum.begin(); it != spectrum.end(); ++it) if (service == it->GetService()) return true; return false; }

Figure 63 Function inSpectrum

bool Kernel::isStrictSolution(const Spectrum& spectrum) const { //-- all requested services must be included for (std::vector<Service>::const_iterator it = services.begin(); it != services.end(); ++it) if (!inSpectrum(*it, spectrum)) return false; return true; } //-------------------------------------------------------------------------------- // bool Kernel::isRelaxedSolution(const Spectrum& spectrum) const { //-- included services must have higher priority than those left out for (std::vector<Service>::const_iterator it = services.begin(); it != services.end(); ++it) if (!inSpectrum(*it, spectrum) && false) return false; return true; }

Figure 64 Functions isStrictSolution and isRelaxSolution

Dynamic Radio Resource Management algorithms for an efficient use of TVWS

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