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TIitle: Planning optimization software tool for DVB-T and DVB-T2

Authors: María Lema Rosas, Evelyn Torras López

Directors: Silvia Ruiz Boqué, Mario García Lozano

Date: June 25 th 2010

Overview

Nowadays the implementation of the Digital Terrestrial Television network is an actual fact in the Spanish territory. Its development is crucial for the digital transition in those countries which mainly depend on terrestrial networks for the reception of multimedia contents.

With the aim of giving to the users a high quality signal but with the minimum cost for the operator, it arises the necessity of an optimization of the transmission network. In this way, there are variables that can be modified to obtain this goal, some of them are uncontrollable by the operators and the others are susceptible of optimization. In this last group, it can be found the static internal delays of the transmitters which are of special interest because changes can be done with cost zero.

The main objective of this project is the design and implementation of a planning optimization software tool that adjusts the internal static delay in transmitters of a digital broadcasting network. The final objective is to minimize the self-interfered areas and obtain the correspondent increase in coverage. The planning optimization software tool makes use of a metaheuristic algorithm and can obtain different layers of study with its corresponding results.

This report describes the problems raised by the current network, the algorithm adapted to the needs of the network to optimize, the implementation of the simulation platform as well as its subsequent validation stage, including several graphical results that allow the evaluation of the improvements introduced over the realistic scenario tested.

Finally it has to be mentioned that this work has been performed within the context of the FURIA project, which is a strategic research project funded by the Spanish Ministry of Industry, Tourism and Commerce.

Resumen

Hoy en día la implementación de una red de Televisión Digital Terrestre es una realidad en España y su desarrollo es crucial para la transición digital para aquellos países en los que la recepción de contenido multimedia depende exclusivamente de las redes terrestres.

Con el objetivo de dar al usuario una gran calidad de señal pero con un coste económico y reducido para las operadoras es necesaria la optimización de la res de transmisión. En este sentido, existen parámetros de red configurables para conseguir este objetivo, algunos de ellos son incontrolables para las operadoras, en cambio otros son susceptibles a la optimización. En este último grupo, se encuentran los retardos estáticos internos de los transmisores que son de especial interés debido a que se pueden modificar a coste cero.

EL principal objetivo del proyecto es el diseño y la implementación de una herramienta software para la planificación óptima de redes que ajusta el parámetro de los retardos estáticos de los transmisores en redes de difusión digital de contenidos con la intención de minimizar las zonas de auto-interferencia y con el correspondiente incremento de cobertura de red. La herramienta software para la planificación hace uso de un algoritmo metaurístico y puede obtener diferentes capas de estudio con sus correspondientes resultados.

La memoria describe los problemas planteados por la red actual, el algoritmo utilizado y como se ha adaptado a las necesidades de la red a optimizar, la implementación de la plataforma de simulación así como su etapa de validación, incluyendo gran cantidad de resultados gráficos que permiten la evaluación de las mejoras introducidas en la red realística testeada.

Finalmente se ha de mencionar que el presente trabajo ha sido realizado en el contexto del proyecto FURIA, el cual es un proyecto de búsqueda estratégica fundado por el ministerio de Industria, Turismo y comercio de España.

Título: Planning optimization software tool for DVB-T and DVB-T2

Autores: María Lema Rosas, Evelyn Torras López

Directores: Silvia Ruiz Boqué, Mario García Lozano

Fecha: 25 de junio de 2010

INDEX

INTRODUCTION ............................................................................................................ 1

CHAPTER 1. INTRODUCTION TO DVB-T ................................................................. 3

1.1 Changing to a digital mode ............................................................................... 3

1.2 Digital television around the world .................................................................... 3

1.3 DVB-T2 physical layer overview ....................................................................... 4

1.3.1 Basic principle ........................................................................................... 5

1.3.2 Time domain physical layer considerations .............................................. 6

1.4 Radio planning of DVB-T systems ................................................................... 7

1.4.1 Coverage computation .............................................................................. 8

1.4.2 Interferences and their impact on coverage .............................................. 9

1.4.3 Solutions to improve coverage area ........................................................ 10

CHAPTER 2. DESIGN OF AN ALGORITHM TO MAXIMIZE COVERAGE ............... 11

2.1 Problem description ........................................................................................ 11

2.2 Metaheuristic solution ..................................................................................... 12

2.3 Introduction to Simulated Annealing ............................................................... 13

2.3.1 Description of the algorithm .................................................................... 13

2.4 Simulated annealing parameters .................................................................... 15

2.4.1 Temperature ............................................................................................ 15

2.4.2 Number of iterations ................................................................................ 16

2.4.3 Conditions of convergence ...................................................................... 16

2.4.4 Definition of the cost function .................................................................. 16

2.5 Implementation issues .................................................................................... 18

2.5.1 Study of the temperature reduction coefficient ........................................ 18

2.5.2 Maximum delay value delimitation .......................................................... 20

2.5.3 Study of the equilibrium condition ........................................................... 22

CHAPTER 3. SIMULATION PLATFORM DEVELOPMENT ...................................... 25

3.1 Required software tools .................................................................................. 25

3.1.1 Sirenet ..................................................................................................... 25

3.1.2 Microsoft Visual Studio 2008 ................................................................... 25

3.1.3 MATLAB .................................................................................................. 26

3.1.4 Google Earth ........................................................................................... 26

3.2 Simulator structure ......................................................................................... 27

3.2.1 Main program .......................................................................................... 28

3.2.2 Scenario .................................................................................................. 28

3.2.3 Algorithm ................................................................................................. 30

3.2.4 Graphical User Interface ......................................................................... 30

3.3 Code optimization ........................................................................................... 34

CHAPTER 4. RESULTS: OPTIMIZATION OF REALISTICS SCENARIOS .............. 35

4.1 Definition of the scenarios .................................................................................. 35

4.1.1 Tarragona scenario ................................................................................. 35

4.1.2 Lleida scenario ........................................................................................ 36

4.1.3 Barcelona scenario ................................................................................. 37

4.2 Results for geographical optimization ............................................................ 38

4.2.1 Tarragona ................................................................................................ 40

4.2.2 Lleida ....................................................................................................... 41

4.2.3 Barcelona ................................................................................................ 42

4.3 Optimizing population density ........................................................................ 43

4.3.1 Population distribution ............................................................................. 44

4.3.2 Discussion over Tarragona ..................................................................... 45

4.3.3 Discussion over Lleida ............................................................................ 47

4.3.4 Discussion over Barcelona ...................................................................... 49

4.3.5 Conclusions for the cost function approach ............................................ 50

CHAPTER 5. RESULTS: IMPACT OF OTHER VARIABLES OVER THE OPTIMIZATION PROCESS .......................................................................................... 51

5.1 Receiving antennas ........................................................................................ 51

5.1.1 Definition of the scenarios ....................................................................... 51

5.1.2 Yagi antennas: Fixed environment .......................................................... 53

5.1.3 Omnidirectional antennas: Mobile environment ...................................... 58

5.2 Types of receivers .......................................................................................... 64

5.2.1 Definition of the scenarios ....................................................................... 64

5.2.2 Yagi antenna: Fixed environment ........................................................... 65

5.2.3 Omnidireccional antenna: Mobile environment ....................................... 67

CHAPTER 6. CONCLUSIONS .................................................................................. 69

CHAPTER 7. REFERENCES .................................................................................... 71

APPENDICES .................................................................................................................. I

FIGURE INDEX

Fig. 1.1 Different standards around the world ................................................................. 4 Fig. 1.2 Time and frequency representation of the SC and OFDM. In OFDM, N data symbols are transmitted simultaneously on N orthogonal subcarriers [3] ...................... 5 Fig. 1.3 Presentation of the OFDM subcarrier frequency ............................................... 6 Fig. 1.4 Cyclic Prefix added at the beginning of the OFDM symbol ............................... 6 Fig. 2.1 Algorithm work flow .......................................................................................... 12 Fig. 2.2 Block diagram of the search of the initial temperature ..................................... 14 Fig. 2.3 Block diagram of SA ........................................................................................ 15 Fig. 2.4 Cost function computation ............................................................................... 17 Fig. 2.5 Cost function vs. Delta ..................................................................................... 19 Fig. 2.6 Execution time vs. Delta .................................................................................. 19 Fig. 2.7 Delay evolution (4 transmitters, 200 iterations) ............................................... 20 Fig. 2.8 Cost function vs. Max Delay ............................................................................ 21 Fig. 2.9 Execution time vs. Max Delay .......................................................................... 21 Fig. 2.10 Cost function vs. Beta .................................................................................... 22 Fig. 2.11 Execution time vs. Beta ................................................................................. 23 Fig. 3.1 Visualization of KML file ................................................................................... 27 Fig. 3.2 Software tool class diagram ............................................................................. 27 Fig. 3.3 Initial correspondence ...................................................................................... 29 Fig. 3.4 GUI interface .................................................................................................... 30 Fig. 3.5 Example of CIR results .................................................................................... 31 Fig. 3.6 Example of population results .......................................................................... 32 Fig. 3.7 Example of transmitter coverage results ......................................................... 33 Fig. 3.8 Example of CIR by population results .............................................................. 33 Fig. 4.1 Coverage area in Tarragona ............................................................................ 36 Fig. 4.2 Coverage area in Lleida ................................................................................... 37 Fig. 4.3 Coverage area in Barcelona ............................................................................ 38 Fig. 4.4 Type one receiver schema .............................................................................. 39 Fig. 4.5 Initial CIR for Tarragona region ....................................................................... 40 Fig. 4.6 Final CIR for Tarragona region ........................................................................ 40 Fig. 4.7 Initial CIR for Lleida region .............................................................................. 41 Fig. 4.8 Final CIR for Lleida region ............................................................................... 42 Fig. 4.9 Initial CIR for Barcelona region ........................................................................ 43 Fig. 4.10 Final CIR for Barcelona region ...................................................................... 43 Fig. 4.11 Population density for Tarragona ................................................................... 44 Fig. 4.12 Population density for Lleida .......................................................................... 45 Fig. 4.13 Population density for Barcelona ................................................................... 45 Fig. 4.14 Initial population covered in Tarragona .......................................................... 46 Fig. 4.15 Geographical based optimization in Tarragona ............................................. 46 Fig. 4.16 Population based optimization in Tarragona ................................................. 47 Fig. 4.17 Initial population covered in Lleida ................................................................. 47 Fig. 4.18 Geographic based optimization in Lleida ....................................................... 48 Fig. 4.19 Population based optimization in Lleida ........................................................ 48 Fig. 4.20 Initial population covered in Barcelona .......................................................... 49 Fig. 4.21 Geographic based optimization in Barcelona ................................................ 50 Fig. 4.22 Population based optimization in Barcelona .................................................. 50 Fig. 5.1 Example of a conventional Yagi horizontal radiation pattern ........................... 54 Fig. 5.2 Initial coverage in Tarragona in fixed scenario with receiver type one ............ 54 Fig. 5.3 Initial coverage in Lleida in fixed scenario with receiver type one ................... 54 Fig. 5.4 Initial coverage in Barcelona in fixed scenario with receiver type one ............. 55 Fig. 5.5 Final CIR in Tarragona in fixed scenario with receiver type one ..................... 55 Fig. 5.6 Final CIR in Lleida in fixed scenario with receiver type one ............................ 56

Fig. 5.7 Final CIR in Barcelona in fixed scenario with receiver type one ...................... 56 Fig. 5.8 Initial percentage of pixels covered ................................................................ 57 Fig. 5.9 Final percentage of pixels covered ................................................................. 57 Fig. 5.10 Initial coverage in Tarragona in portable scenario with receiver type one ..... 58 Fig. 5.11 Initial coverage in Lleida in portable scenario with receiver type one ............ 58 Fig. 5.12 Initial coverage in Barcelona in portable scenario with receiver type one ..... 59 Fig. 5.13 Final coverage in Tarragona in portable scenario with receiver type one ..... 59 Fig. 5.14 Final coverage in Lleida in portable scenario with receiver type one ............ 60 Fig. 5.15 Final coverage in Barcelona in portable scenario with receiver type one ...... 60 Fig. 5.16 Percentage of pixels covered with at least the minimum power .................... 61 Fig. 5.17 Initial coverage of omnidireccional TXs ......................................................... 63 Fig. 5.18 Final coverage of omnidireccional TXs .......................................................... 63 Fig. 5.19 Final coverage according to population ......................................................... 63 Fig. 5.20 Type two receiver schema ............................................................................. 64 Fig. 5.21 Final coverage in Tarragona in fixed scenario with receiver type two ........... 65 Fig. 5.22 Final coverage in Lleida in fixed scenario with receiver type two .................. 65 Fig. 5.23 Final coverage in Barcelona in fixed scenario with receiver type two ............ 66 Fig. 5.24 Comparison of final percentage of pixels covered in fixed scenario .............. 66 Fig. 5.25 Final coverage in Tarragona in portable scenario with receiver type two ...... 67 Fig. 5.26 Final coverage in Lleida in portable scenario with receiver type two ............. 67 Fig. 5.27 Final coverage in Barcelona in portable scenario with receiver type two ...... 67 Fig. 5.28 Comparison of final percentage of pixels covered in portable scenario ....... 68

TABLE INDEX

Table 1.1 Specified length of the guard interval [4] ........................................................ 7 Table 1.2 Link budget results .......................................................................................... 9 Table 3.1 Example of KML file ...................................................................................... 27 Table 3.2 Pixel information ........................................................................................... 28 Table 3.3 Legend of population density distribution ..................................................... 32 Table 3.4 Example of code ........................................................................................... 34 Table 3.5 Example of loop jamming ............................................................................. 34 Table 4.1 Analysed transmitters in Tarragona .............................................................. 36 Table 4.2 Analysed transmitters in Lleida ..................................................................... 37 Table 4.3 Analysed transmitters in Barcelona .............................................................. 38 Table 4.4 Radio parameters ......................................................................................... 39 Table 5.1 Principal radio characteristics of portable scenario ...................................... 52 Table 5.2 Principal radio characteristics of fixed scenario ............................................ 52 Table 5.3 Main roads in Lleida region .......................................................................... 62 Table 5.4 Characteristics of the transmitters added ..................................................... 62

INTRODUCTION 1

INTRODUCTION

In the context of broadcasting television networks, analogical technologies are being replaced by digital ones. This transition of analogical to digital by the roll out of the Digital Video Broadcasting Terrestrial (DVB-T) standard provides advantages in the exploitation of bandwidths, more robustness in front to the noise and another series of advantages that are translated in a clear improvement of the image and the sound, besides adding new applications for users.

One of the strengths of DVB-T is that it can be deployed with a single frequency network (SFN) scheme. This is possible because the physical layer is based on Orthogonal Frequency Division Multiplexing (OFDM) and the introduction of a cyclic prefix (CP) between consecutive symbols. SFNs allow a more efficient use of available bandwidth than classic Multiple Frequency Networks (MFNs). They also simplify the radio-planning process since frequency allocation strategies are not required.

Due to the multipath tolerance that the OFDM scenario has, one receiver is allowed to combine signals coming from different transmitters (TXs), as long as these signals remain inside the guard interval (GI). This interval copes with intersymbol interference (ISI) induced by the multipath channel. The fact of combining signals provides a diversity gain in reception only in the case that TXs are allocated near of the receiver area, however it can happen that a signal is delayed due to the distance between the TX and the receiver area. In that case, this signal cannot be combined because it falls outside of the GI. This phenomenon is call self-interference.

With the aim of giving users a high quality signal but at the same time of limiting expenditures for the operator, it turns necessary the optimization of the transmission network, bearing in mind that the most important factor for the optimization is the minimization of self- interference. There are variables that can be modified to obtain this objective, some of them are uncontrollable by operators, as for example: The propagation environment and the configuration of OFDM receivers. Given this, different receiver options should be considered and assessed when optimizing the network planning. On the other hand, there exists another type of variables that are susceptible of optimization, such as the geographic position of the TXs, their transmission power, the configuration of their radiant system and their static internal delays. Among these, the last one is of special interest because changes can be done with cost zero.

Given this, this project develops a planning optimization software tool that adjusts the static delays of the TXs in SFNs in order to minimize self-interfered areas and with the correspondent increase in coverage for a given broadcasting video network. To solve the optimization problem the software tool makes use of the metaheuristic Simulated Annealing (SA), previously studied in [1], but further improved in this project by adding a complimentary and complete software tool and more layers of study.

The report is organized as follows. First of all a brief introduction to the DVB-T and DVB-T2 standard is presented, consecutively, after setting the problem, the main objectives of the project are explained and the starting points of the simulator are established. Examples of this are the link budget that determines the simulation thresholds and the territory to optimize. Chapter 2 describes the SA algorithm and its parameters, followed by the characterization of the algorithm itself. Then the

2 Planning optimization software tool for DVB-T and DVB-T2

optimization software tool structure and implementation is explained in chapter 3. Results are divided in two parts, first of all chapter 4 presents the results obtained with realistic scenarios. Chapter 5 focuses in specific topics and assess the impact of other variables that affect on the received signal, as for example types of receiving antennas or the modification of the type of receiver. Finally, the report is closed with some conclusions, and a set of ideas that can give further lines of investigation on this topic.

This project has been developed under a more ambitious national project named FURIA [2]. FURIA is a SSP (Strategically Singular Project) in the field of Network Audiovisual Technologies, whose main objective is to develop and validate the integration of emergent technologies for the spreading of audiovisual contents in fixed and mobile devices. Joining forces from the different national organisations (companies, technological centres and universities) with the final purpose of increasing the national technological level.

Following the same FURIA consortium definition of its activity [2], this collection of enterprises and organisms will be able to finish the investigation and development stages in the new contents of broadcasting audiovisual technologies, and will realise valuable contributions to the main standardization bodies in an industrial forum context, collaborating with technical proposals in the definition of the new DVB-T standard in the recent years, named DVB-T2.

It is expected that the generated research results of the consortium will be immediately applied, by means of generating pre-industrial outcomes.

Other objectives of the FURIA project are:

Establishment of relationships with other national and European projects, which will allow the enrichment of Spanish technological level.

Contributions to forums and European standards, to grow up Spanish technological consortium acknowledgment and to have influence in the standardisation section.

CHAPTER 1. INTRODUCTION TO DVB-T 3

CHAPTER 1. INTRODUCTION TO DVB-T

In the context of video broadcasting networks, analogue technologies have been already replaced by digital transmission technologies. This transition from analogue to digital through the implantation of the DVB-T standard provides the networks with certain advantages such as a better bandwidth exploitation, more robustness in front of noise, and more advantages that are reflected in the improvement on the image and sound, and also includes new applications to the users.

This chapter aims at explaining the main changes from the analogue technology to the digital one, as well as the theoretical background necessary to understand the main features of the DVB-T standard.

1.1 Changing to a digital mode

Digital television arises due to the fact that provides better characteristics than the analogue television. The old method had less spectral efficiency, as every single image was transmitted, in order to improve this mechanism, digital television introduces MPEG-2 compression, which sends the changes of the images and thus, much less information. Due to this fact, the required bandwidth is reduced and then on the same channel several programs can be multiplexed, or they can be transmitted with high definition, multimedia, interactivity can be included, etc. The spectral efficiency is then much higher on digital systems. Another set of common problems found in analogue television are ghost of images due to multipath in the radio channel, noise from weak signals, which degrade the quality of the signal and sound, etc. All this is efficiently solved by a digital transmission.

Moreover, the fact of changing to the DVB-T and DVB-T2 standard does not imply an increment on deployment cost, as most part of the already existing infrastructure can be re-used.

1.2 Digital television around the world

The change towards digital television is being done all around the world. Each country, or a set of countries have decided which standard are going to adapt in their territory. For instance in Europe it is used the DVB-T standard, but there are other possibilities as it is shown in Fig 1.1.

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Fig. 1.2 Time and frequency representation of the SC and OFDM. In OFDM, N data symbols are

transmitted simultaneously on N orthogonal subcarriers [3]

Digital broadcast television is based in OFDM, a well-known transmission technique widely used in communications on the last years. For instance, it is the technology adopted by ADSL, some version of the IEEE 802.11 standar, IEEE 802.16, LTE, data transmission in power-lines and many other standards. OFDM is a very powerful transmission technique. It is based on the principle of transmitting simultaneously many narrow-band orthogonal frequencies, named subcarriers. The number of subcarriers is often noted as N. These frequencies are orthogonal to each other which (in theory) eliminates the interference between channels. Each frequency channel is modulated with a possibly different digital modulation. The frequency bandwidth associated with each of these channels is then much smaller than if the total bandwidth was occupied by a single modulation, which is known as the Single Carrier (SC) (see Fig. 1.2). Having a smaller frequency bandwidth for each channel is equivalent to greater symbol time (N times longer) and then better resistance to multipath propagation (with regard to the SC option). Better resistance to multipath and the fact that the carriers are orthogonal allows a very high spectral efficiency. For these reasons, OFDM is often presented as the best performing transmission technique used for wireless systems.

1.3.1 Basic principle

OFDM makes use of the properties of the Discrete Fourier Transform (DFT) to generate the final signal without the need of one oscillator per sub-carrier. In particular, to speed the calculus, the Fast Fourier (FFT) algorithm is used instead. The FFT can be applied as long as the number of points in the sampled signal is a power of 2 (e.g. N = 256). This condition is easily imposed by the DVB-T standard (or any other standard). The IFFT is the Inverse Fast Fourier Transform operator and realises the reverse operation. OFDM theory shows that the IFFT of magnitude N, applied on N symbols, realises an OFDM signal, where each symbol is transmitted on one of the N orthogonal frequencies. The symbols are the data symbols of the type QPSK, QAM-16 and QAM-64.

If the duration of one transmitted modulation data symbol is Td, then Td = 1/f, where f is the frequency bandwidth of the orthogonal frequencies. As the modulation symbols are transmitted simultaneously,


Fig. 1.3 Presentation of the OFDM subcarrier frequency

This duration, f, is the frequency distance between the maximums of two adjacent OFDM subcarriers, as it can be seen in Fig. 1.3. This figure shows how the neighbouring OFDM subcarriers have values equal to zero at a given OFDM subcarrier maximum, which is why they are considered to be orthogonal. In fact, duration of the real OFDM symbol is a little greater due to the addition of the CP.

1.3.2 Time domain physical layer considerations

After application of the IFFT a Cyclic Prefix must be added at the beginning of the OFDM symbol as it is shown on Fig.1.4. The CP allows the receiver to absorb the delay spread due to the multipath and to avoid intersymbol interference (ISI). The CP that occupies a duration called the Guard Interval (GI) is a temporal redundancy to give continuity to the OFDM signal. On the other hand, including this prefix reduces the effective data rate because during this time no new information is transmitted.

Following the DVB organization rules that are shown in the recommendations, there are several possible GIs. The operator may choose among these options considering the radio channel features in its particular deployment. Table 1.1 remarks the recommended lengths. Note that mode 8k and 2k stand for the two different periods of symbols that are considered in the standard: 896 s and 224 s respectively. The GI is always given as a fraction of this value. This fraction represents de percentage of time in which no new information is transmitted. Consequently, for the same ‘degree of inefficiency’ the 8k mode allows deploying larger SFNs.

Fig. 1.4 Cyclic Prefix added at the beginning of the OFDM symbol


Table 1.1 Specified length of the guard interval [4]

For instance, in this project it has been applied the OFDM 8k mode, with 56μs of GI. The longest GIs are suitable for networks with longer distances between TX stations, as for example with national SFNs. The shortest intervals are suitable for regional or local broadcast transmissions. In summary, the longer the guard interval is, the less interference will appear, but less information is sent.

1.4 Radio planning of DVB-T systems

Radio planning DVB-T networks allows basically two types of deployment, on the one hand the classic Multi – Frequency Networks (MFN), and on the other hand Single – Frequency Networks (SFN).

Conventionally planned DVB-T networks consist of TXs with independent programme signals and with individual radio frequencies. Therefore they are also referred to as MFN. In order to cover large areas with one DVB-T signal a certain number of radio-frequency channels is needed. The number of channels depends on the robustness of the transmission, i.e. the type of modulation associated with the applied channel code rate and on the objective of planning, (full area coverage or coverage of densely populated areas only). As the robustness of a broadcasting system is generally expressed in terms of protection ratios, one might expect that the number of channels needed for DVB-T is significantly lower than for analogue broadcasting as the protection ratios are generally lower in the digital case. However, due to some other phenomena, the number of radio-frequency channels needed for conventionally planned DVB-T networks tends to be in the same order as with analogue TV systems. The frequency resource expressed as the number of channels needed to provide one signal at any location is far higher with MFN than with SFN. Nevertheless, one of the advantages that MFNs have is that it is not necessary to have synchronous emissions as one area is only served by one TX.

In a SFN, all TXs are synchronously modulated with the same signal and radiate on the same frequency. Due to the multi-path capability of OFDM with its GI, signals from several TXs arriving at a receiving antenna may contribute constructively to the total wanted signal. However, the limiting effect of the SFN technique is the so-called self-interference of the network. If signals from far distant transmitters are delayed more than allowed by the GI they behave as noise-like interfering signals rather than as wanted signals. The strength of such signals depends on the propagation conditions, which will vary with time. The self-interference of an SFN for a given transmitter spacing is reduced by selecting a large GI. In order to keep the redundancy due to the GI down to a reasonably low value (25 %), the useful symbol length has also to be large given the transmitter spacing in most European countries. Thus the 8k-mode was introduced. On the other hand a smaller GI would lead to a higher number of TXs.


With the SFN technique large areas can be served with a common multiplex at a common radio frequency. Therefore the frequency efficiency of SFNs appears to be very high compared to MFNs. Gaps in the coverage area of an SFN are easily filled by adding a new transmitter or repeater without the need for additional frequencies.

In conventionally planned networks and particularly in single TX situations, a common way to achieve service continuity at a high percentage of locations is to include a relatively large fade margin in the link budget and thus to increase the transmitter power significantly. However with omnidirectional reception in SFNs, where the wanted signal consists of several signal components from different transmitters the variations of which are only weakly correlated, fades in the field strength of one transmitter may be filled by another transmitter. This is translated into a receiving gain, and therefore transmitters with the SFN technique should be able to transmit with lower power.

As it has been explained on the problematic, this project has adopted the SFN technique as it seems to be the most efficient when deploying a DVB-T network and in fact is the option used in Spain at several geographical levels (national, regional, local).

1.4.1 Coverage computation

In order to make possible a feasible analysis of the DVB-T/T2 coverage, it is necessary to compute a link budget to guess the amount of power required at the receiver. It is important to fix the initial level of carrier to interference ratio (CIR) that the receiver must achieve in order to have a good visualization of TV, this value was extracted from [4], and placed as an input data in the link budget.

The link budget takes into account all the losses or degradation the signal suffers since it is emitted until arrives to the receiver end. Depending on the sophistication that is desired to provide to the calculation more or less parameters can be taken into account. For instance, in this project, environments focused on fixed scenario have the same link budget than the portable ones, as it is quite difficult to combine several parameters on a single receiver in Sirenet.

The whole link budget is placed in Appendix A, however below these lines there is a summary in Table 1.2 with the most relevant aspects.


Table 1.2 Link budget results

Parameter Units Result

Band Band V

Receiving Condition Fixed antenna (outdoor 10 m)

Frequency f [MHz] 800

Boltzmann Constant k [J∙K‐1] 1,38E‐23

Bandwidth B [Hz] 7,60E+06

Temperature T0 [K] 290

Thermal noise power Pn,th [dBW] ‐135,2

Receiver noise figure F [dB] 7

Total noise power Pn [dBW] ‐128,2

Minimum carrier to noise ratio required by system

CNR [dB] 2 8 14 18 26

Minimum receiver signal input power

Ps min [dBW]

‐126,2 ‐120,2 ‐114,2 ‐110,2 ‐102,2

In order to assure an 18 dB of carrier to noise ratio, the minimum power received must be almost -110 dBW, which corresponds to -80 dBm. Based on these results every pixel that receives a value of power beyond this threshold is considered to be covered by one or more transmitters.

Regarding the coverage area, the one selected is the entire region of Catalonia. The TXs placed in this area are a total of 180, which is a very high number taking into account that the scenario must be optimized, so in the case the whole Catalonia territory is set to be analysed, the simulation time would be prohibitive, in this case different pieces of terrain are selected. As for the scenario, the pixel resolution may vary depending on the exact detail that the results are desired, but it is necessary to mention that, if this resolution is set too small the execution time is going to rise. In this project it is considered that on each pixel there’s placed a receiver sharing all the same characteristics.

In DVB-T there are basically two types of receiver defined, one that catches the transmitter whose echo first arrives, and the other selects the transmitter taking into account the most powerful signal. This is more detailed when explaining all the sets of scenarios prepared for the simulations. The TX’s information is more specific, as each one has different values of altitude or position. However, the emitting power of all the TXs is considered the same, although it is known that in real conditions each TX can have its own configuration of power, tilt, delay value, etc.

1.4.2 Interferences and their impact on coverage

Several types of interferences can interact and contribute negatively on the received signal. For instance the same signal received, can have one or more echoes due to the multipath phenomenon. DVB-T standards offer protection against these, due to the orthogonal frequency multiple division, every frequency carrier is divided into a subset of subcarriers, being smaller in frequency bandwidth terms. Even more protection is added with the GI, which can protect the receiver from other types of interferences. In a SFN network all the TXs send information at the same frequency, being possible the reception of one or more signals coming from different sources. If this signal echoes are received inside the GI, then it is said that the signal contributes positively, on the


contrary will degrade the signal quality. Moreover, other services operating at near frequencies can have an interfering contribution to the signals.

1.4.2.1 Deployment of an SFN network

As it is previously explained in sections above, the SFN network allows to complement the received signal with the ones coming from other transmitters. However, this idea can be extrapolated into the analogue, which means that one pixel that initially is covered by one or more transmitter, loses its quality due to the strong self-interference that suffers from the transmitters placed nearby. For this reason SFN networks must be optimized, in order to reduce these levels of interference and make sure that all the signals coming to a given receiver perform a good quality one, instead of destroying it.

It is clear that the longer the GI is, the easier the reduction of self-interference, as many echoes will arrive inside the CP. However this also implies a less efficient transmission since no new information is contained in the added interval and so the effective data rate is reduced. Besides, mobile television is gaining focus particularly in the context of the DVB-T2 standard, and long symbols with large GIs are much more sensitive to Doppler Effect. A good system design implies as short as possible GIs while maintaining sufficient multipath protection.

1.4.3 Solutions to improve coverage area

Several solutions arise in order to reduce the impact of the self-interferences in SFN networks. There exist basically two types of variables, the ones which are uncontrolled by the operator and those that are susceptible of optimization.

On the first classification of variables one can contemplate the propagation environment and the configuration of the OFDM receivers. The fact that one receiver is set to one type or another changes the coverage and the impact on the interferences, this is studied in further chapters in this project. However, this cannot be managed by the operator, the variables that can control are those regarding the transmitters such as, its geographic position, the configuration of the radiant system (radiation pattern, downtilt, nullfilling techniques), the transmission power or the static delays. Moreover, in a context of operative DVB-T networks and in some cases in the beginning of a transition towards DVB-T2, powers, antennas and positions (in this order) are increasingly more static and unlikely to be dramatically changed.

Given this, this project is focused on the optimization of the static delays of the transmitters in an SFN network. The final objective is then, to reduce the self-interfered areas and with its consequent increase in coverage area. This action can be performed manually, but it turns very difficult to find an optimal solution due to the interdependencies of the variables. For this reason, this project proposes a technique that optimizes a set of transmitters in a given area, and searches for the set of delays that minimize the areas affected by ISI.

CHAPTER 2. DESIGN OF AN ALGORITHM TO MAXIMIZE COVERAGE 11

CHAPTER 2. DESIGN OF AN ALGORITHM TO MAXIMIZE COVERAGE

2.1 Problem description

The problematic analysed in this project, as it was presented in the section before, is to improve the coverage area by means of changing the delays of all the transmitters so the maximum number of pixels under a test area are improved. First of all, let analyse with a simple example which is the impact of changing the internal delay of a repeater.

Let consider a canonical scenario in which two TXs (L on the left and R on the right) are deployed in a flat terrain. Under these circumstances, the border between the areas with the second contribution falling inside or outside the GI is given by the locus of points where the difference of the distances to the two TX is a constant, that is a hiperbola with both TXs as foci. However, not the full area on the left of the left semi-hiperbola and on the right of the right semi-hiperbola are necessarily out of coverage. As long as the CNR is good enough, other contributions can be received out of the GI. Self-interfered areas can be modified by means of changes on static delays. Thus, for example, if the internal delay of L is increased, then R has virtually got closer and consequently the left semi-hiperbola is reduced (eventually eliminated). Conversely, this action has a negative effect on R, because now L has been virtually moved further away and so the self-interfered area on the right is increased. This simple modification could be useful for example in an environment in which R transmits with a higher power and so can cope with the signal from L causing interference. More details on this can be found in [1].

To be able to solve this problem in a case with many TXs, it is necessary to find the combination of delays such as the highest number of pixels is improved. The simplest possibility to solve the problem is just using a brute force search. Of course this cannot be done, the search requires a prohibitive computational time. In particular, assuming a finite set of m possible delays and n TXs, each value can be assigned to every TX with repetition. The assignment of the same values to different TXs also changes the solution (TXA-delay1, TXB-delay2) (TXA-delay2, TXB-delay1). Thus, the solutions space is a variation with repetition of m values taken from n in n:

, (2.1)

The number of solutions is then, dependent on the maximum delay value and the number of transmitters. For instance, focusing on a scenario with 40 TXs and 10000 possible delays (from 0 to 100 s in steps of 0.01 s), then the number of possible solutions is 3.98·1016020. So a brute force search of the solution is completely infeasible and other options have to be developed. This problem is in fact a combinatorial optimization one which can be viewed as searching for the best element of some set of discrete items, therefore, in principle, any sort of iterative search algorithm or metaheuristic can be used to obtain good solutions in a reasonable computation time.

12

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2.3 Introduction to Simulated Annealing

SA is widely used in the resolution of combinatorial optimization problems, and in particular to try to find solutions to NP-hard problems. In this type of problems it is not possible finding an optimal solution in a polynomial time in relation to the size of the problem. With the help of metaheuristics, and in particular of SA, a near-optimal solution can be found in a reasonable period of time. For instance, the traveling sales man problem is a classic one, and there are many others.

2.3.1 Description of the algorithm

Let consider the physical process in which the temperature of a given liquid is reduced. If this action is performed abruptly below its melting point, the result is a disorganized state with a much higher energy than the one corresponding to the compost in crystalline state. In this case, the molecules have reached a local minimum of energy. On the contrary, if the liquid’s temperature is reduced slowly and according to a given cooling rule, the liquid evolves to a state of equilibrium with minimum internal energy, it is the one corresponding to an ordered and crystalline compost.

Inspired on this procedure, the SA algorithm was developed as a helpful tool in the resolution of combinatorial optimization problems. The cooling process is formulated as the search of the solution implying a lower value of a given cost function (energy). This is done as follows:

Starting with a unique solution of a problem, SA changes it in an iterative and random fashion. These modifications are done on a restricted way, so for example if the solution is represented as a vector or combination of elements, only one position of it would be changed. For each new proposed solution, SA compares the final energy (the cost functions) of both solutions. If the energy obtained on the new solution is lower than the old one, then this new solution is accepted and substitutes the current one. If, on the contrary, the energy rises, the new solution is accepted with a probability determined by the Boltzmann factor e‐Δf/T, where Δf is the energy difference f(i+1)‐f(i) between the new state i+1 and the one before i, and T corresponds to the current system’s temperature. This accepting rule is known as the Metropolis criterion. In the case of a combinatorial optimization problem it is defined as:

11, 1

, 1 (2.2)

The probability of accepting a new solution worse than the current one is lower as the difference of energies grows, and it is also lower as the temperature of the system decreases. This procedure of generation, and acceptation or refutation is repeated a given number of iterations for the same value of temperature. Once all the iterations

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So, following the above explanation, the initial value of temperature T0 must be calculated to guarantee that it is high enough so that almost all the transitions are accepted. Therefore, it is necessary to define a ratio of acceptances (ratio=transitions accepted/all transitions) and when it is near to 1 (or 100% of probability) then this temperature is said to be suitable. To find this value, the next procedure has been implemented: Starting with a small temperature, this is repeatedly multiplied by 1.5 until the ratio is comprised between 0,8 - 0,9.

The way in which the temperature T is decremented is important in terms of execution time and final cost as well. For this particular study-case, the following schema has been used because it preserves the convergence theory of SA as much as possible [12]:

·

(2.3)

The aggressiveness in the reduction of T can be controlled with , so that the simulation time can be adjusted to the available hardware capabilities. On the other hand σ represents the standard deviation of the cost evolution with temperature Ti. In particular, if changes on T are very small on state i, then a sharper decrease is promoted by σ.

2.4.2 Number of iterations

Ideally the reduction of temperature must be done when, for a given level of temperature the system has reached the steady state; but, the fact of doing this, leads into an unacceptable processing demand. For this reason it is defined an upper bound on the total number of iterations (transitions) that the algorithm does for a single value of temperature. This number must be close to the number of the neighbour solutions [13] as on each intern loop the algorithm visits a number of these before reducing the temperature value.

2.4.3 Conditions of convergence

The algorithm finishes its search when the ratio of accepted solutions is lower than 1%. When the system reaches this situation, it means that there will be no more significant changes on the cost function. Obviously, the algorithm also finishes when the cost function is zero.

2.4.4 Definition of the cost function

The definition of the cost function is very important when designing SA. This function is the one that must be minimized by changing continuously some parameters of the system. On this project it is necessary to minimize the total number of pixels not totally covered on a given region.

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This new delay value of the transmitter under test is the current proposed solution, at this point, the algorithm analyses if this solution leads on a reduction of the total number of pixels not covered, as is to say, if the cost function is reduced. To compute this value, the program calculates the fictitious distance (the real distance plus an increment due to the delay applied) of the selected transmitter respect all the pixels of the scenario, and it is analysed how it affects to the total coverage. As it has been explained on the first chapter of this project, the fact of changing the fictitious distance of a transmitter towards any pixel, leads to a change in the moment in which the signal arrives to each receiver, being possible that if initially was interfering, now can contribute positively, or vice versa.

The cost function follows a few simple rules to analyse if a pixel is covered or not. The first step is to analyse for each pixel which are the transmitters’ signals that arrive inside the GI, and which are interfering the useful ones. Once this classification is done, the cost function can calculate the CIR value for each pixel and can classify the pixels according its coverage.

When there is no contribution of any signal in which the received power is higher than the sensitivity, the pixel is considered as null as it is not receiving enough power from any transmitter of the scenario. This situation can never be improved as the transmitted power is not a matter of optimization, just the propagation of the signal arriving to a pixel. Moreover, it may happen that due to the time instant of arrival of some signal echoes they won’t be placed inside the GI, being a source of interference. As a result the CIR of the pixel (receiver) is below the desired one. In this case, the pixel is considered as not covered or Pixel KO, because modifying the time instant of the transmitters this problem can be solved. In any other case, the pixel is considered like covered or Pixel OK. The Fig 2.4 explains the above lines.

When the algorithm classifies all the pixels according to the explanation, it is computed the total number of Pixels KO or the total population affected regarding the two approaches of computing the cost function.

The number obtained is the cost solution or final energy of the proposed solution. Now it is evaluated by the Metropolis criterion that it will decide that if the solution is accepted or not. All these steps will be repeated as many times as the algorithm determines.

2.5 Implementation issues

As it was explained in the above section, it is very important to adjust all the parameters in order to obtain a better performance of the algorithm. This section explains the different measurements that have been done in this project to compute the values of the principal parameters. It is important to highlight that when analysing the behaviour of the algorithm, it is necessary to arrive into a commitment between execution time and final value of the cost function.

2.5.1 Study of the temperature reduction coefficient

This parameter (delta: δ) is important in the sense of execution time and final values of the cost function. If the temperature is not “significantly different” from one loop to another, the probability of having the same result is very high. On the other hand, if the


temperature of the system decreases in a rapid way, then interesting solutions can be missed, although execution time reduces notoriously. As it is previously explained, it has been chosen a logarithmic reduction depending on the delta parameter. The higher it is, the stronger the temperature reduces.

In order to compute the best value of δ several simulations have been done, and it has been compared the different final values of the cost function and the execution times. The scenario was built with 15 transmitters and 700 iterations per value of temperature (maximum delay Number of transmitters) were executed.

Fig. 2.5 Cost function vs. Delta

Fig. 2.6 Execution time vs. Delta

1038

1040

1042

1044

1046

1048

1050

1052

1054

Number of pixels uncovered

Delta_0.1

Delta_0.5

Delta_0.9

Delta_3

0,0

1000,0

2000,0

3000,0

4000,0

5000,0

6000,0

7000,0

Time (s)

Delta_0.1

Delta_0.5

Delta_0.9

Delta_3


The graphs (Fig. 2.5 and 2.6) show the average final cost obtained from five simulations, as well as the maximum and minimum value obtained. The results show the previously announced effect of δ. As the temperature decreases more slowly, the total number of evaluated solutions increases, the solution space is more smoothly explored. Therefore, it is possible to obtain better cost values. However, when this happens, the execution time of the program raises exponentially, while the result of the cost function is just slightly lower (in average, just half a pixel comparing the two first cases).

According to the results, the value that best solves this trade-off is δ = 0.5. In this case, the average cost function is almost the same as when it is equal to 0.1 and the execution time is 3.6 times less. Besides, if higher values of delta are tested, the results on cost function worsen without an outstanding benefit in execution time. As a conclusion, from now on the value established for the temperature reduction coefficient is set to 0.5.

2.5.2 Maximum delay value delimitation

At the beginning of the simulations, SA is totally random, and the best result for each iteration is quite different from the one obtained just before. This is, because the higher the temperature is, the higher is the probability of accepting wrong solutions for the problem as well. On each simulation, there is a moment in which the algorithm stabilizes and finds a region in which the results are better, which is close to the final one.

The problem here is that is needed to provide the algorithm with a wide enough variety of delays. Setting the possible values to a too low range implies poorer solutions. On the other hand if the range is too large, the algorithm wastes iterations evaluating redundant solutions. Note that what it is important is the relative delay among transmitters and not the absolute values themselves. In order to find the appropriate maximum value (the minimum is fixed to zero) a testing simulation has been run. The obtained results are shown on Fig. 2.7.

Fig. 2.7 Delay evolution (4 transmitters, 200 iterations)

0,00E+00

1,00E‐05

2,00E‐05

3,00E‐05

4,00E‐05

5,00E‐05

0 20 40 60 80 100 120 140 160

Tíme (s)

iterations

TX_1

TX_2

TX_3

TX_4


The four transmitters start choosing delay values with random variation but as the system gets cooler, the delay values get nearer to the optimal ones. The maximum delay value was established initially at 50 s, and the final cost value was around 166 pixels not covered. As results show, transmitter four seems to need more range, as the final delay value is pretty near to the maximum one. For this reason, it may be profitable to prove higher values of delays.

In order to test the improvement in the cost function, several ranges of delays are tested in five simulations, and then the average cost is represented. The next graphs (see Fig. 2.8) show the results.

Fig. 2.8 Cost function vs. Max Delay

Fig. 2.9 Execution time vs. Max Delay

0

20

40

60

80

100

120

140

160

180

Number of pixels not covered

50

65

80

90

0

20

40

60

80

100

120

Execution tim

e (s)

50

65

80

90


The decline in cost function with respect the maximum delay value is very representative. Initially, establishing 50 s, it was considered a value which was not enough for the transmitters in order to minimize the number of pixels not covered. Results show that there were not enough values to find a suitable solution. When increasing the maximum number of delays the cost function decreases, arriving to a point (over 80 pixels) in which no improvement happens when changing the maximum delay value. The final value established for the following simulations is 90 s, as it has slightly lower cost function.

However, modifying this parameter has an impact on the execution time, as the equilibrium is computed as the product (first approximation) and this is the most rapid loop. Fig. 2.9 represents the evolution of the execution time depending on the value of Max delay. The value chosen is the one which takes more time to find an optimal solution, this is due to the fact that the neighbouring is much larger.

2.5.3 Study of the equilibrium condition

Equilibrium is the parameter which determines the number of iterations that must be done for a single value of temperature. Theory of SA mathematically demonstrates that if this parameter tends to infinite, then the best solution is found with probability one. Since this is not practical, an empirical value has been obtained.

This value is governed by a new parameter β(0<β<1) which represents a percentage of the total number of neighbour solutions, which is estimated to be the product of the number of transmitters and the maximum delay value established in the previous section. The main goal is to find the best value of β according to the final value of the cost function and execution time as well. The parameters established for these simulations are the same than in the previous one, four transmitters, maximum delay of 90 μs and temperature coefficient value δ=0.5.

Fig. 2.10 Cost function vs. Beta

78

80

82

84

86

88

90

92

94

96

98

Number of pixels not covered

Beta_0,1

Beta_0.5

Beta_1


Fig. 2.11 Execution time vs. Beta

The above graphs (Fig. 2.10 and 2.11) show the result of five simulations. The bar represents the average value and then it is represented the maximum and minimum value obtained. It must be taken into account that the fact of changing the value of β leads to an important change on the equilibrium value (number of transitions per value of temperature) which relates the total number of solutions that can be explored. When β is low, as in the case of 0.1, means that only 10% of the total neighbouring solutions are being explored, for this reason results are worse than in the case beta is equal to 1 and all of them are explored.

It is a matter of fact that if the number of iterations grows the execution time rises exponentially and, as it is a function of the number of transmitters, the bigger the scenario is more time will take to the simulator to find a solution. Nevertheless, this time results are not so high (over 100 s for only four transmitters), so in order not to lose any possible good solution it has been decided not to apply any value of beta in the equilibrium value.

0

20

40

60

80

100

120

Time(s) Beta_0.1

Beta_0.5

Beta_1

CHAPTER 3. SIMULATION PLATFORM DEVELOPMENT 25

CHAPTER 3. SIMULATION PLATFORM DEVELOPMENT

Once the algorithm to maximize the coverage is designed, it is necessary, as it has been explained before, to develop a Radio-planning Optimization platform in order to run the algorithm. The platform is the responsible to manage the input and output data needed by the algorithm and also is the responsible of representing the results in an understanding way for the user.

Given this, this chapter explains the different software tools that are needed to develop and implement the global Radio-planning Optimization platform. Consequently, it is explained how the optimization software is structured and implemented.

3.1 Required software tools

This section depicts the set of programs that have been required to develop and implement the optimization tool. They all integrate the development framework that has been used along this project.

3.1.1 Sirenet

Sirenet is a radio spectrum management tool set aside for radio networks planning and the electromagnetic compatibility analysis. The tool is based on the simulation of real environments relying on an advanced geographical information system (GIS), on the exact reproduction of the behaviour of the radio electric equipments and on the most advanced and current algorithms for the prediction of the radio propagation on different environments. The tool presents a friendly interface on a Windows platform. The managing is simple and intuitive and its functionality adapts to the needs of different user's profiles.

The choice of this tool and not other is justified by its proven capabilities in the analysis and synthesis of networks following the DVB-T standard. For this project it is used a digital elevation model (DEM) referenced by Universal Transverse Mercator (UMT) coordinates with a resolution of 20 m 20 m.

3.1.2 Microsoft Visual Studio 2008

This tool is an Integrated Development Environment (IDE) from Microsoft. It can be used to develop console and graphical user interface applications. In particular, Visual

Studio supports different programming languages such as C/C++, VB.NET and C#.

In this project both, C# and Visual Basic, have been used and combined taking the best of them. The reasons are indicated subsequently:


1. The C# language was created to be a simple, modern, general-purpose, object-oriented programming language. It provides support for software engineering principles and offers software robustness, durability, and programmer productivity.

2. Visual Basic is a programming language that allows creating Graphical User Interface (GUI) applications in a simple way. The use of both allows the programmers to create an application with very complex code but very simple GUI.

3.1.3 MATLAB

Matlab is a numerical computing environment that allows matrix manipulations, plotting of functions and data, implementation of algorithms and interfacing with programs written in other languages.

In this case this tool is only used for testing. In the first phases of the development, Matlab was used to picture and analyse the scenario created by the optimization tool. The drawback of this kind of representation is its simplistic view. Besides, creating transparencies over the DEM is not straight forward. For those reasons and in order to improve the quality of the outputs, Google Earth was used instead in the final version of the platform.

3.1.4 Google Earth

Google Earth (GE) is a virtual globe, map and geographic information program; it was created by Keyhole, Inc, a company acquired by Google in 2004. It maps the Earth by

the superimposition of images obtained from satellite imagery, and aerial photography.

This project uses the GE library, which allows to embed a GE application in a Visual Basic one, to show the final results obtained in a real digital map, clearly outperforming the initial results obtained with Matlab.

In order to show images in GE, it is necessary to create a Keyhole Mark-up Language (KML) file, which is a format used to display geographic data in an “earth browser” such as GE, Google Maps or Google Maps for mobile. KML uses a tag-based structure with nested elements and attributes and it is based on the Extensible Markup Language (XML) standard.

In the Table 3.1 it is shown an example of KML file, is possible to appreciate that the text file is written using tags, marked with ‘< >’, and values inside the tags. Fig. 3.1 shows the corresponding result of the previous file once the GE has read it and has processed the information.

Once the simulation tool was developed, the only program required to run it is GE that must be installed in the computer previously.


3.2 Simulator structure

In order to understand the Planning optimization software tool it is necessary a brief description of the programmed code. For this purpose, the class diagram is provided, showing the interconnection among all the elements (Fig.3.2).

Fig. 3.2 Software tool class diagram

<?xml version="1.0" encoding="UTF-8"?> <kml xmlns="http://www.opengis.net/kml/2.2"> <Document> <GroundOverlay> <name>CoberturaInicialCOLLSEROLA_BARCELONA</name> <color>80ffffff</color> <drawOrder>15</drawOrder> <Icon> <href>CoberturaInicialCOLLSEROLA_BARCELONA.jpg </href></Icon> <LatLonBox>

<north>41.9560278931201</north> <south>41.2461504003643</south> <east>2.87671247699269</east> <west>1.4274046397569</west> </LatLonBox>

</GroundOverlay> <Placemark>

<name>COLLSEROLA_BARCELONA</name> <styleUrl>#ImagenBTS</styleUrl> <description> Name: COLLSEROLA_BARCELONA Delay 5,30499565289588E-05 s </description> <Point> <extrude>1</extrude> <coordinates>2.11533107324137,41.4191951922794,0</coordinates> </Point>

</Placemark> </Document> </kml>

Table 3.1 Example of KML file

Fig. 3.1 Visualization of KML file


3.2.1 Main program

This part of the code is the core of the program; its main tasks are summarized as follows:

Creation of the scenario Responsible for calling the optimization algorithm and processing its results Is in charge of reading the input files and acquiring the required data It also implements and interfaces to talk with visual application Finally, it is responsible for the connection of all the elements and brings the

information from one part to other.

3.2.2 Scenario

This class contains all the necessary elements to create a virtual scenario based on the data loaded from the input files. This data is mainly of three types:

Target area to analyse: DEM, population density per pixel. Transmitter’s data: coordinates, name, power, individual coverage obtained with

Sirenet. Receiver’s data: basically the type of hardware implementation, which affects

the treatment of the OFDM signal. Further details are given in the results chapters.

Then it will be created a virtual pixel matrix, where each pixel represents an area of 1 km2 over the area to be analysed. The class Pixel contains all the necessary information of this reduced area as described in Table 3.2.

Note that information in bold is read from input files organized as three information layers, an altimetry layer, a population layer and a type of receiver layer. These represent the height above sea level, the number of inhabitants in the pixel and the kind of the receiver that the pixel has, respectively.

Table 3.2 Pixel information

Pixel Class Properties

Coordinates (x, y , height)

Population

Type of receiver

Real distance to each transmitter

Fictitious distance to each transmitter (after considering the device internal delay)Received power from each transmitter

Interference power level

Useful signal power level


There is no need to mention that all this data must be coherent in order to obtain valid results. This was verified with the help of Matlab and following all the steps that a user must perform to generate a solution:

1. The first thing to do is select the coverage area to be analysed. 2. Then transmitters are placed and the coverage of each one is computed.

These steps are done using Sirenet. Subsequently, the next steps are done using the optimization tool.

3. The coverage data is exported into files with a format that will be understandable by the optimization tool in order to create the virtual scenario successfully.

4. The coverage information is imported and then the starting point of the scenario is calculated, such as the initial levels of CIR and the distances from each pixel to all the transmitters.

5. Once the algorithm calculates de CIR levels in the region selected, results obtained from the program are processed with Matlab where then are compared with those from Sirenet. Fig. 3.3 shows the correspondence of the initial coverage area computed by Sirenet (right window in the picture) with the initial scenario created by the optimization tool (left window). It can be seen that both match perfectly.

Of course, the verification step was only required during the development of the platform. In a normal use of the final version of the tool, this is not required at any point.

Fig. 3.3 Initial correspondence


3.2.3 Algorithm

The algorithm implemented to optimize the network is an adaptation of SA to this particular problem, as it has been explained in Chapter 2. This piece of code runs the optimization. To be able to do it, SA uses the data and parameters created by the Scenario class and saves results in different variables and files that are further used in the application part.

3.2.4 Graphical User Interface

In order to visualise the results in a more friendly way a graphical user interface (GUI) is developed. As explained before, this application makes use of Google Earth to show the results obtained from the algorithm painted over the maps.

This part of the code is the responsible not only of representing the results but also of processing the output data of the algorithm, in order to create the KML and text files that allow recording the results obtained before. As it is plotted in Fig. 3.4 the interface is divided in two parts, the first one is where the GE application is placed and the second one contains the controls to access the different results.

The most important information that comes out of the program, in this case to the operator, is the optimal delay that has been calculated for each transmitter, as is the information required to adjust the time instant of emitting signals. Given this, the first idea was to introduce this data into Sirenet and observe the optimization in terms of coverage in their maps, but after the initial trials it was observed that this task was too tedious and discouraged making multiple assessments, due to the fact that the values of delays must be introduced manually transmitter by transmitter.

Fig. 3.4 GUI interface


For this reason it was finally decided to implement an interface with GE to represent the results obtained when the optimal delays are applied to each transmitter. There are three kinds of results that the tool is programmed to show, however, more results are generated to perform statistics.

3.2.4.1 Initial and Final CIR

This map shows the coverage area, pixels are coloured in blue or red according to the value of CIR. The legend of the colours is:

Red: If the CIR value is lower than 18 dB. Blue: CIR value is higher than 18 dB. Transparent: When the total received power (interference plus constructive

signals) is lower than a certain threshold (-80 dBm, obtained from link budget calculus, as explained in Chapter 1), then it is considered that the pixel cannot improve its coverage, not at least with this type of optimization. Therefore, the pixel is not evaluated by SA and it is not painted.

In Fig.3.5 it is pictured how the application shows these results. In this map is possible to observe, apart from the levels of CIR and the pixels not evaluated, specific data corresponding to each transmitter as it is the name and delay applied, this information appears by clicking over the transmitter icon.

Fig. 3.5 Example of CIR results


3.2.4.2 Population Density

The population density option in the list represents the population per km2 distributed in the target area to be analysed, taking into account that each pixel has an area of 1 km2. It is assumed that the population density corresponds to the total number of inhabitants living under the area of a pixel.

The colour will be different depending on the quantity, the possibilities are detailed in Table 3.3, starting upon the yellow pixels, representing zero population density, up to cyan that represents population densities higher than five hundred inhabitants. Fig. 3.6 represents an example of how the application pictures the population density distribution in the target area. More details on how this layer was created are given in Section 4.3.1.

Table 3.3 Legend of population density distribution

Inhabitants / km2

0 x<50 50<x<200 200<x<350 350<x<500 x>500

Fig. 3.6 Example of population results


3.2.4.3 Initial and Final Coverage of each transmitter

For every transmitter two maps of coverage can be shown, one corresponds to the initial coverage, and another represents the same information after the optimization (Fig. 3.7). Each map plots in blue all pixels to which the transmitter sends a signal that contributes constructively in the receiver at that pixel.

3.2.4.4 Initial and Final CIR by population

This result is a combination of the population density map mixed with the results of the values of initial and final CIR. In this case the map paints the pixels in which the CIR is higher than 18 dB, but the colour is the one corresponding to the population density following the previously defined legend in Table 3.3. This kind of result allows quantifying the population affected by the optimization of coverage as it is appreciated in Fig. 3.8.

Fig. 3.7 Example of transmitter coverage results

Fig. 3.8 Example of CIR by population results


3.3 Code optimization

It is required an optimized code in which the simulation time is as low as possible. By the way, there is no need to mention that SA parameters must be properly adjusted so that the simulations are not unnecessarily long (see Section 2.5). Usually, the program execution spends a lot of time in a small part of the code; this is the so called rule of 90-10, where 90% of the execution time is spent in the 10% of the code that usually contains loops.

Given this, doing some changes in the code, it is possible to reduce the execution time dramatically, like simplifying algebraic instructions, using threads which parallelize the execution or doing loop jamming (put together two or more loops). In this case, the options that have been applied are the use of threads and loop jamming, also called loop fusion, is a kind of optimization that replaces multiples loops with a single one. For example Table. 3.4 represents a code in which are needed two loops to assign values to variables, but applying loop jamming the same code turns into the one depicted on Table 3.5, where the same assignation of values is done but in this case with only one loop.

Also, it is taken into account that the computer in which the program runs is an Intel Xeon CPU X3320 @ 2.5GHz with 4 GB of RAM memory. For example, in optimization involving 25 transmitters in a scenario of 9600 km2 the simulation finishes in 19 hours 40 minutes, taking into account that mainly all the pixels are being analysed. In any case a pixel does not fulfil the requirements of minimum power is then deleted to speed up the optimization.

Table 3.4 Example of code Table 3.5 Example of loop jamming

int i, a[100], b[100]; for (i = 0; i < 100; i++) { a[i] = 1; b[i] = 2; }

int i, a[100], b[100]; for (i = 0; i < 100; i++) a[i] = 1; for (i = 0; i < 100; i++) b[i] = 2;

CHAPTER 4. RESULTS: OPTIMIZATION OF REALISTICS SCENARIOS 35

CHAPTER 4. RESULTS: OPTIMIZATION OF REALISTICS SCENARIOS

During the previous chapters the problem and the method to solve it were presented, as well as the software tool developed to calculate and show results. Due to the high number of transmitters and pixels the area has, it is not possible to run a simulation taking into account the whole Catalan territory. In this sense, three different, independent scenarios have been defined. This chapter explains the definition of these pieces of territory, the initial snap-shot of interferences (CIR levels) and results from both approaches to compute the cost function mentioned previously (Section 2.4.4).

4.1 Definition of the scenarios

The scenarios have been defined considering several geographic features of Catalonia. As the whole area is very varied, demographically and geographically, it is interesting to choose three very well differentiated territories.

Catalonia presents a very well defined geographic diversity, in a relatively reduced area, about 32.000 km2 with a seaboard of almost 580 km. Nowadays, there are in Catalonia 946 municipalities, from which 28 do not exceed the number of 100 of inhabitants and 21 have more than 50.000 inhabitants. Nevertheless, almost 70% of the population live on the 45 most populated areas (those which are over 20.000 inhabitants).

Along this territory different kinds of relief can be found, the big ones are the Pirinees and the Pre-pirinees; limiting in the north with the Pre-pirinees there is the Central Depression, which is the Catalan sector referring to the Ebro’s depression. Finally, there are the littoral mountain ranges and the Transversal ones.

4.1.1 Tarragona scenario

The first test area defined belongs to the Pre-littoral and littoral mountain ranges situated in the south part of Catalonia. In this part of the territory is where Tarragona is located. This area is quite high populated in a heterogeneous distribution, being mostly on the seaboard part.

On the Tarragona region, a total of 14 transmitters were placed and the desired test region covers 4050 km2. The coordinates and the matrix size are:

N=4596010 W=333350 Matrix size: 50 x 81 pixels

The Table 4.1 lists the analysed transmitters and Fig.4.1 represents in the map the coverage area for this region.


Fig. 4.1 Coverage area in Tarragona

4.1.2 Lleida scenario

The following scenario chosen to simulate belongs to the Central Depression area, and where the province of Lleida is located. This fraction of territory is the less populated and with the less population density of Catalonia, the unique municipality that exceeds the 20.000 inhabitants is the capital Lleida, and concentrates over the 30% of the total population. The piece of Lleida chosen for the simulations is quite plain and covers 6497 km2.

The simulation proposed is with 13 transmitters listed in Table 4.2. Note that these are not all the transmitters placed on the zone, just a part were selected to run their optimization. The upper left corner in UTM coordinates is:

N=4637770 W=265030 Matrix sixe: 73 x 89 pixels

Note that the pixel size remains the same as in the previous analysis, 1 km2. Fig. 4.2 represents the coverage area to analyse in this region.

Table 4.1 Analysed transmitters in Tarragona

Name Coord_x Coord_y H

MARMELLAR 377845 4578365 17

MONTAGUT_QUEROL 368189 4585311 15

MONTBLANC 351565 4579235 24

REUS 341460 4557949 10

SALOU 343246 4549611 24

SANT_PERE_MÀRTIR 424677 4583037 40

SANT_SADURNÍ_D'ANOIA 400413 4584890 16

SITGES 403808 4566217 20

TARRAGONA_LLORITO 355223 4555667 20

TORREDEMBARRA 365496 4557812 28

VENDRELL 375288 4561866 26

VESPELLA_GAIÀ 362545 4563208 18

VILAFRANCA 389289 4579400 17

Vilanova_i_la_Geltru 392924 4564199 35


4.1.3 Barcelona scenario

The last area chosen to be analysed belongs also to the Pre-littoral and littoral mountain ranges, but in this case situated on the central part of the Catalan seaboard. Barcelona, the capital province of Catalonia is located in this area. Therefore, Barcelona province is much more populated than any of the others analysed. The population density rises up to 700,43 inhabitants/km2.

It has a higher number of transmitters, but again, only some of them are optimized. The total analysed area is of 9600 km2. The upper left coordinates of this area are:

N=4646090 W=369670 Matrix size: 80 x 120 pixels.

Table 4.3 lists the transmitters placed on this territory and Fig. 4.3 represents, as in the previous cases, the coverage area of Barcelona.

Table 4.2 Analysed transmitters in Lleida

Name Coord x Coord y h

AGRAMUNT 340934 4624475 27

ALMACELLES 287972 4623637 26

ALMATRET 285319 4575154 12

ALMENAR 298255 4629035 21

ALPICAT 294323 4615529 59

BALAGUER 318327 4629074 18

BALTASSANA 333115 4577075 7

LES BORGES BLANQUES 322964 4598010 13

MEQUINENÇA 273983 4586092 22

MOLLERUSSA 324772 4610892 11

PUIG_DE_VALL 304465 4607765 22

PUNTA_CURULL 329035 4580105 19

ULLDEMOLINS 320395 4577825 14

Fig. 4.2 Coverage area in Lleida


Table 4.3 Analysed transmitters in Barcelona

4.2 Results for geographical optimization

Once chosen all the transmitters and the area to be analysed, it is obtained the map of the CIR of the territory taking into account that all the transmitters’ delays are set to zero. This is the starting point for the simulator as it has already calculated how much pixels perceive a value higher than the minimum required CIR, and therefore the number of pixels that must be improved by SA. In this case the cost function is computed as the number of pixels KO in a given territory.

The simulations executed in the previous scenarios follow the same radio frequency characteristics, in terms of transmitter and receiver adjustments. This includes the type of emitting and receiving antennas, the height of each, the propagation model and the thresholds established, these last are based on the link budget explained on Chapter 1. The following Table 4.4 summarizes the principal radio characteristics.

Name Coord x Coord y h

AIGUAFREDA 436707 4624181 5

BAIX_LLOBREGAT 415075 4577735 22

BELLATERRA_II 424195 4595945 29

BOIXADORS 387745 4622915 26

CASTELLDEFELS 414178 4570272 25

SANT_CELONI 455725 4622945 22

COLLSEROLA_BARCELONA 426071 4585671 100

SANTA_COLOMA_GRAMANET 434670 4590033 20

COLLSUSPINA 433522 4630263 46

FARELL 427608 4612332 3

GRANOLLERS 441467 4606786 15

IGUALADA 381505 4601435 35

SANT_ANDREU_LLAVANERES 457367 4602795 25

MANRESA 402697 4620680 14

CABRILS‐MATARO 448705 4597505 8

MOLINS_DE_REI 415405 4585955 22

MONTCADA 433655 4593486 28

MONTSERRAT 401425 4606805 21

SABADELL 424750 4601631 20

SANT_SADURNI_D'ANOIA 400413 4584890 16

SALLENT 408835 4630745 20

SANTA_MARIA_D'OLO 420310 4637405 25

TERRASSA 418145 4602058 20

VALLBONA_D'ANOIA 391817 4599345 9

VIC 438475 4642565 25

Fig. 4.3 Coverage area in Barcelona

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Comparing both snap-shots it is verified that the fact of modifying the delays of the transmitters can lead into an important change on coverage. Initially, most of the littoral mountain region was partly uncovered having several points of low coverage. SA, by adjusting the time instant in which the transmitters are supposed to send the signals, can improve highly the level of self interferences. In order to have an idea of the amount of pixels improved, without any delay adjustment the number of pixels considered OK were 1658 over a total of 4050, this is almost a 40% of coverage on the region, once the results are obtained the number of pixels considered OK rises to 2466 which means a 60% of coverage region, a 20% of improvement on this scenario.

4.2.2 Lleida

The same as in the previous case is done in the region of Lleida, were almost the same number of transmitters is placed but the area is larger. For this reason, it may happen that many points do not receive enough power to exceed the minimum required (remember that this number is computed as the total contribution of power, the serving base stations but also the interfering ones), and these points of the map cannot be improved by changing the transmitter’s delay.

The next figure extracted from the simulator, represents the map with the initial CIR levels (Fig. 4.7) and the following represents the final ones (Fig. 4.8).

Once more, the coverage of the region is improved once SA applied the changes on the delays. The total number of pixels that receive power levels higher than the minimum required one are 2873, which means over a 45% of the total area. Initially, this area is covered on a 22%.

Fig. 4.7 Initial CIR for Lleida region


Fig. 4.8 Final CIR for Lleida region

The important problem found in this area is that the most important city (Lleida) is almost entirely uncovered due to the strong self interference that suffers. This means that over 30% of the population remains uncovered despite the changes done. This last idea is one of the clue points on the discussion of the cost function calculation approach, explained in this chapter. It is a matter of fact that by introducing a new transmitter with a high level of transmitted power this problem could be initially solved, but in contrast it would generate more interference in other areas, and one of the objectives is to optimize the use of the existing infrastructure.

4.2.3 Barcelona

The last area to be analysed is Barcelona. As it was highlighted on the previous section, this area owns much more transmitters than the other two; the test area, however is larger as well. The fact of placing more transmitters can lead to more interferences, and here, the lack of coverage can be an important problem as in the whole province live over 74% of the total Catalan population.

After observing the results (Fig 4.9 and 4.10), the fact of placing so much transmitters leads to strong interferences and thus, a lack of coverage on areas which initially should be covered. For example, what happens in the region of Granollers, as it can be appreciated on the first map. In this case SA enhances most of the pixels belonging to the sea, which is not profitable at all. However, the improvement in number of pixels is significant; at the initial study only a 20% of the total pixels were considered to have a good signal quality, and once SA modifies the delays this percentage rises up to 37%. Once more, as on the previous case, it is necessary to add a variable to be able to distinguish the nature of the pixel or rather, its population density.


Fig. 4.9 Initial CIR for Barcelona region

Fig. 4.10 Final CIR for Barcelona region

4.3 Optimizing population density

The optimization procedure initially proposed for the algorithm shows a good performance and works properly in the sense of improving as much pixels as possible under a given area. However, at this point the program is not provided of intelligence to guess which points of the map are more problematic in the sense that are more overpopulated. The results already shown are positive, but when considering the total amount of population uncovered the three areas must be improved even more.

For this reason, it is proposed a modification in the algorithm. Now the cost function defined in Chapter 2 is used. Hence, once a pixel is considered not to be covered, instead of taking into account just the pixel itself on the cost function, it will be considered the amount of population that collects. By adjusting the cost function adding


a different weight on each pixel, the optimization focuses on those areas in which more users have coverage problems.

This section is focused on the comparison of both approaches, the geographical based optimization and the population based one. For this purpose, two simulations are done in each region, and results are shown in terms of population density.

4.3.1 Population distribution

As the population distribution layer was not available during the realization of the project, it was required to generate it manually from the data extracted in [15]. The procedure to introduce this data into the program is:

1. First knowing the central coordinates of the town and the area, guess how many pixels are occupied by this town.

2. On each pixel that belongs to this town, it is introduced the population density (inhabitants/km2). This is done straightforward because one pixel has a size of 1 km x 1 km size.

3. The document with the exact data can be found in Appendix B.

The following figures show the final population distribution on the three scenarios (See Fig. 4.11, 4.12 and 4.13). The correspondence of each colour was already detailed in Table 3.3. As it can be appreciated on the images extracted from the simulator, the population distribution is only an approximation of the real one. Nevertheless this is accurate enough for the purposes of this project, and of course it is much better than a null consideration of this data.

Fig. 4.11 Population density for Tarragona


Fig. 4.12 Population density for Lleida

Fig. 4.13 Population density for Barcelona

4.3.2 Discussion over Tarragona

This first area is quite populated mostly on the seaboard; however, when the transmitters are not adjusted the areas provided with more inhabitants suffer from strong interferences, having as a result poor signal quality on the most important cities such as Tarragona and Reus (Fig. 4.14).


Fig. 4.14 Initial population covered in Tarragona

The optimization applied with both approaches is shown in Fig. 4.15 and 4.16. When SA searches for a solution minimizing the total population uncovered, the program enhances the result and adjusts the delays of the transmitters so that areas with more inhabitants are covered. It is noticeable how if population data is taken into account, then the sea is left out of the optimization objectives.

Comparing the results, it can be observed that when just the geographical area is optimized, then the final number of covered pixels is higher. Nevertheless, the important difference appears when analysing the total served people, when SA seeks the minimum number of pixels, the population covered is 569564 inhabitants, but if population is actively considered in the optimization then the algorithm reaches a total of 638065 inhabitants. This means that indeed, it is not so important to have as much pixels as possible, but those that are selected are the most problematic ones, in the sense that many people live in there.

Fig. 4.15 Geographical based optimization in Tarragona


Fig. 4.16 Population based optimization in Tarragona

4.3.3 Discussion over Lleida

In this region there are not many high populated centers, so the improvement should be focused on the largest city, Lleida. With the initial configuration of the transmitters (no delay) the most important city suffers from strong interferences and the quality of the received signal is not high enough, as it is shown in Fig 4.17. Fig. 4.18 and 4.19 correspond to the results considering both approaches, being the first geographical based optimization and the second population based optimization.

When applying SA with the population based optimization on this scenario, the final result is that the transmitters’ delays are modified in order to improve the more populated area (Lleida) rather than those which are not so problematic.

Fig. 4.17 Initial population covered in Lleida


Fig. 4.18 Geographic based optimization in Lleida

Fig. 4.19 Population based optimization in Lleida

The fact of considering the number of inhabitants when analysing the terrain means that one solution that benefits the most populated areas can be detrimental to other areas not so highly populated. When changing the delays, a given small village initially covered can then be worsened. On the geographical based optimization, this does not happen as every pixel is considered equally. So, for instance, the first approach proposes a solution that increases the coverage on the area between Balaguer and Mollerussa. On the contrary, with the second approach, the most enhanced area is Lleida, and the region between Mollerusa and Balaguer remains out of coverage, just as it was before any type of optimization.


4.3.4 Discussion over Barcelona

The last scenario to be analysed in this discussion is the most problematic one in terms of population density. As it was shown in the population distribution map, the Barcelona province owns very large municipalities all of them highly populated. Again, the most crowded zones are located near the seaboard, and as it happened in the Tarragona scenario, when SA applies the optimization it may happen that most of the pixels improved are indeed part of the sea.

Given the previous paragraph, it is important to notice if the second approach manages to improve the most populated areas. Fig. 4.20 shows the population distribution layer of the map, corresponding to the areas in which the CIR is equal or higher than the desired one before any type of optimization is performed. Next, Fig. 4.21 and 4.22 show the results taking into account both optimization approaches, the territory-centric and the population-centric one respectively.

Running SA the coverage obviously increases, however it can be observed that both solutions are totally opposite. The first one leaves the city of Barcelona with several points out of coverage, and this is because of the amount of transmitters that are placed nearby generating strong interference. The second solution, effectively improves the coverage of the metropolitan area, as it is the one more populated, but the interior part remains with strong interference problems. On these cases, the problem may be solved by placing some repeaters on the area with less transmitted power, reducing interferences caused in other zones.

Fig. 4.20 Initial population covered in Barcelona


Fig. 4.21 Geographic based optimization in Barcelona

Fig. 4.22 Population based optimization in Barcelona

4.3.5 Conclusions for the cost function approach

Based on the results extracted from the three different simulations, SA is more selective on the optimization when the procedure considers population data. On the other hand, when the program just analyses the number of pixels, it has no intelligence to know if one pixel is important enough or not, so what happens is that a lot of pixels belonging to the sea, or mountains where no people live improve their connectivity, while other important pixels remain uncovered. By changing the way of calculating the cost function, it may happen that finally a smaller geographical area is improved, but with a higher number of served inhabitants.

Finally, it is important to mention that many pixels on the area cannot be initially improved because the received power is not enough from the beginning, and as it has been already explained this is not a delay adjustment issue, it may be solved by increasing the emitted power or by the installation of new transmitters or gap-fillers (repeaters).

CHAPTER 5. RESULTS: IMPACT OF OTHER VARIABLES OVER THE OPTIMIZATION PROCESS 51

CHAPTER 5. RESULTS: IMPACT OF OTHER VARIABLES OVER THE OPTIMIZATION PROCESS

This chapter aims to study the impact that certain collateral parameters have on the optimization result. In particular, it has been taken into account the variety of types of receivers that can exist in real networks. This has been done by adapting the receivers to the type of service being used and thus considering different types of receivers.

5.1 Receiving antennas

Normally, depending on the type of environment receiving antennas are different because it is intended to choose the radiation pattern that gives better performance to the system. In general, most coverage studies concerning digital terrestrial TV have been aimed towards fixed reception using roof-level directional receiving antennas. However the possibility of outdoor or indoor reception on a portable receiver with an in-built or set-top receiving antenna might offer substantial additional user benefits. This situation would not be represented by the general case with directive antennas, since portable equipment usually receive ominidirectionally. In this project up to the moment, all the simulations were done with omnidirectional antennas, which could be a good choice if it is always considered the worst case scenario.

Portable reception will take place under a great variety of conditions e.g. outdoor, indoor, ground-floor or higher-floors and with simple antennas. The conditions for portable reception differ from fixed reception in the:

• absence of receiving antenna gain and directivity;

• reduced feeder loss;

• generally lower reception height;

• building penetration loss in the case of indoor reception.

The main objective of this section is to compare and discuss the different ways in which the receiver point can be set.

5.1.1 Definition of the scenarios

The scenarios that have been simulated are very similar to the ones presented in the previous chapter with some changes in the receiver point, but the geographical situation and the population remain equal.

In this case two different receiver points are defined, both of them adapted to the service that it is going to be received. For instance, when simulating a portable scenario the principal radio characteristics are the ones listed in Table 5.1.


Table 5.1 Principal radio characteristics of portable scenario

Receiver

Type 1: catches the first echo

Antenna Omnidirectional

Height 1.5 m

Prmin -115 dBW

CIRmin 18 dB

Pixel resolution 1 km2

Transmitter ERP 11.25 dBW

Antenna Omnidirectional

Propagation model REC 1546 ITU-R

Note that in this case the height of the receiver antenna is set to 1.5 m as it is recommended on the implementation guidelines given by the European Broadcasting Union in [4]. In this same document it is also mentioned that while the propagation model is correct enough to apply in a portable scenario, some extra losses should be added due to the indoor penetration of the signals, which is not considered in the REC 1546 ITU-R. Moreover, when considering a fixed scenario, radio characteristics are the ones shown in Table 5.2.

The sensitivity set for both receivers is the same, but for sure that this could change depending on the device.

Table 5.2 Principal radio characteristics of fixed scenario

Receiver

Type 1: catches the first echo

Antenna Directional (Yagi)

Gain 14 dB

Height 10 m

Prmin -115 dBW

CIRmin 18 dB

Pixel resolution 1 km2

Transmitter ERP 11.25 dBW

Antenna Omni directional

Propagation model REC 1546 ITU-R


5.1.2 Yagi antennas: Fixed environment

As it is previously mentioned, when considering a fixed environment the most common situation is to place directive antennas in the roof in order to gain altitude aiming at having line of sight with the TX. The most common antenna used in this case is the Yagi-Uda, commonly known as Yagi. This is a directive antenna which means that has maximum gain in one direction and atenuates the transmitted/received signal in other angles, this is profitable to attenuate interfering signals coming from other transmitters, as it can be appreciated in Fig. 5.1, which shows a conventional horizontal radiation pattern of a Yagi antenna.

In this project the radiation pattern of the antenna was not introduced manually in the simulator. Instead, using the software tool Sirenet, the coverage of each transmitter was computed considering a Yagi of 14 dB of gain, meaning that the data extracted from Sirenet (the received power in each point of the map) considers this information within the received power.

Fig. 5.1 Example of a conventional Yagi horizontal radiation pattern


The initial coverage taking into account this fixed environment is shown in the next collection of figures. (See Fig. 5.2, 5.3 and 5.4)

Fig. 5.2 Initial coverage in Tarragona in fixed scenario with receiver type one

Fig. 5.3 Initial coverage in Lleida in fixed scenario with receiver type one


Fig. 5.4 Initial coverage in Barcelona in fixed scenario with receiver type one

The received power enhances in the entire area, and for this reason the initial level of signal is better having Yagi antennas in the roof than omnidirectional, based on the results shown in Chapter 4, Fig. 4.16, 4.19 and 4.22. The reason of this difference on the initial snap-shot is that being directive, all the interfering echoes does not have much contribution. By the way, the number of pixels that exceed the minimum level of received power increases, which means that initially there are more pixels capable of being improved, the region in which this is most noticeable is in Lleida.

The optimization done in this three scenarios follow the same characteristics than the ones in the previous chapters, being the cost function computed as a sum of the population uncovered. Results are shown in Fig. 5.5, 5.6 and 5.7.

Fig. 5.5 Final CIR in Tarragona in fixed scenario with receiver type one


Fig. 5.6 Final CIR in Lleida in fixed scenario with receiver type one

Fig. 5.7 Final CIR in Barcelona in fixed scenario with receiver type one

Once the optimization is done in all the scenarios defined, the coverage increases in all cases, as it was already predictable. For instance, the area defined in Tarragona is the one that improves the most as the final coverage raises a 18% from the initial one, while Barcelona improves an 8,3% and Lleida is the last one with only a few tenths over 2%, which is not very different from the results obtained with the omnidirectional antennas on Chapter 4.

The following graphs (Fig. 5.8 and 5.9) show the comparison between both cases, the one receiving with omnidirectional antennas in the roof (results in Chapter 4) and the last case with Yagi antennas. These results express in a numerical point of view the improvement obtained by changing the antennas. This is mainly reflected in an important rise in the number of pixels initially covered in the area of Lleida, which compared with the results obtained on Chapter 4, the number of pixels covered rises in almost a 13%. Meanwhile, the other two scenarios remain almost equal.


To conclude this section, the fact of placing Yagi antennas for fixed reception is better to improve the total number of pixels in which the minimum power required is exceeded. The interference is well attenuated as the reduction in gain from the primary lobe to the secondaries, but at the same time, the secondary lobes also attenuate other echoes that could improve the signal quality.

Fig. 5.8 Initial percentage of pixels covered

Fig. 5.9 Final percentage of pixels covered

05

101520253035404550

Barcelona Lleida Tarragona

Pixels covered (%)

Omni

Yagi

0

10

20

30

40

50

60

70


Pixels covered (%)

Omni

Yagi


5.1.3 Omnidirectional antennas: Mobile environment

Both DVB-T and DVB-T2 mention the possibility of having a portable environment, and the capability of giving TV service to users with the existing infrastructure for the fixed scenario. In this project, it has been studied the possibility of giving mobile coverage considering the same number of transmitters placed for the simulations in the fixed environment. Below these lines, there are pictured the initial level of signals that result having omnidirectional receivers at 1.5 m high (See Fig. 5.10, 5.11 and 5.12).

Fig. 5.10 Initial coverage in Tarragona in portable scenario with receiver type one

Fig. 5.11 Initial coverage in Lleida in portable scenario with receiver type one


Fig. 5.12 Initial coverage in Barcelona in portable scenario with receiver type one

Coverage regarding portable scenarios is much poorer than in the other cases. This lack of received power is due to the height of the receiver point and the lack of directivity. In fact, the optimization, as it can be seen in Fig. 5.13, 5.14, and 5.15 is not very noticeable, because the initial number of pixels to be improved is very poor.

Fig. 5.13 Final coverage in Tarragona in portable scenario with receiver type one


Fig. 5.14 Final coverage in Lleida in portable scenario with receiver type one

Fig. 5.15 Final coverage in Barcelona in portable scenario with receiver type one


The algorithm optimizes as much as possible, but it is clear that in this case it is not enough with the modification of internal delays. The scenario which is improved the most is Tarragona with an increase of about a 17% which makes a final coverage of the 94% (over the pixels that accomplish the receiving power constraint).

The big problem that arises in this portable environment is that, with the existing infrastructure it turns difficult to assure a mobile coverage in all the three scenarios defined. As a matter of comparison, it is interesting to study the difference between the initial percentage of pixels in a scenario with Yagi antennas, intended to fixed reception, and a scenario with omnidirectional antennas just for portable reception, this is shown in Fig.5.16.

This graph represents the percentage of pixels among those that accomplish the minimum received power constraint. The difference in coverage regarding both scenarios is quite huge, and this is due to the antenna’s height but also because the Yagi antenna receives with an added gain.

However, in a real communication system, it could be possible that any user inside a city uses either a fixed or a portable device, with the coverage obtained this could be initially possible, as the main city centres are covered. In general, portable users can be located elsewhere, so it is important to assure at least coverage in the main roads, in the case users want to watch TV while doing a car journey. To make this possible is necessary to study the main roads and the number of users that are going to pass through, its population density.

Fig. 5.16 Percentage of pixels covered with at least the minimum power

0

20

40

60

80

100


Pixels covered (%)

Omni

Yagi


The region chosen to apply changes in order to improve the portable coverage is Lleida, the reason is that it is the poorest covered area, and it has plenty of space to add new transmitters. The idea is to add at least three transmitters to enhance the coverage in the main roads listed in Table 5.3.

The placement of the transmitters intends to minimize the cause of interferences in other transmitters already situated for fixed environments. Regarding the receivers layer, one pixel can hold both types of receiver, so the simulator takes into account any of the two possibilities. From this point it is possible to guess how much pixels obtain a portable service from these antennas.

Based upon several simulations done in Sirenet the transmitters were finally situated in a given position and with the characteristics listed in Table 5.4.The transmitted power is higher in order to be sure that enough useful signal arrives to the portable receivers.

Table 5.3 Main roads in Lleida region

Roads Beginning End Length IMD Vehicles/day Vehicles/km

Coord X Coord Y Coord X Coord Y

C‐13 302200 4610300 317825 4629050 25 8960 358,40

N‐240 296550 4615825 322300 4598950 26 10784 414,77

N‐230 298025 4630275 302200 4610300 20 4710 235,50

A‐2 302200 4610300 324750 4611025 16 32564 2035,25

AP‐2 322300 4598950 299100 4603900 20 12728 636,40

AP‐2 313625 4596700 346450 4582250 30 14001 466,71

AP‐2 279550 4593300 299100 4603900 23 12728 553,39

A‐2 287125 4623250 296550 4615825 17 32564 1915,53

C‐12 291350 4582375 294750 4567450 15 5.728 381,89

C‐12 291350 4582375 299100 4603900 23 8509 369,96

A‐2 324750 4611025 345250 4612600 19 31000 1631,58

Table 5.4 Characteristics of the transmitters added

Transmitter Coord X Coord Y PARA H

Omni_1 293680 4591840 20 5

Omni_2 338720 4610880 20 5

Omni_3 282800 4612960 20 5


Fig. 5.17 and 5.18 depicts the initial and final coverage obtained by these three transmitters, considering all the transmitters of the scenario on the simulation. Pixels coloured in blue correspond to those in which the useful signal comes from one of these transmitters and the CIR value is higher than 18dB’s. Thus, those points in were good coverage is available.

Thanks to the placement of these transmitters, the mobile coverage has increased. However, this can lead to strong interferences on the fixed network, and worsen the areas already optimized by the fact of adding new transmitters. The final coverage of the scenario is pictured on Fig. 5.19.

If this figure is compared with Fig. 5.6 (results having Yagi receiver antennas and only 13 transmitters over the scenario) the coverage has worsened in the most populated region, however, there’s more coverage along the roads (lines coloured in cyan). This means that due to the fact of placing a new transmitter the whole signal level of the scenario destabilizes, and needs more optimization. Indeed, in this scenario it is necessary to make more adjustments in terms of power and height of the antennas.

Fig. 5.19 Final coverage according to population

Fig. 5.18 Final coverage of omnidireccional TXs Fig. 5.17 Initial coverage of omnidireccional TXs

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5.2.2 Yagi antenna: Fixed environment

The main goal of changing the type of receivers is to study the improvement in those pixels that still remain under the minimum value of CIR fixed at 18 dB.

In this section the initial CIR is not a matter of study as the important point is the total optimization. What it is important to mention regarding this issue, is that while the initial CIR changes due to the nature of the receivers, the number of pixels that exceed the minimum power remains equal than when using receivers type one.

The next figures (Fig. 5.21, 5.22 and 5.23) show the final coverage obtained taking into account the new configuration of receivers in Tarragona, Barcelona and Lleida, respectively. Note that these images can be compared with the ones obtained for a Yagi antenna, but with type one receiver (see section 4.1.2).

Fig. 5.21 Final coverage in Tarragona in fixed scenario with receiver type two

Fig. 5.22 Final coverage in Lleida in fixed scenario with receiver type two


Fig. 5.23 Final coverage in Barcelona in fixed scenario with receiver type two

Following the results obtained, the optimization done in the same scenario with different receivers is worse than the one obtained in Fig. 5.5, 5.6 and 5.7. Again, Tarragona is the area in which the coverage is improved the most, higher than a 9%. But, the improvement percentage is just the half obtained with receivers type one. Meanwhile Barcelona and Lleida improve a few tenths over a 4%. Not only the improvement worsens from the results obtained before, the initial CIR is worse also. Fig. 5.24 shows in a numerical way the impact that has the fact of changing the receivers. In all the three cases the improvement is worse and also the final and initial CIR.

When a receiver type two catches one transmitter this will be the one which contributes with more power, so when the scenario is optimized, in all the cases the receiver will remain having the same serving transmitter, as the received power remains equal. When a receiver type one catches the transmitter that first arrives to it, means that when changing the delays, this serving transmitter can be easily substituted by another that, once changed the delays, now arrives earlier. This simple change in the receivers’ performance leads into less freedom when trying to minimize the total population uncovered. The only issue to be optimized in the case of an environment with a type two receiver, is the management of the interferences, changing the echoes, some that were out of the GI can now be a positive contribution.

Fig. 5.24 Comparison of final percentage of pixels covered in fixed scenario

0

10

20

30

40

50

60

70


Pixels covered (%)

Receiver 1

Receiver 2


5.2.3 Omnidireccional antenna: Mobile environment

As explained before, the portable scenario has a lower number of pixels to be improved, for this reason the percentage of pixels covered obtained is lower, regardless the type of receiver. Again the number of pixels that exceed the sensitivity are the same than in the previous case, however the initial CIR is slightly lower. The following figures (Fig. 5.25, 5.26 and 5.27) show the result of the optimization in the three defined scenarios.

Fig. 5.25 Final coverage in Tarragona in portable scenario with receiver type two

Fig. 5.26 Final coverage in Lleida in portable scenario with receiver type two

Fig. 5.27 Final coverage in Barcelona in portable scenario with receiver type two


The general behaviour in the three scenarios is maintained, as the optimization is quite poor due to the lack of pixels having better received power. Once again, the final coverage for this type of receivers is worse than with receivers type one (Fig. 5.13, 5.14 and 5.15). For instance, in the region of Barcelona, no more pixels are enhanced; the improvement in terms of quantity of pixels is zero, but the physical location of these points changes as it is taken into account the total population of each spot. Nevertheless, this does not happen in all the scenarios, in Tarragona, the most improved area, the number of pixels raises a 2% and in Lleida the improvement is only of 1%. Comparing then the values obtained in both cases, the results are shown in Fig. 5.28.

In almost all the cases the total number of pixels is higher when taking into account type one receivers. Once more, the reason has to do with the freedom when changing the serving transmitter.

To sum up, the fact of using an SFN technology gives the possibility of managing the delays in order to minimize the number of echoes that remain outside the GI. If this possibility is followed by the constraint that always the serving transmitter is going to be the one that gives more power, then the probability of having more echoes outside the time interval will increase, as it is possible to have pre-echoes as well. This increases the interference power, and consequently de CIR reduces below the thresholds.

Fig. 5.28 Comparison of final percentage of pixels covered in portable scenario

0

5

10

15

20


Pixels covered (%)

Receiver 1

Receiver 2

CHAPTER 6. CONCLUSIONS 69

CHAPTER 6. CONCLUSIONS

This project has proposed a technique based on the simulated annealing methaheuristic with the final goal of optimizing existing DVB-T networks by means of reducing the self-interfered areas. The strategy has been validated by means of several software tools that helped to perform the input data for the simulator. Among all the tools used along the development of this project those with more relevance were:

Software optimization tool: fully programmed in the development of the project. Designed to compute the optimal solution based on the input data extracted from the previous tool.

Sirenet: useful to compute the coverage area. Google earth: useful to geo-reference results over a map.

In order to perform this optimization, previous adjustments were required in order to assure that SA was working at its maximum performance. As there are several parameters to adjust in this algorithm, the result of modifying some variables has a direct impact on the quality of the cost function and computational time.

Based upon the multitude of simulations done in this project it is possible to conclude that the interest of this method relies on several facts. First of all, it allows obtaining maximum performance of the existing network, as the static delay adjustment improves the SFN coverage by reducing the self-interfered areas. In general, a lack of coverage can be turned on the idea of installing a new transmitter, however, as it is shown in this project this is not always necessary. This software tool helps the operator to assess the decision of adding new transmitters, if once the optimization is done and with the existing nodes there is no good solution, then this idea can be considered. Once a new transmitter is installed in the network, this solution allows to automatically reconfigure the optimum delays in the target area. Besides, the fact of changing the transmitter’s offsets does not imply any economical effort, so it is an improvement that can be done with no extra investment.

There are several opened lines for further investigation. Levels of interference can be handled with this optimization tool, but the fact of adding more variables to the optimization may lead into better results of coverage improvement. As an example, if a power allocation algorithm is added, the program could be able to control even more the levels of interference. This software tool can be improved to simulate portable scenarios taking into account all the variables that involve this environment, as well as further improvement in the network to assure at least a 90% coverage in all the regions. Another improvement that can be applied to the program, is the fact of the coexistence of several receivers.

To conclude this project, it is important to mention that it was developed inside the FURIA project a national AVANZA project with the participation of the most representative companies and research teams of the audiovisual technologies at national level.

CHAPTER 7. REFERENCES 71

CHAPTER 7. REFERENCES

[1] M. García-Lozano, S. Ruiz-Boqué, F. Minerva, Static Delays Optimization to

Reduce Self-Interference in DVB-T Networks, 2010.

[2] FURIA Project. Official webpage of FURIA council. Available in

www.furiapse.com

[3] L. Nuaymi, WiMAX: Technology for broadband wireless access, John Wiley &

Sons, France, 2007.

[4] EBU-UER (ETSI) Technical report v.1.3.1, Digital Video Broadcasting (DVB);

Implementation guidelines for DVB terrestrial services; Transmission aspects,

2008-10.

[5] M. Duque-Antón, D. Kunz, and B. Rüber, “Channel Assignment for Cellular

Radio Using Simulated Annealing,” IEEE Transactions on Vehicular

Technology, vol. 42, no. 1, pp. 14–21, Feb. 1993.

[6] D. Beckmann and U. Killat, “Frequency Planning with Respect to Interference

Minimization in Cellular Radio Networks,” COST 259, Vienna (Austria), Tech.

Rep. available as TD(99)032, Apr. 1999.

[7] S. Salcedo-Sanz, R. Santiago-Mozos, and C. Bousoño-Calzón, “A Hybrid

Hopfiled Network- Simulated Annealing Approach for Frequency Assignment in

Satellite Communications Systems”, IEEE Transactions on Systems Man and

Cybernetics Part B. Cybernetics, vol. 34, no. 2, pp. 1108–1116, Apr. 2004.

[8] Ligeti and J. Zander, “Minimal Cost Coverage Planning for Single Frequency

Networks,” IEEE Transactions on Broadcasting, vol. 45, no. 1, pp. 78–87, Mar.

1999.

[9] M. Kamenetsky and M. Unbehaun, “Coverage Planning for Outdoor Wireless

LAN Systems,” in Proc. of IEEE International Zurich Seminar on Broadband

Communications, Zurich (Switzerland), Feb. 19–21, 2002.

[10] S. Menon and S. Gupta, “Assigning Cells to Swithces in Cellular Netowrks by

Incorporating a Pricing Mechanism into Simulated Annealing,” IEEE

Transactions on Systems Man and Cybernetics Part B. Cybernetics, vol. 34, no.

1, pp. 558–565, Feb. 2004.

[11] J. Harmatos, A. Sentéis, and I. Gódor, “Planning of Tree- Topology UMTS

Terrestrial Access Networks,” in Proc. of IEEE International Symposium on

Personal, Indoor and Mobile Radio Communications (PIMRC 2000), London

(England), Sep. 18–21, 2000.


[12] E. Aarts, J. Korst, Simulated Annealing and Boltzman Machines, John Wiley &

Sons, Chicester, 1989.

[13] P.J.M van Laarhoven, E.H.L Aarts, Simulated Annealing: Theory and

Applications, D.Reidel, Dordrecht, 1987.

[14] P. Moreno, G. Huecas, J. Sánchez, A. García (2007) Metaheuristica de

optimización combinatoria. Tecnología y desarrollo, volumen V, 25pp.

[15] Población. gencat datos. Generalitat de Catalunya. Available in http://www.gencat.cat/gencat_dades/cas/poblacio.htm

APPENDICES I

APPENDICES

A) Link budget for TDT systems

Band Band V

Receiving Condition Fixed antenna (outdoor 10 m

a.g.l)

Frequency f [MHz] 800

Boltzmann Constant k [J∙K‐1] 1,38E‐23

Bandwidth B [Hz] 7,60E+06

Temperature T0 [K] 290

Thermal noise power Pn,th [dBW] ‐135,2

Receiver noise figure F [dB] 7

Total noise power Pn [dBW] ‐128,2

Minimum carrier to noise ratio required by system

CNR [dB] 2 8 14 18 26

Minimum receiver signal input power Ps min [dBW]‐

126,2

‐120,2

‐114,2

‐110,2

‐102,2

Receiver input impedance Zi [Ω] 75

Minimum equivalent receiver input voltage Us min [dBµV]

13 19 25 29 37

Feeder loss Lf [dB] 5

Wave length λ [m] 0,4

Effective isotropic antenna aperture (sup equival isotrop)

Aa,iso [dB(m2)]

‐19,5

Antenna gain relative to half wave dipole Ga [dBd] 12

Antenna gain relative to isotropic Ga [dBi] 14,15

Effective antenna aperture (superficie equivalente)

Aa [dB(m2)] ‐5,4

Minimum power flux density at receiving place

φmin [dB(W/m2)]

‐115,8

‐109,8

‐103,8

‐99,8 ‐91,8

Minimum equivalent field strength at receiving place

Emin [dB(µV/m)]

30 36 42 46 54

Margin for man made noise Mman [dB] 0

Pérdida debida a no estar a 10 m sobre suelo y exterior

Lh [dB] 0

Building penetration loss Lb [dB] 0

II Planning optimization software tool for DVB-T and DVB-T2

Location probability: 70 %

Función k(p) kp 0,52

Desviación típica componente log‐normal σ [dB] 5,5

Location correction factor (Shadowing margin)

MF [dB] 2,9

Median power flux density at 10 m above ground level

φmed [dB(W/m2)]

‐112,9

‐106,9

‐100,9

‐96,9

‐88,9

Median equivalent field strength at 10 m a.g.l.

Emed [dB(µV/m)]

33 39 45 49 57

Location probability: 95 %

Función k(p) kp 1,64

Desviación típica componente log‐normal σ [dB] 5,5

Location correction factor (Shadowing margin)

MF [dB] 9,0

Median power flux density at 10 m above ground level

φmed [dB(W/m2)]

‐106,8

‐100,8

‐94,8 ‐

90,8 ‐

82,8

Median equivalent field strength at 10 m a.g.l.

Emed [dB(µV/m)]

39 45 51 55 63

Band Frequency (MHz)

Man Made Noise Margin (dB)

Extra Loss for not

being 10 m a.g.l. (dB)

Receiving Condition

Antenna Gain (dB)

Antenna Gain Class A&B (dB)

Building loss (dB)

Feeder Loss (dB)

Band I 65 6 10

Fixed antenna

(outdoor 10 m a.g.l)

3 ‐2,2 8 1

Band III 200 1 10 Portable Outdoor (Class A)

7 ‐2,2 8 2

Band IV 500 0 12 Portable

Indoor (Class B)

10 0 7 3

Band V 800 0 12 12 0 7 5

APPENDICES III

B) Population distribution in Catalonia

Municipality Area Population

UTM‐X UTM‐Y (km2) 2009 Density

Abella de la Conca 342450.00 4669650.00 78 183 2

Abrera 408471.00 4596983.00 20 11521 576

Àger 314700.00 4652600.00 161 575 4

Agramunt 342100.00 4628075.00 80 5608 70

Aguilar de Segarra 386250.00 4621900.00 43 257 6

Agullana 487450.00 4693750.00 28 812 29

Aiguafreda 438028.00 4624410.00 8 2464 308

Aiguamúrcia 363150.00 4578725.00 73 906 12

Aiguaviva 480450.00 4643100.00 14 714 51

Aitona 287900.00 4596950.00 67 2398 36

Alamús, els 311750.00 4609800.00 21 740 35

Alàs i Cerc 377225.00 4690100.00 58 391 7

Albagés, l' 311250.00 4591250.00 26 476 18

Albanyà 477125.00 4683900.00 94 152 2

Albatàrrec 300450.00 4605350.00 10 1872 187

Albesa 305600.00 4625100.00 38 1613 42

Albi, l' 327750.00 4587975.00 33 836 25

Albinyana 373400.00 4567350.00 19 2275 120

Albiol, l' 340050.00 4568675.00 20 405 20

Albons 506700.00 4661850.00 11 687 62

Alcanar 286800.00 4491400.00 47 10570 225

Alcanó 301200.00 4595100.00 21 239 11

Alcarràs 293725.00 4604525.00 114 7776 68

Alcoletge 308075.00 4613400.00 17 2677 157

Alcover 346875.00 4569725.00 46 5100 111

Aldea, l' 298500.00 4513250.00 35 4063 116

Aldover 289450.00 4528625.00 20 984 49

Aleixar, l' 336300.00 4563150.00 26 898 35

Alella 441226.00 4593975.00 10 9397 940

Alfara de Carles 281025.00 4528200.00 64 400 6

Alfarràs 298400.00 4634050.00 11 3155 287

Alfés 301525.00 4599500.00 32 318 10

Alforja 330350.00 4564300.00 38 1851 49

Algerri 303950.00 4632225.00 54 469 9

Alguaire 299225.00 4623575.00 50 3165 63

IV Planning optimization software tool for DVB-T and DVB-T2

Alins 362050.00 4712275.00 183 297 2

Alió 358325.00 4573025.00 7 384 55

Almacelles 287125.00 4623250.00 49 6506 133

Almatret 284450.00 4576100.00 57 397 7

Almenar 298025.00 4630275.00 67 3669 55

Almoster 341900.00 4562600.00 6 1357 226

Alòs de Balaguer 330993.00 4642201.00 69 146 2

Alp 408500.00 4692050.00 44 1735 39

Alpens 425786.00 4663555.00 14 311 22

Alpicat 296550.00 4615825.00 15 6058 404

Alt Àneu 345125.00 4722050.00 218 434 2

Altafulla 363800.00 4555925.00 7 4685 669

Amer 467200.00 4651225.00 40 2304 58

Ametlla de Mar, l' 314950.00 4528450.00 67 7592 113

Ametlla del Vallès, l' 438668.00 4613334.00 14 7949 568

Ampolla, l' 306850.00 4520650.00 36 3118 87

Amposta 295650.00 4509450.00 138 21240 154

Anglès 470075.00 4645250.00 16 5569 348

Anglesola 340525.00 4613750.00 24 1351 56

Arbeca 327025.00 4601175.00 58 2480 43

Arboç, l' 383100.00 4569550.00 14 5441 389

Arbolí 328200.00 4567850.00 21 112 5

Arbúcies 459875.00 4629650.00 86 6595 77

Arenys de Mar 462519.00 4603449.00 7 14627 2090

Arenys de Munt 461697.00 4606889.00 21 8190 390

Argelaguer 470600.00 4673750.00 13 445 34

Argençola 370472.00 4606509.00 47 240 5

Argentera, l' 324625.00 4556300.00 10 134 13

Argentona 450180.00 4600628.00 25 11633 465

Armentera, l' 506275.00 4669100.00 6 855 143

Arnes 269375.00 4532600.00 43 488 11

Arres 312800.00 4736350.00 12 68 6

Arsèguel 383375.00 4689825.00 11 93 8

Artés 413258.00 4628076.00 18 5433 302

Artesa de Lleida 308525.00 4602900.00 24 1517 63

Artesa de Segre 338150.00 4640200.00 176 3869 22

Ascó 296100.00 4561775.00 74 1608 22

Aspa 305800.00 4596600.00 10 251 25

Avellanes i Santa Linya, les 314700.00 4642100.00 103 467 5

Avià 402919.00 4659138.00 27 2206 82

APPENDICES V

Avinyó 414672.00 4635324.00 63 2289 36

Avinyonet de Puigventós 493025.00 4677700.00 12 1449 121

Avinyonet del Penedès 397872.00 4579783.00 29 1690 58

Badalona 437019.00 4588862.00 21 219547 10455

Badia del Vallès 426300.00 4595800.00 1 13679 13679

Bagà 406270.00 4678681.00 43 2362 55

Baix Pallars 340600.00 4687725.00 129 413 3

Balaguer 317825.00 4629050.00 57 16779 294

Balenyà 436578.00 4629669.00 17 3743 220

Balsareny 406785.00 4635503.00 37 3512 95

Banyeres del Penedès 381275.00 4571025.00 12 2945 245

Banyoles 480700.00 4663050.00 11 18327 1666

Barbens 335025.00 4616125.00 8 892 112

Barberà de la Conca 352025.00 4586075.00 27 539 20

Barberà del Vallès 426944.00 4596449.00 8 31144 3893

Barcelona 430743.00 4583711.00 101 1621537 16055

Baronia de Rialb, la 350525.00 4644025.00 145 279 2

Bàscara 492650.00 4667800.00 18 946 53

Bassella 358855.00 4651965.00 70 252 4

Batea 274275.00 4552900.00 128 2163 17

Bausen 313450.00 4745200.00 18 63 4

Begues 409836.00 4576374.00 50 6271 125

Begur 517300.00 4644925.00 21 4258 203

Belianes 334725.00 4603100.00 16 589 37

Bellaguarda 310650.00 4578900.00 17 336 20

Bellcaire d'Empordà 507950.00 4658750.00 13 664 51

Bellcaire d'Urgell 325900.00 4625375.00 31 1284 41

Bell‐lloc d'Urgell 315200.00 4611400.00 35 2447 70

Bellmunt del Priorat 312700.00 4559575.00 9 354 39

Bellmunt d'Urgell 329900.00 4626900.00 5 215 43

Bellprat 369442.00 4597549.00 31 92 3

Bellpuig 334500.00 4610250.00 35 4940 141

Bellvei 380900.00 4566650.00 8 2005 251

Bellver de Cerdanya 399325.00 4691875.00 98 2231 23

Bellvís 319650.00 4618250.00 47 2481 53

Benavent de Segrià 303150.00 4618900.00 7 1489 213

Benifallet 291150.00 4539050.00 62 788 13

Benissanet 301350.00 4547975.00 23 1250 54

Berga 404657.00 4661908.00 23 17160 746

Besalú 475225.00 4672300.00 5 2361 472

VI Planning optimization software tool for DVB-T and DVB-T2

Bescanó 478500.00 4646175.00 36 4450 124

Beuda 476150.00 4676350.00 36 163 5

Bigues i Riells 435386.00 4614388.00 29 8401 290

Biosca 363775.00 4633700.00 66 222 3

Bisbal de Falset, la 309400.00 4572550.00 14 237 17

Bisbal del Penedès, la 373450.00 4571200.00 33 3397 103

Bisbal d'Empordà, la 503350.00 4645350.00 21 10385 495

Biure 491450.00 4687525.00 10 243 24

Blancafort 346325.00 4589200.00 14 438 31

Blanes 482650.00 4613925.00 18 40047 2225

Boadella i les Escaules 488225.00 4686575.00 11 241 22

Bolvir 408050.00 4697050.00 10 378 38

Bonastre 369325.00 4564625.00 25 657 26

Bòrdes, Es 313450.00 4734425.00 21 238 11

Bordils 492825.00 4654825.00 7 1732 247

Borges Blanques, les 322300.00 4598950.00 62 6058 98

Borges del Camp, les 334050.00 4559975.00 8 2115 264

Borrassà 494000.00 4674750.00 9 665 74

Borredà 417011.00 4665584.00 43 614 14

Bossòst 311350.00 4739800.00 28 1219 44

Bot 280175.00 4543250.00 35 698 20

Botarell 331350.00 4555975.00 12 1047 87

Bovera 302500.00 4577900.00 31 353 11

Bràfim 361100.00 4570125.00 6 666 111

Breda 463425.00 4622150.00 5 3784 757

Bruc, el 398400.00 4604150.00 47 1904 41

Brull, el 442448.00 4629878.00 41 252 6

Brunyola 473925.00 4639425.00 37 373 10

Cabacés 310200.00 4568900.00 31 345 11

Cabanabona 351925.00 4634950.00 14 109 8

Cabanelles 485200.00 4675575.00 56 236 4

Cabanes 498100.00 4684325.00 15 932 62

Cabanyes, les 390478.00 4581005.00 1 888 888

Cabó 355500.00 4677950.00 80 95 1

Cabra del Camp 356025.00 4584275.00 27 1116 41

Cabrera d'Anoia 449570.00 4597495.00 17 1320 78

Cabrera de Mar 391850.00 4592800.00 9 4408 490

Cabrils 447375.00 4597600.00 7 6964 995

Cadaqués 522850.00 4682125.00 26 2860 110

Calaf 376408.00 4621358.00 9 3630 403

APPENDICES VII

Calafell 380100.00 4562200.00 20 24265 1213

Calders 415886.00 4627008.00 33 899 27

Caldes de Malavella 484275.00 4631975.00 57 6710 118

Caldes de Montbui 430658.00 4609352.00 37 16885 456

Caldes d'Estrac 460711.00 4602364.00 1 2799 2799

Calella 472014.00 4607306.00 8 18627 2328

Calldetenes 440762.00 4641960.00 6 2391 399

Callús 399056.00 4626531.00 13 1759 135

Calonge 506350.00 4634500.00 34 10637 313

Calonge de Segarra 373874.00 4624897.00 37 196 5

Camarasa 323975.00 4638200.00 157 991 6

Camarles 303575.00 4516250.00 25 3555 142

Cambrils 336550.00 4548950.00 35 31720 906

Camós 480750.00 4660300.00 16 692 43

Campdevànol 431450.00 4675200.00 33 3505 106

Campelles 429200.00 4683100.00 19 125 7

Campins 455505.00 4619535.00 7 390 56

Campllong 486050.00 4638200.00 9 423 47

Camprodon 447800.00 4685025.00 103 2542 25

Canejan 315275.00 4745550.00 48 104 2

Canet d'Adri 478350.00 4653850.00 44 605 14

Canet de Mar 465216.00 4604473.00 6 13548 2258

Canovelles 440400.00 4607850.00 7 16023 2289

Cànoves i Samalús 446377.00 4615621.00 29 2742 95

Cantallops 493925.00 4696900.00 20 319 16

Canyelles 393085.00 4571539.00 14 4104 293

Capafonts 334925.00 4573650.00 13 119 9

Capçanes 313775.00 4552450.00 22 415 19

Capellades 390437.00 4598659.00 3 5525 1842

Capmany 493525.00 4691525.00 26 593 23

Capolat 396972.00 4659415.00 34 77 2

Cardedeu 446656.00 4610070.00 12 16596 1383

Cardona 390750.00 4641250.00 67 5187 77

Carme 385024.00 4598891.00 12 832 69

Caseres 269050.00 4546950.00 43 293 7

Cassà de la Selva 489825.00 4637475.00 45 9537 212

Casserres 404229.00 4652102.00 29 1590 55

Castell de l'Areny 413032.00 4669710.00 24 75 3

Castell de Mur 324550.00 4662400.00 62 173 3

Castellar de la Ribera 368925.00 4653650.00 60 160 3

VIII Planning optimization software tool for DVB-T and DVB-T2

Castellar de n'Hug 419099.00 4681886.00 47 198 4

Castellar del Riu 393385.00 4665355.00 33 154 5

Castellar del Vallès 424014.00 4607721.00 45 23002 511

Castellbell i el Vilar 405500.00 4609500.00 28 3680 131

Castellbisbal 415031.00 4592180.00 31 11977 386

Castellcir 429325.00 4623700.00 34 628 18

Castelldans 313625.00 4596700.00 65 1015 16

Castelldefels 414300.00 4570300.00 13 62080 4775

Castellet i la Gornal 382150.00 4568150.00 47 2222 47

Castellfollit de la Roca 462925.00 4674450.00 1 1043 1043

Castellfollit de Riubregós 370305.00 4626295.00 26 195 8

Castellfollit del Boix 390600.00 4613712.00 59 426 7

Castellgalí 403800.00 4614525.00 17 1918 113

Castellnou de Bages 403508.00 4632297.00 29 1021 35

Castellnou de Seana 331100.00 4612875.00 16 739 46

Castelló de Farfanya 311400.00 4632400.00 53 572 11

Castelló d'Empúries 506175.00 4678800.00 42 12111 288

Castellolí 391782.00 4606199.00 25 506 20

Castell‐Platja d'Aro 505665.00 4629715.00 22 10376 472

Castellserà 332800.00 4623800.00 16 1131 71

Castellterçol 427049.00 4622611.00 32 2375 74

Castellvell del Camp 340475.00 4560750.00 5 2686 537

Castellví de la Marca 384500.00 4576100.00 28 1661 59

Castellví de Rosanes 408157.00 4589478.00 16 1719 107

Catllar, el 359675.00 4559750.00 26 4079 157

Cava 383455.00 4687075.00 42 50 1

Cellera de Ter, la 468650.00 4646700.00 15 2186 146

Celrà 490050.00 4652750.00 20 4513 226

Centelles 435338.00 4627812.00 15 7209 481

Cercs 405975.00 4666828.00 47 1334 28

Cerdanyola del Vallès 428114.00 4593734.00 31 58747 1895

Cervelló 412874.00 4583451.00 24 8393 350

Cervera 356250.00 4614750.00 55 9328 170

Cervià de les Garrigues 321325.00 4588375.00 34 846 25

Cervià de Ter 492700.00 4657450.00 10 889 89

Cistella 487575.00 4679800.00 26 238 9

Ciutadilla 345025.00 4602900.00 17 226 13

Clariana de Cardener 386497.00 4643443.00 41 152 4

Cogul, el 307150.00 4593350.00 18 203 11

Colera 512700.00 4694900.00 24 573 24

APPENDICES IX

Coll de Nargó 361025.00 4670650.00 151 634 4

Collbató 402475.00 4602781.00 18 4040 224

Colldejou 322700.00 4552125.00 14 183 13

Collsuspina 431577.00 4630980.00 15 351 23

Colomers 499025.00 4659350.00 4 195 49

Coma i la Pedra, la 383625.00 4670475.00 61 278 5

Conca de Dalt 332775.00 4679100.00 166 410 2

Conesa 357550.00 4597850.00 29 124 4

Constantí 350125.00 4557550.00 31 6373 206

Copons 376679.00 4610671.00 19 318 17

Corbera de Llobregat 410700.00 4585600.00 18 13843 769

Corbera d'Ebre 288050.00 4550550.00 53 1174 22

Corbins 308300.00 4618225.00 21 1356 65

Corçà 501500.00 4648650.00 16 1280 80

Cornellà de Llobregat 422763.00 4579550.00 7 86519 12360

Cornellà del Terri 484925.00 4659950.00 28 2176 78

Cornudella de Montsant 324600.00 4570500.00 64 1015 16

Creixell 369325.00 4558750.00 10 3219 322

Crespià 483500.00 4670850.00 11 253 23

Cruïlles, Monells i Sant Sadurní de l'Heura 499400.00 4645150.00 100 1272 13

Cubelles 388810.00 4562912.00 13 13711 1055

Cubells 330650.00 4635550.00 39 401 10

Cunit 385625.00 4561850.00 10 12279 1228

Darnius 486375.00 4690775.00 35 536 15

Das 407050.00 4690750.00 15 227 15

Deltebre 307700.00 4510350.00 107 11751 110

Dosrius 450684.00 4605238.00 41 4937 120

Duesaigües 326400.00 4557200.00 14 240 17

Escala, l' 511150.00 4663875.00 16 10140 634

Esparreguera 405802.00 4599324.00 27 21855 809

Espinelves 451725.00 4635575.00 17 190 11

Espluga Calba, l' 333550.00 4595825.00 22 429 20

Espluga de Francolí, l' 341450.00 4584650.00 57 3982 70

Esplugues de Llobregat 423562.00 4580967.00 5 46862 9372

Espolla 500150.00 4693425.00 44 404 9

Esponellà 483250.00 4669775.00 16 462 29

Espot 343100.00 4715800.00 97 364 4

Espunyola, l' 398300.00 4656575.00 35 260 7

Estamariu 378475.00 4692500.00 21 115 5

X Planning optimization software tool for DVB-T and DVB-T2

Estany, l' 426388.00 4635859.00 10 390 39

Estaràs 365150.00 4617050.00 21 177 8

Esterri d'Àneu 346200.00 4721450.00 8 965 121

Esterri de Cardós 357500.00 4717200.00 17 75 4

Falset 317175.00 4557350.00 32 2864 90

Far d'Empordà, el 499700.00 4677925.00 9 537 60

Farrera 358225.00 4707425.00 62 133 2

Fatarella, la 288150.00 4560025.00 57 1128 20

Febró, la 332970.00 4571687.00 16 46 3

Figaró‐Montmany 439708.00 4619262.00 15 1057 70

Fígols 404218.00 4670813.00 29 48 2

Fígols i Alinyà 363100.00 4673825.00 102 284 3

Figuera, la 309900.00 4565400.00 19 148 8

Figueres 496900.00 4679450.00 19 43330 2281

Figuerola del Camp 355025.00 4581700.00 23 341 15

Flaçà 496325.00 4655375.00 7 1072 153

Flix 294750.00 4567450.00 117 4098 35

Floresta, la 326500.00 4597650.00 5 179 36

Fogars de la Selva 473500.00 4621100.00 32 1513 47

Fogars de Montclús 453800.00 4619800.00 40 465 12

Foixà 499825.00 4654575.00 19 336 18

Folgueroles 443181.00 4643663.00 10 2205 221

Fondarella 322975.00 4611600.00 5 821 164

Fonollosa 389429.00 4624516.00 52 1399 27

Fontanals de Cerdanya 409600.00 4693300.00 29 451 16

Fontanilles 508950.00 4651100.00 9 173 19

Fontcoberta 482750.00 4665850.00 17 1251 74

Font‐rubí 387350.00 4585750.00 37 1483 40

Foradada 335200.00 4638250.00 29 189 7

Forallac 504675.00 4645500.00 51 1737 34

Forès 353050.00 4595325.00 16 43 3

Fornells de la Selva 484500.00 4642700.00 12 2295 191

Fortià 503300.00 4676975.00 11 639 58

Franqueses del Vallès, les 441500.00 4607750.00 29 17660 609

Freginals 290500.00 4505425.00 18 449 25

Fuliola, la 335225.00 4620075.00 11 1239 113

Fulleda 335100.00 4592250.00 16 113 7

Gaià 410984.00 4641271.00 39 171 4

Galera, la 285650.00 4506700.00 27 884 33

Gallifa 426401.00 4616205.00 16 214 13

APPENDICES XI

Gandesa 284850.00 4548050.00 71 3236 46

Garcia 302875.00 4556800.00 52 602 12

Garidells, els 353175.00 4563450.00 3 221 74

Garriga, la 440705.00 4615035.00 19 14991 789

Garrigàs 496275.00 4671425.00 20 395 20

Garrigoles 502700.00 4661950.00 9 160 18

Garriguella 504750.00 4688100.00 21 825 39

Gavà 416614.00 4573330.00 31 45994 1484

Gavet de la Conca 328200.00 4665750.00 91 307 3

Gelida 405124.00 4588534.00 27 6801 252

Ger 405025.00 4696275.00 33 450 14

Gimenells i el Pla de la Font 282750.00 4614625.00 56 1182 21

Ginestar 301200.00 4546300.00 16 1066 67

Girona 485175.00 4648075.00 39 96188 2466

Gironella 407559.00 4654542.00 7 5052 722

Gisclareny 399985.00 4678455.00 36 34 1

Godall 286125.00 4503750.00 34 841 25

Golmés 327750.00 4611425.00 17 1693 100

Gombrèn 425100.00 4677900.00 43 232 5

Gósol 389650.00 4677100.00 56 219 4

Granada, la 392992.00 4581654.00 7 1976 282

Granadella, la 304800.00 4581025.00 89 765 9

Granera 421775.00 4620300.00 24 77 3

Granja d'Escarp, la 278850.00 4588850.00 39 984 25

Granollers 440939.00 4606563.00 15 60658 4044

Granyanella 352545.00 4614335.00 24 160 7

Granyena de les Garrigues 303700.00 4589700.00 20 172 9

Granyena de Segarra 353975.00 4609700.00 16 138 9

Gratallops 313675.00 4562775.00 14 266 19

Gualba 458800.00 4620150.00 23 1192 52

Gualta 508675.00 4653150.00 9 380 42

Guardiola de Berguedà 407633.00 4676434.00 62 1007 16

Guiamets, els 311400.00 4552700.00 12 330 28

Guils de Cerdanya 407875.00 4700400.00 22 479 22

Guimerà 348800.00 4603225.00 26 334 13

Guingueta d'Àneu, la 346850.00 4717550.00 108 372 3

Guissona 357900.00 4627550.00 18 6145 341

Guixers 389900.00 4665500.00 66 138 2

Gurb 436500.00 4645350.00 52 2475 48

Horta de Sant Joan 274150.00 4537400.00 119 1305 11

XII Planning optimization software tool for DVB-T and DVB-T2

Hospitalet de Llobregat, l' 424992.00 4579383.00 12 257038 21420

Hostalets de Pierola, els 397550.00 4598950.00 33 2612 79

Hostalric 469750.00 4621875.00 3 3994 1331

Igualada 384825.00 4604075.00 8 38918 4865

Isona i Conca Dellà 338600.00 4664950.00 139 1163 8

Isòvol 404475.00 4694775.00 11 289 26

Ivars de Noguera 299750.00 4636150.00 27 363 13

Ivars d'Urgell 332500.00 4616575.00 24 1741 73

Ivorra 366750.00 4625850.00 15 136 9

Jafre 500975.00 4658100.00 7 419 60

Jonquera, la 489750.00 4696375.00 57 3174 56

Jorba 379088.00 4606786.00 31 827 27

Josa i Tuixén 381875.00 4676600.00 68 155 2

Juià 492400.00 4651900.00 8 326 41

Juncosa 314050.00 4582500.00 76 504 7

Juneda 318700.00 4602675.00 47 3417 73

Les 313025.00 4742500.00 23 979 43

Linyola 325750.00 4619950.00 29 2836 98

Llacuna, la 377616.00 4592444.00 52 925 18

Lladó 484700.00 4677500.00 14 682 49

Lladorre 356575.00 4720350.00 147 227 2

Lladurs 376500.00 4656025.00 128 201 2

Llagosta, la 432644.00 4596296.00 3 13820 4607

Llagostera 491150.00 4630800.00 76 7764 102

Llambilles 488425.00 4641225.00 15 678 45

Llanars 446050.00 4685825.00 25 584 23

Llançà 512550.00 4690500.00 28 5209 186

Llardecans 295200.00 4583400.00 66 533 8

Llavorsí 353025.00 4706575.00 69 374 5

Lleida 302200.00 4610300.00 212 135919 641

Llers 492850.00 4682775.00 21 1179 56

Lles de Cerdanya 392025.00 4694150.00 103 271 3

Lliçà d'Amunt 436699.00 4607061.00 22 14143 643

Lliçà de Vall 436920.00 4604922.00 11 6290 572

Llimiana 327750.00 4660400.00 42 168 4

Llinars del Vallès 450207.00 4610046.00 28 9035 323

Llívia 416250.00 4702050.00 13 1589 122

Lloar, el 311425.00 4562050.00 7 117 17

Llobera 373525.00 4645650.00 39 211 5

Llorac 358925.00 4602125.00 23 108 5

APPENDICES XIII

Llorenç del Penedès 378850.00 4571500.00 5 2185 437

Lloret de Mar 487500.00 4616750.00 49 39363 803

Llosses, les 427150.00 4667025.00 114 234 2

Lluçà 423425.00 4657955.00 53 260 5

Maçanet de Cabrenys 479575.00 4692950.00 68 729 11

Maçanet de la Selva 477850.00 4625325.00 46 6871 149

Madremanya 496575.00 4648900.00 14 238 17

Maià de Montcal 478950.00 4674700.00 17 410 24

Maials 291350.00 4582375.00 57 987 17

Maldà 336500.00 4601975.00 31 261 8

Malgrat de Mar 478580.00 4610460.00 9 18472 2052

Malla 436597.00 4637722.00 11 255 23

Manlleu 440663.00 4650218.00 17 20647 1215

Manresa 402462.00 4620400.00 42 76558 1823

Marçà 315625.00 4555350.00 16 649 41

Margalef 312025.00 4572975.00 35 117 3

Marganell 399643.00 4610812.00 14 308 22

Martorell 410800.00 4592100.00 13 26681 2052

Martorelles 436500.00 4597900.00 4 4922 1231

Mas de Barberans 278175.00 4512750.00 79 663 8

Masarac 497825.00 4688950.00 13 265 20

Masdenverge 291550.00 4510350.00 15 1133 76

Masies de Roda, les 443550.00 4649150.00 16 755 47

Masies de Voltregà, les 436750.00 4652750.00 22 3232 147

Masllorenç 367300.00 4570050.00 7 532 76

Masnou, el 443053.00 4592454.00 3 22288 7429

Masó, la 351150.00 4566550.00 4 298 75

Maspujols 336250.00 4561050.00 4 663 166

Masquefa 400977.00 4595333.00 17 8168 480

Masroig, el 309750.00 4555450.00 16 566 35

Massalcoreig 279550.00 4593300.00 14 593 42

Massanes 471125.00 4624050.00 26 713 27

Massoteres 359750.00 4628850.00 26 209 8

Matadepera 418700.00 4606219.00 25 8616 345

Mataró 453731.00 4599067.00 23 121722 5292

Mediona 384150.00 4592950.00 48 2360 49

Menàrguens 312475.00 4622475.00 20 854 43

Meranges 400375.00 4700200.00 37 93 3

Mieres 470300.00 4663950.00 26 344 13

Milà, el 349900.00 4568050.00 4 178 45

XIV Planning optimization software tool for DVB-T and DVB-T2

Miralcamp 323450.00 4608250.00 15 1440 96

Miravet 298150.00 4545975.00 32 814 25

Moià 425084.00 4629464.00 75 5710 76

Molar, el 307650.00 4559675.00 23 302 13

Molins de Rei 417974.00 4585246.00 16 24067 1504

Mollerussa 324750.00 4611025.00 7 14319 2046

Mollet de Peralada 500100.00 4689950.00 6 174 29

Mollet del Vallès 434371.00 4598932.00 11 52484 4771

Molló 451075.00 4688750.00 43 360 8

Molsosa, la 379100.00 4627175.00 27 119 4

Monistrol de Calders 418182.00 4623839.00 22 683 31

Monistrol de Montserrat 403791.00 4607362.00 12 3029 252

Montagut i Oix 466800.00 4675600.00 94 971 10

Montblanc 346450.00 4582250.00 91 7305 80

Montbrió del Camp 332500.00 4554225.00 11 2219 202

Montcada i Reixac 432200.00 4593150.00 23 33453 1454

Montclar 397757.00 4652754.00 22 112 5

Montellà i Martinet 392650.00 4690850.00 55 661 12

Montesquiu 434739.00 4662421.00 5 906 181

Montferrer i Castellbò 370600.00 4689050.00 177 1089 6

Montferri 363200.00 4569700.00 19 366 19

Montgai 330750.00 4629775.00 29 727 25

Montgat 440050.00 4591300.00 3 10270 3423

Montmajor 395344.00 4652688.00 76 487 6

Montmaneu 368145.00 4609598.00 14 192 14

Montmell, el 370625.00 4575150.00 73 1431 20

Montmeló 437449.00 4600603.00 4 8955 2239

Montoliu de Lleida 299100.00 4603900.00 7 486 69

Montoliu de Segarra 355950.00 4605900.00 29 193 7

Montornès de Segarra 352650.00 4607200.00 12 108 9

Montornès del Vallès 438849.00 4599497.00 10 15509 1551

Mont‐ral 340800.00 4572600.00 35 171 5

Mont‐ras 511950.00 4639550.00 12 1847 154

Mont‐roig del Camp 328700.00 4550675.00 63 11847 188

Montseny 449881.00 4623321.00 27 319 12

Móra d'Ebre 302025.00 4551800.00 45 5695 127

Móra la Nova 302725.00 4552550.00 16 3179 199

Morell, el 350025.00 4561800.00 6 3285 548

Morera de Montsant, la 319325.00 4570650.00 53 153 3

Muntanyola 431939.00 4636821.00 40 570 14

APPENDICES XV

Mura 414906.00 4617128.00 48 238 5

Nalec 342925.00 4602825.00 9 95 11

Naut Aran 328250.00 4730650.00 256 1740 7

Navarcles 408972.00 4622906.00 6 5947 991

Navàs 407121.00 4639604.00 81 6243 77

Navata 488600.00 4674800.00 18 1128 63

Navès 387400.00 4649875.00 145 266 2

Nou de Berguedà, la 408000.00 4669000.00 25 159 6

Nou de Gaià, la 363800.00 4560600.00 4 497 124

Nulles 357300.00 4568025.00 11 418 38

Odèn 366795.00 4666075.00 114 281 2

Òdena 386831.00 4607143.00 53 3334 63

Ogassa 440500.00 4679750.00 45 264 6

Olèrdola 393120.00 4575252.00 30 3462 115

Olesa de Bonesvalls 403848.00 4578822.00 31 1740 56

Olesa de Montserrat 407801.00 4599851.00 17 23301 1371

Oliana 360550.00 4658950.00 32 1976 62

Oliola 348625.00 4637800.00 86 248 3

Olius 380815.00 4647594.00 55 814 15

Olivella 400563.00 4574040.00 39 3340 86

Olost 425188.00 4648800.00 29 1217 42

Olot 457825.00 4670325.00 29 33524 1156

Oluges, les 360350.00 4617725.00 19 179 9

Olvan 409500.00 4656866.00 36 903 25

Omellons, els 329850.00 4596675.00 11 244 22

Omells de na Gaia, els 339500.00 4596300.00 13 142 11

Ordis 492450.00 4674225.00 9 372 41

Organyà 362100.00 4674750.00 13 958 74

Orís 437150.00 4656725.00 27 284 11

Oristà 422228.00 4642955.00 68 585 9

Orpí 383875.00 4598625.00 15 187 12

Òrrius 446285.00 4600849.00 6 640 107

Os de Balaguer 310800.00 4638150.00 136 985 7

Osor 463275.00 4644050.00 52 354 7

Ossó de Sió 347075.00 4624375.00 26 219 8

Pacs del Penedès 388835.00 4580026.00 6 869 145

Palafolls 479233.00 4613067.00 17 8584 505

Palafrugell 513700.00 4640800.00 27 22365 828

Palamós 510900.00 4633250.00 14 18161 1297

Palau d'Anglesola, el 323750.00 4613350.00 12 2099 175

XVI Planning optimization software tool for DVB-T and DVB-T2

Palau de Santa Eulàlia 497150.00 4669250.00 8 112 14

Palau‐sator 509200.00 4648750.00 12 290 24

Palau‐saverdera 512425.00 4684250.00 16 1451 91

Palau‐solità i Plegamans 431850.00 4604500.00 15 14070 938

Pallaresos, els 354975.00 4559900.00 5 3991 798

Pallejà 416246.00 4586330.00 8 11134 1392

Palma de Cervelló, la 413815.00 4585315.00 5 3057 611

Palma d'Ebre, la 304625.00 4572950.00 37,9 413 11

Palol de Revardit 483225.00 4657775.00 18 479 27

Pals 512425.00 4646650.00 26 2799 108

Papiol, el 417343.00 4587998.00 9 3900 433

Pardines 435325.00 4684900.00 31 159 5

Parets del Vallès 436200.00 4602800.00 9 17632 1959

Parlavà 502675.00 4652500.00 6 394 66

Passanant i Belltall 349675.00 4599575.00 27 159 6

Pau 509725.00 4685100.00 11 578 53

Paüls 281100.00 4533600.00 44 613 14

Pedret i Marzà 505675.00 4684575.00 9 160 18

Penelles 330950.00 4625050.00 26 546 21

Pera, la 497950.00 4652300.00 12 443 37

Perafita 426240.00 4654970.00 20 408 20

Perafort 353800.00 4561600.00 10 1154 115

Peralada 500875.00 4684200.00 44 1805 41

Peramola 356775.00 4657900.00 56 379 7

Perelló, el 307300.00 4527600.00 101 3235 32

Piera 395650.00 4597492.00 57 14324 251

Piles, les 361775.00 4596225.00 22 215 10

Pineda de Mar 474260.00 4608445.00 11 26203 2382

Pinell de Brai, el 291100.00 4543650.00 57 1130 20

Pinell de Solsonès 369035.00 4645655.00 91 214 2

Pinós 379025.00 4631775.00 104 308 3

Pira 349900.00 4587500.00 8 510 64

Pla de Santa Maria, el 357125.00 4580700.00 35 2309 66

Pla del Penedès, el 392481.00 4586151.00 10 1041 104

Planes d'Hostoles, les 461750.00 4656250.00 38 1756 46

Planoles 426300.00 4685450.00 19 279 15

Plans de Sió, els 350250.00 4625125.00 56 573 10

Poal, el 321650.00 4616550.00 9 659 73

Pobla de Cérvoles, la 325750.00 4581825.00 62 257 4

Pobla de Claramunt, la 389907.00 4601135.00 19 2286 120

APPENDICES XVII

Pobla de Lillet, la 415553.00 4677617.00 51 1312 26

Pobla de Mafumet, la 350025.00 4561275.00 6 2403 401

Pobla de Massaluca, la 278150.00 4562450.00 43 399 9

Pobla de Montornès, la 367125.00 4559950.00 12 2852 238

Pobla de Segur, la 332475.00 4679525.00 33 3237 98

Poboleda 319550.00 4567200.00 14 371 27

Polinyà 429824.00 4601033.00 9 7676 853

Pont d'Armentera, el 363150.00 4582750.00 22 620 28

Pont de Bar, el 385250.00 4692250.00 43 197 5

Pont de Molins 494250.00 4684850.00 9 486 54

Pont de Suert, el 314250.00 4697850.00 148 2570 17

Pont de Vilomara i Rocafort, el 406225.00 4617250.00 27 3714 138

Pontils 365500.00 4593250.00 68 147 2

Pontons 376125.00 4586175.00 26 530 20

Pontós 493250.00 4670650.00 14 238 17

Ponts 349650.00 4642100.00 31 2803 90

Porqueres 482518.00 4661985.00 34 4380 129

Porrera 320300.00 4562075.00 29 480 17

Port de la Selva, el 516975.00 4687550.00 42 1015 24

Portbou 513100.00 4697300.00 9 1325 147

Portella, la 303900.00 4623725.00 12 775 65

Pradell de la Teixeta 321850.00 4558475.00 22 182 8

Prades 331650.00 4575250.00 33 676 20

Prat de Comte 281875.00 4540325.00 26 205 8

Prat de Llobregat, el 424372.00 4575838.00 31 63418 2046

Pratdip 321200.00 4546825.00 36 843 23

Prats de Lluçanès 419748.00 4651432.00 14 2722 194

Prats de Rei, els 378719.00 4618319.00 26 538 21

Prats i Sansor 404525.00 4691300.00 7 237 34

Preixana 336700.00 4608150.00 22 433 20

Preixens 338150.00 4628950.00 29 506 17

Premià de Dalt 445400.00 4595379.00 7 9944 1421

Premià de Mar 446560.00 4593555.00 2 27399 13700

Preses, les 455500.00 4666025.00 9 1731 192

Prullans 396050.00 4692850.00 21 232 11

Puigcerdà 411850.00 4698600.00 19 9022 475

Puigdàlber 391467.00 4584663.00 0,4 508 1270

Puiggròs 324025.00 4602225.00 10 309 31

Puigpelat 357525.00 4571300.00 9 977 109

XVIII Planning optimization software tool for DVB-T and DVB-T2

Puig‐reig 407245.00 4647524.00 46 4403 96

Puigverd d'Agramunt 344050.00 4627025.00 17 278 16

Puigverd de Lleida 311050.00 4601800.00 13 1391 107

Pujalt 368709.00 4619694.00 31 203 7

Quar, la 415800.00 4662150.00 38 61 2

Quart 486900.00 4643150.00 38 2852 75

Queralbs 431075.00 4689025.00 93 199 2

Querol 366250.00 4587250.00 72 539 7

Rabós 502375.00 4692100.00 45 217 5

Rajadell 392490.00 4620556.00 46 496 11

Rasquera 298150.00 4541975.00 51 955 19

Regencós 514200.00 4644725.00 6 326 54

Rellinars 409300.00 4611275.00 18 713 40

Renau 358600.00 4565300.00 8 90 11

Reus 341350.00 4557850.00 53 107118 2021

Rialp 346650.00 4700975.00 63 656 10

Riba, la 347575.00 4575850.00 8 722 90

Riba‐roja d'Ebre 289600.00 4569800.00 99 1348 14

Ribera d'Ondara 361975.00 4609975.00 54 445 8

Ribera d'Urgellet 366800.00 4685950.00 107 954 9

Ribes de Freser 431700.00 4684475.00 42 1976 47

Riells i Viabrea 463300.00 4619575.00 27 3779 140

Riera de Gaià, la 362575.00 4558675.00 9 1587 176

Riner 381050.00 4644500.00 47 300 6

Ripoll 433350.00 4672350.00 74 11057 149

Ripollet 429689.00 4594701.00 4 37088 9272

Riu de Cerdanya 403415.00 4689015.00 12 110 9

Riudarenes 476550.00 4630250.00 48 2070 43

Riudaura 451175.00 4671075.00 24 443 18

Riudecanyes 328975.00 4555375.00 17 1083 64

Riudecols 330300.00 4559650.00 19 1296 68

Riudellots de la Selva 483950.00 4638200.00 13 1984 153

Riudoms 336500.00 4556150.00 32 6436 201

Riumors 503525.00 4675200.00 7 233 33

Roca del Vallès, la 443962.00 4604511.00 37 10214 276

Rocafort de Queralt 356650.00 4593400.00 8 278 35

Roda de Barà 370500.00 4560700.00 17 6186 364

Roda de Ter 442891.00 4648223.00 2 6015 3008

Rodonyà 365900.00 4571350.00 8 508 64

Roquetes 289500.00 4522050.00 137 8223 60

APPENDICES XIX

Roses 514800.00 4679100.00 46 20197 439

Rosselló 299950.00 4618750.00 10 2912 291

Rourell, el 350725.00 4565300.00 2 380 190

Rubí 419237.00 4593980.00 32 72987 2281

Rubió 381022.00 4611514.00 48 202 4

Rupià 500975.00 4652250.00 5 243 49

Rupit i Pruit 455862.00 4652793.00 48 325 7

Sabadell 425814.00 4599900.00 38 206493 5434

Sagàs 414238.00 4656133.00 45 134 3

Salàs de Pallars 329350.00 4675650.00 20 342 17

Saldes 395873.00 4676245.00 66 348 5

Sales de Llierca 471200.00 4676225.00 36 141 4

Sallent 408419.00 4631036.00 65 7129 110

Salomó 363850.00 4565725.00 12 501 42

Salou 343250.00 4549100.00 15 26649 1777

Salt 482450.00 4647150.00 7 29985 4284

Sanaüja 359950.00 4637600.00 33 457 14

Sant Adrià de Besòs 435510.00 4586514.00 4 33761 8440

Sant Agustí de Lluçanès 427900.00 4659800.00 13 101 8

Sant Andreu de la Barca 414554.00 4588963.00 6 26401 4400

Sant Andreu de Llavaneres 456871.00 4602548.00 12 10181 848

Sant Andreu Salou 485625.00 4635900.00 6 152 25

Sant Aniol de Finestres 468195.00 4657875.00 48 327 7

Sant Antoni de Vilamajor 450192.00 4613848.00 14 5444 389

Sant Bartomeu del Grau 431397.00 4648507.00 34 957 28

Sant Boi de Llobregat 419832.00 4577595.00 21 82428 3925

Sant Boi de Lluçanès 429866.00 4656749.00 20 569 28

Sant Carles de la Ràpita 296350.00 4499500.00 54 15511 287

Sant Cebrià de Vallalta 466721.00 4607810.00 16 3309 207

Sant Celoni 457714.00 4615494.00 65 16860 259

Sant Climent de Llobregat 416199.00 4576779.00 11 3779 344

Sant Climent Sescebes 498450.00 4690950.00 24 513 21

Sant Cugat del Vallès 423467.00 4591732.00 48 79253 1651

Sant Cugat Sesgarrigues 395805.00 4580164.00 6 932 155

Sant Esteve de la Sarga 315100.00 4661275.00 93 143 2

Sant Esteve de Palautordera 453062.00 4617208.00 11 2458 223

Sant Esteve Sesrovires 406045.00 4594320.00 19 7202 379

Sant Feliu de Buixalleu 465727.00 4626891.00 62 811 13

Sant Feliu de Codines 430522.00 4615738.00 15 5702 380

Sant Feliu de Guíxols 502475.00 4625875.00 16 21977 1374

XX Planning optimization software tool for DVB-T and DVB-T2

Sant Feliu de Llobregat 420345.00 4581802.00 12 42919 3577

Sant Feliu de Pallerols 459375.00 4658500.00 35 1397 40

Sant Feliu Sasserra 419024.00 4644359.00 22 641 29

Sant Ferriol 472750.00 4672025.00 42 216 5

Sant Fost de Campsentelles 436197.00 4596658.00 13 8234 633

Sant Fruitós de Bages 406417.00 4622955.00 22 7961 362

Sant Gregori 478425.00 4649025.00 49 3167 65

Sant Guim de Freixenet 368600.00 4612950.00 25 1126 45

Sant Guim de la Plana 360825.00 4625025.00 12 191 16

Sant Hilari Sacalm 459575.00 4636500.00 83 5763 69

Sant Hipòlit de Voltregà 436983.00 4651965.00 1 3447 3447

Sant Iscle de Vallalta 464208.00 4608195.00 18 1267 70

Sant Jaume de Frontanyà 419560.00 4671208.00 21 29 1

Sant Jaume de Llierca 467675.00 4673650.00 7 809 116

Sant Jaume dels Domenys 379475.00 4573225.00 24 2388 100

Sant Jaume d'Enveja 307300.00 4508800.00 61 3528 58

Sant Joan de les Abadesses 441150.00 4676250.00 54 3556 66

Sant Joan de Mollet 495300.00 4655025.00 3 517 172

Sant Joan de Vilatorrada 400750.00 4622250.00 16 10779 674

Sant Joan Despí 421214.00 4580050.00 6 32030 5338

Sant Joan les Fonts 459875.00 4673750.00 32 2787 87

Sant Jordi Desvalls 496250.00 4657900.00 12 649 54

Sant Julià de Cerdanyola 408729.00 4675427.00 12 273 23

Sant Julià de Ramis 488000.00 4653300.00 19 3233 170

Sant Julià de Vilatorta 444082.00 4641639.00 16 2955 185

Sant Julià del Llor i Bonmatí 472225.00 4623600.00 10 1276 128

Sant Just Desvern 422930.00 4581611.00 8 15811 1976

Sant Llorenç de la Muga 482750.00 4685575.00 32 213 7

Sant Llorenç de Morunys 383625.00 4666125.00 4 1084 271

Sant Llorenç d'Hortons 401927.00 4591567.00 20 2419 121

Sant Llorenç Savall 421783.00 4614671.00 41 2402 59

Sant Martí d'Albars 423450.00 4653500.00 15 113 8

Sant Martí de Centelles 437800.00 4624000.00 26 1001 39

Sant Martí de Llémena 470950.00 4654050.00 43 545 13

Sant Martí de Riucorb 337900.00 4602950.00 35 697 20

Sant Martí de Tous 376981.00 4602194.00 39 1160 30

Sant Martí Sarroca 383954.00 4582667.00 35 3142 90

Sant Martí Sesgueioles 374379.00 4617923.00 4 371 93

Sant Martí Vell 494350.00 4652200.00 18 250 14

Sant Mateu de Bages 394799.00 4628191.00 103 658 6

APPENDICES XXI

Sant Miquel de Campmajor 473550.00 4665100.00 33 252 8

Sant Miquel de Fluvià 499400.00 4669450.00 4 782 196

Sant Mori 499350.00 4667100.00 8 180 23

Sant Pau de Segúries 447775.00 4679250.00 9 717 80

Sant Pere de Ribes 397269.00 4568755.00 41 28353 692

Sant Pere de Riudebitlles 391801.00 4589791.00 5 2376 475

Sant Pere de Torelló 441800.00 4658599.00 55 2389 43

Sant Pere de Vilamajor 449240.00 4615034.00 35 4021 115

Sant Pere Pescador 506850.00 4670850.00 18 2029 113

Sant Pere Sallavinera 381548.00 4621690.00 22 171 8

Sant Pol de Mar 468695.00 4605801.00 8 5102 638

Sant Quintí de Mediona 388506.00 4591181.00 14 2167 155

Sant Quirze de Besora 435531.00 4661534.00 8 2257 282

Sant Quirze del Vallès 423430.00 4598522.00 14 18462 1319

Sant Quirze Safaja 429672.00 4620170.00 26 645 25

Sant Ramon 363825.00 4620825.00 19 563 30

Sant Sadurní d'Anoia 398760.00 4586764.00 19 12237 644

Sant Sadurní d'Osormort 448783.00 4639269.00 31 101 3

Sant Salvador de Guardiola 397425.00 4615175.00 37 3082 83

Sant Vicenç de Castellet 405472.00 4613719.00 17 8564 504

Sant Vicenç de Montalt 459034.00 4603300.00 8 5627 703

Sant Vicenç de Torelló 440006.00 4657369.00 7 1996 285

Sant Vicenç dels Horts 417306.00 4583100.00 9 27701 3078

Santa Bàrbara 288650.00 4510350.00 28 3955 141

Santa Cecília de Voltregà 435679.00 4649476.00 9 190 21

Santa Coloma de Cervelló 417700.00 4580250.00 7 7744 1106

Santa Coloma de Farners 472125.00 4634800.00 71 11739 165

Santa Coloma de Gramenet 434120.00 4589437.00 7 119717 17102

Santa Coloma de Queralt 365225.00 4599325.00 34 3167 93

Santa Cristina d'Aro 500100.00 4629200.00 68 5017 74

Santa Eugènia de Berga 440633.00 4639166.00 7 2231 319

Santa Eulàlia de Riuprimer 432802.00 4640405.00 14 1052 75

Santa Eulàlia de Ronçana 435550.00 4611550.00 14 6802 486

Santa Fe del Penedès 393123.00 4582488.00 3 389 130

Santa Llogaia d'Àlguema 496150.00 4675875.00 2 335 168

Santa Margarida de Montbui 384050.00 4603600.00 28 9834 351

Santa Margarida i els Monjos 388258.00 4575485.00 17 6989 411

Santa Maria de Besora 438739.00 4664428.00 25 163 7

Santa Maria de Corcó 447802.00 4654116.00 62 2293 37

Santa Maria de Martorelles 437850.00 4596900.00 5 850 170

XXII Planning optimization software tool for DVB-T and DVB-T2

Santa Maria de Merlès 415448.00 4650478.00 52 163 3

Santa Maria de Miralles 377150.00 4597500.00 25 130 5

Santa Maria de Palautordera 453882.00 4616100.00 17 8823 519

Santa Maria d'Oló 420134.00 4636422.00 66 1086 16

Santa Oliva 378675.00 4568125.00 10 3240 324

Santa Pau 464650.00 4666075.00 49 1610 33

Santa Perpètua de Mogoda 431765.00 4598746.00 16 25048 1566

Santa Susanna 475682.00 4609493.00 13 3251 250

Santpedor 403642.00 4626664.00 17 6875 404

Sarral 353750.00 4589800.00 52 1715 33

Sarrià de Ter 485600.00 4651800.00 4 4468 1117

Sarroca de Bellera 325600.00 4692100.00 88 133 2

Sarroca de Lleida 296450.00 4592800.00 42 432 10

Saus, Camallera i Llampaies 497100.00 4663650.00 11 768 70

Savallà del Comtat 358300.00 4600675.00 15 71 5

Secuita, la 355900.00 4563050.00 18 1522 85

Selva de Mar, la 515500.00 4686025.00 7 226 32

Selva del Camp, la 344050.00 4564500.00 35 5376 154

Senan 340375.00 4592900.00 12 59 5

Sénia, la 270200.00 4501975.00 108 6179 57

Senterada 330150.00 4688075.00 34 145 4

Sentiu de Sió, la 323900.00 4630450.00 30 496 17

Sentmenat 428098.00 4606753.00 29 7870 271

Serinyà 479200.00 4668875.00 17 1095 64

Seròs 283850.00 4593650.00 86 1864 22

Serra de Daró 506100.00 4653050.00 8 194 24

Setcases 442575.00 4691850.00 49 173 4

Seu d'Urgell, la 373400.00 4690800.00 15 13063 871

Seva 440658.00 4632318.00 30 3370 112

Sidamon 319500.00 4611100.00 8 756 95

Sils 478800.00 4628650.00 30 5127 171

Sitges 400439.00 4565891.00 44 27668 629

Siurana 499600.00 4673175.00 11 206 19

Sobremunt 431004.00 4654299.00 14 98 7

Soleràs, el 306350.00 4587450.00 12 398 33

Solivella 347950.00 4591150.00 21 685 33

Solsona 377500.00 4650575.00 18 9233 513

Sora 430638.00 4662824.00 32 180 6

Soriguera 350300.00 4692700.00 106 370 3

Sort 346225.00 4697350.00 105 2382 23

APPENDICES XXIII

Soses 290425.00 4601200.00 30 1716 57

Subirats 399500.00 4582150.00 56 3099 55

Sudanell 297200.00 4603600.00 9 887 99

Sunyer 299225.00 4599800.00 13 296 23

Súria 396477.00 4632272.00 24 6438 268

Susqueda 462575.00 4651650.00 51 130 3

Tagamanent 439200.00 4621200.00 43 308 7

Talamanca 415128.00 4621261.00 29 162 6

Talarn 326675.00 4672750.00 28 397 14

Talavera 361550.00 4604950.00 30 297 10

Tallada d'Empordà, la 504625.00 4658825.00 17 420 25

Taradell 440892.00 4636009.00 26 5964 229

Tarragona 353250.00 4553350.00 65 140323 2159

Tàrrega 345250.00 4612600.00 88 16539 188

Tarrés 334650.00 4587800.00 13 108 8

Tarroja de Segarra 356700.00 4621550.00 8 178 22

Tavèrnoles 444285.00 4644847.00 19 302 16

Tavertet 451932.00 4649650.00 32 158 5

Teià 443587.00 4594332.00 7 6087 870

Térmens 313800.00 4621050.00 28 1536 55

Terrades 486825.00 4684425.00 21 301 14

Terrassa 417894.00 4602110.00 70 210941 3013

Tiana 439035.00 4592647.00 8 7590 949

Tírvia 355750.00 4708675.00 9 143 16

Tiurana 355504.00 4648785.00 16 85 5

Tivenys 290575.00 4531675.00 54 959 18

Tivissa 309550.00 4546075.00 209 1815 9

Tona 436018.00 4633478.00 17 7955 468

Torà 367500.00 4630300.00 93 1367 15

Tordera 476693.00 4616603.00 84 15345 183

Torelló 439191.00 4655576.00 13 13808 1062

Torms, els 309500.00 4585200.00 14 174 12

Tornabous 338225.00 4618650.00 24 857 36

Torre de Cabdella, la 334100.00 4698800.00 165 811 5

Torre de Claramunt, la 388279.00 4599054.00 15 3726 248

Torre de Fontaubella, la 320825.00 4554950.00 7 147 21

Torre de l'Espanyol, la 300975.00 4562950.00 28 678 24

Torrebesses 299050.00 4589075.00 27 300 11

Torredembarra 365700.00 4556225.00 9 15272 1697

Torrefarrera 300900.00 4616450.00 23 3911 170

XXIV Planning optimization software tool for DVB-T and DVB-T2

Torrefeta i Florejacs 356600.00 4624100.00 89 633 7

Torregrossa 319100.00 4605600.00 41 2255 55

Torrelameu 308900.00 4619750.00 11 685 62

Torrelavit 393922.00 4589433.00 24 1372 57

Torrelles de Foix 380499.00 4583031.00 37 2463 67

Torrelles de Llobregat 414858.00 4579102.00 14 5430 388

Torrent 510600.00 4644525.00 8 193 24

Torres de Segre 292750.00 4601275.00 51 2052 40

Torre‐serona 302875.00 4616300.00 6 358 60

Torroella de Fluvià 503450.00 4669350.00 17 678 40

Torroella de Montgrí 510650.00 4654650.00 66 11598 176

Torroja del Priorat 316625.00 4564900.00 13 165 13

Tortellà 469575.00 4675975.00 11 760 69

Tortosa 291100.00 4520925.00 219 35143 160

Toses 418975.00 4686150.00 58 160 3

Tossa de Mar 494350.00 4618750.00 39 5948 153

Tremp 326000.00 4670700.00 303 6228 21

Ullà 509025.00 4655625.00 7 1076 154

Ullastrell 412972.00 4597970.00 7 1864 266

Ullastret 505775.00 4650025.00 11 239 22

Ulldecona 284025.00 4497350.00 127 7236 57

Ulldemolins 322375.00 4576850.00 38 481 13

Ultramort 502950.00 4654050.00 4 196 49

Urús 405550.00 4689550.00 17 201 12

Vacarisses 409805.00 4607033.00 41 5872 143

Vajol, la 483575.00 4694800.00 5 98 20

Vall de Bianya, la 454900.00 4673550.00 94 1321 14

Vall de Boí, la 319400.00 4708450.00 219 1090 5

Vall de Cardós 354450.00 4714100.00 56 420 8

Vall d'en Bas, la 455385.00 4663215.00 91 2780 31

Vallbona d'Anoia 392275.00 4597381.00 6 1427 238

Vallbona de les Monges 340700.00 4599025.00 34 251 7

Vallcebre 402550.00 4673275.00 28 264 9

Vallclara 331350.00 4582800.00 14 123 9

Vallfogona de Balaguer 318400.00 4624750.00 27 1769 66

Vallfogona de Ripollès 442500.00 4672050.00 39 221 6

Vallfogona de Riucorb 353025.00 4602975.00 11 119 11

Vallgorguina 459272.00 4610875.00 22 2465 112

Vallirana 410718.00 4582355.00 24 14066 586

Vall‐llobrega 510550.00 4636800.00 5 853 171

APPENDICES XXV

Vallmoll 353350.00 4567450.00 17 1630 96

Vallromanes 441793.00 4598257.00 11 2283 208

Valls 353625.00 4572250.00 55 25092 456

Valls d'Aguilar, les 363400.00 4683950.00 124 307 2

Valls de Valira, les 373175.00 4693625.00 171 832 5

Vandellòs i l'Hospitalet de l'Infant 317725.00 4543400.00 103 5754 56

Vansa i Fórnols, la 374850.00 4677025.00 106 213 2

Veciana 374222.00 4612930.00 39 172 4

Vendrell, el 377300.00 4564575.00 37 35821 968

Ventalló 502316.27 4666566.33 25 780 31

Verdú 345400.00 4608450.00 36 1025 28

Verges 503950.00 4656850.00 10 1162 116

Vespella de Gaià 362374.00 4653261.00 18 404 22

Vic 438306.00 4642404.00 31 39844 1285

Vidrà 443100.00 4663850.00 34 173 5

Vidreres 481675.00 4626625.00 48 7430 155

Vielha e Mijaran 319500.00 4730325.00 212 5710 27

Vilabella 360200.00 4567775.00 18 799 44

Vilabertran 498550.00 4681400.00 2 882 441

Vilablareix 483100.00 4644850.00 6 2283 381

Vilada 411676.00 4665728.00 22 520 24

Viladamat 506300.00 4664800.00 12 466 39

Viladasens 494350.00 4660550.00 16 217 14

Viladecans 418026.00 4574555.00 20 63489 3174

Viladecavalls 412922.00 4601192.00 20 7322 366

Vilademuls 490900.00 4665375.00 62 786 13

Viladrau 449450.00 4633250.00 51 1100 22

Vilafant 494950.00 4677400.00 8 5416 677

Vilafranca del Penedès 391354.00 4578122.00 20 38425 1921

Vilagrassa 342350.00 4612675.00 20 455 23

Vilajuïga 507850.00 4686200.00 13 1149 88

Vilalba dels Arcs 282650.00 4555550.00 67 724 11

Vilalba Sasserra 453550.00 4611500.00 6 636 106

Vilaller 312450.00 4705400.00 59 715 12

Vilallonga de Ter 443400.00 4686975.00 64 478 7

Vilallonga del Camp 349675.00 4564950.00 9 1889 210

Vilamacolum 504675.00 4671650.00 6 322 54

Vilamalla 497625.00 4674150.00 9 1134 126

Vilamaniscle 505675.00 4691675.00 5 169 34

XXVI Planning optimization software tool for DVB-T and DVB-T2

Vilamòs 314025.00 4735500.00 15 174 12

Vilanant 490950.00 4678300.00 17 356 21

Vilanova de Bellpuig 330450.00 4609100.00 14 1170 84

Vilanova de la Barca 310975.00 4617975.00 22 1273 58

Vilanova de l'Aguda 355200.00 4641600.00 54 234 4

Vilanova de Meià 336300.00 4651250.00 105 418 4

Vilanova de Prades 329225.00 4579625.00 22 144 7

Vilanova de Sau 449104.00 4644292.00 59 336 6

Vilanova de Segrià 301975.00 4620750.00 9 845 94

Vilanova del Camí 386449.00 4603302.00 10 12649 1265

Vilanova del Vallès 440500.00 4600650.00 15 4654 310

Vilanova d'Escornalbou 326875.00 4553525.00 17 559 33

Vilanova i la Geltrú 393250.00 4564500.00 34 65890 1938

Vilaplana 335250.00 4566100.00 23 614 27

Vila‐rodona 362675.00 4574775.00 33 1260 38

Vila‐sacra 501550.00 4679450.00 6 598 100

Vila‐sana 327675.00 4614575.00 19 696 37

Vila‐seca 344400.00 4552850.00 22 20866 948

Vilassar de Dalt 446648.00 4596553.00 9 8672 964

Vilassar de Mar 449323.00 4595034.00 4 19482 4871

Vilaür 496450.00 4665875.00 5 140 28

Vilaverd 347600.00 4577725.00 13 494 38

Vilella Alta, la 314050.00 4566350.00 5 114 23

Vilella Baixa, la 312550.00 4565875.00 6 206 34

Vilobí del Penedès 388178.00 4582965.00 9 1112 124

Vilobí d'Onyar 478700.00 4637600.00 33 2956 90

Vilopriu 499550.00 4661700.00 16 221 14

Vilosell, el 328450.00 4583600.00 19 200 11

Vimbodí i Poblet 337100.00 4585250.00 66 1077 16

Vinaixa 330900.00 4588500.00 38 607 16

Vinebre 297900.00 4562200.00 26 483 19

Vinyols i els Arcs 335450.00 4553500.00 11 1829 166

Viver i Serrateix 399078.00 4644950.00 67 186 3

Xerta 288750.00 4531750.00 32 1300 41

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