Applications of Geographic Information Systems for Wireless Network Planning 1630817635, 9781630817633

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Applications of Geographic Information Systems for Wireless Network Planning

For a listing of recent titles in the Artech House Antennas and Electromagnetics Analysis Library, turn to the back of this book.

Applications of Geographic Information Systems for Wireless Network Planning Francisco Saez de Adana Josefa Gómez Pérez Abdelhamid Tayebi Tayebi Juan Casado Ballesteros

Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the U.S. Library of Congress. British Library Cataloguing in Publication Data A catalog record for this book is available from the British Library.

ISBN-13:  978-1-63081-763-3 Cover design by John Gomes © 2020 Artech House All rights reserved. Printed and bound in the United States of America. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher. All terms mentioned in this book that are known to be trademarks or service marks have been appropriately capitalized. Artech House cannot attest to the accuracy of this information. Use of a term in this book should not be regarded as affecting the validity of any trademark or service mark. 10 9 8 7 6 5 4 3 2 1

Contents Preface   11

1 Introduction   13 1.1  Introduction to Empirical Propagation Methods   13 1.2  Introduction to Geographical Information Systems   14 1.3  Advantages of Combining Empirical Propagation Methods with Geographical Information Systems   15 1.4  Structure of the Book   16 References   17

2 Empirical Propagation Methods   19 2.1  Fundamentals of the Empirical 5

6   Applications of Geographic Information Systems for Wireless Network Planning

Propagation Methods   19 2.2  Okumura-Hata Model   21 2.3  Cost 231-Hata Model   22 2.4  Ibrahim-Parsons Model   23 �2.5  McGeehan and Griffiths Model   24 2.6  Atefi and Parsons Model   24 2.7  Sakagami-Kuboi Model   25 2.8  Ikegami Model   26 2.9  Walfisch and Bertoni Model   27 2.10  Xia and Bertoni Model   29 2.11  Cost 231-Walfisch-Ikegami Model   30 2.12  Stanford University Interim Model   32 2.13  Ericsson Model   34 2.14  Eibert and Kuhlman Model   34 2.15  Analysis of the Required Geometric Parameters to Apply the Empirical Propagation Methods    36 References   38

3 Geographical Information Systems   41 3.1  GIS Definition   42 3.2  GIS Architecture and Components   43



Contents

3.2.1  Spatial Databases   44 3.2.2  Edition and Visualization Tools   46 3.2.3  Map Servers   48 3.2.4  High-Level APIs   50 3.2.5  Data Providers   51 3.2.6  User Code   52 3.2.7  Full-Stack Frameworks   53

3.3  Spatial Data Manifestations   54 3.3.1  Raster Data   54 ��3.3.2  Vector Data   56 3.3.3  Map Types   58 3.3.4  Layers   63 ��3.3.5  Coordinate Systems   64

��3.4  Which GIS Components to Use?   66 3.5  How Will Each Type of Data be Useful?   67 ��3.5.1  Maps   68 ��3.5.2  Terrain Height   68 ��3.5.3  Roads   69 ��3.5.4  3D Building Shapes   69 3.5.5  Environment Type   70 ��3.5.6  Geocoding   70 3.5.7  Social Data   71

3.6  GIS Comparison   71 3.6.1  Spatial Databases   72 3.6.2  Edition and Visualization Tools   72 3.6.3  Map Servers   74 3.6.4  High Level APIs   75 3.6.5  Data Providers   76 3.6.6  GIS Offered as Full-Stack Frameworks   78 References   80

7

8   Applications of Geographic Information Systems for Wireless Network Planning

4 Description of the Application   83 4.1  Combination of a GIS and a Semiempirical Propagation Method    84 4.1.1  OpenStreetMap   87 4.1.2  Propagation Model   89

4.2  Technologies Used   89 4.3  Main Features of the Application   91 4.4  How to Develop the Application   98 4.5  Future Improvements of the Application   106 References   109

5 Applications   113 5.1  Electromagnetic Spectrum   114 5.2  Types of Cells in Cellular Networks   114 5.3  Main Applications   118 5.3.1  Planning and Optimization of Radio Communication Systems    119 5.3.2  Broadcasting   123

5.4  Secondary Applications   125 5.4.1  Precision Agriculture Applications   125 5.4.2  Educational, Research, and Training Applications   127 5.4.3  Applications for Emergency Service Providers   130 5.4.4  Humanitarian Use, Military Defense,



Contents

and Public Security   130 5.4.5  Terrain Exploration   131 5.4.6  Other Applications   134

5.5  Computer Software Applications    135 References   138

About the Authors   141 Index   145

9

Preface This book explores the combination of empirical propagation methods with geographical information systems (GIS) to solve real-world engineering problems. The content of the book is based on the authors’ experience as developers of computer tools for analyzing propagation problems in real environments using the geographical information provided by GIS. After several years of working on these problems and developing a web-based application (whose link is included in Chapter 4 of this book), we decided to share these experiences in a book that would be beneficial for readers who are interested in implementing these techniques to analyze engineering problems. With this goal, we have given a practical focus to this book to demonstrate the capabilities these techniques for the analysis of realistic problems. We cover the most widely used empirical propagation methods as well as the most widely used GIS and its features in Chapters 2 and 3. We also show, in Chapter 4, how both techniques must be combined to create an application that is suitable to tackle real-world engineering problems. These theoretical concepts 11

12   Applications of Geographic Information Systems for Wireless Network Planning

would remain purely academic if examples of real problems were not included in the book. The capabilities of the presented tool are described in Chapter 5 through examples of real engineering problems that can be solved using a proper implementation of an empirical method combined with a GIS. As a benefit to the reader, a link to access the tool that integrates empirical propagation methods with geographical information systems is provided. Therefore, this book is targeted toward engineers and researchers working on different problems related to the calculation of propagation in real scenarios. The book can also be used by graduate students in the fields of antennas, propagation, or radio communication systems, although basic knowledge of antennas and propagation is assumed. The authors would like to acknowledge the invaluable help provided by many people in the development and execution of this book. First of all, we thank all the people who contributed in one way or another to the development of the tool presented in the book: Oscar Gutiérrez and Marian Fernández de Sevilla, and the students Alejandro Gabriel Marrero and Luis Wilmar Sánchez. The authors are also grateful to all the institutions that have supported the development of the research presented in this book through the projects CM/JIN/2019-028, CCG2018/EXP-020 and CCG2015/EXP-042. We also want to thank Artech House for its confidence in our work and its reviewers and editors for their helpful commentaries during the development of this book. Finally, we are sincerely grateful to our families for all the support during these years, especially in those moments when our dedication to the study of propagation has stolen some of the time that could have been dedicated to them. This book would not be possible without them.

1 Introduction This book shows the practical procedure for combining empirical propagation methods with geographical information systems (������������������������������������������������������������ GISs) to obtain radio coverage in open environments. The approach of this book is very practical. It will start with the theoretical explanation of empirical methods and GISs as a basis to develop a real tool that combines both aspects to provide users with a suitable method for wireless network planning in urban areas. The applications of such tools will also be analyzed in this book. In this chapter, the main aspects of the book are introduced.

1.1  Introduction to Empirical Propagation Methods Knowledge of the radio propagation characteristics is essential in the design stage of a wireless communications system that is intended to estimate the optimal locations of the base stations to achieve high transmission ratios and a large coverage area. Ex13

14   Applications of Geographic Information Systems for Wireless Network Planning

perimental measurements can provide very accurate results, but because the coverage area is large and the desired accuracy is high, its use is costly in terms of time and technology. A feasible alternative due to the speed of its implementation is the use of simulation tools based on wireless channel models to estimate the system parameters. The study of the propagation of radio waves can be performed using deterministic models based on Maxwell’s equations to determine the solutions compatible with the imposed boundary conditions. The applications of these models require, in practice, the application of the uniform theory of diffraction (UTD) [1, 2] to compute the received power at a given location. However, the application of deterministic methods requires precise knowledge of the environmental obstacles, which is not always possible. Even when this information can be obtained, the computational cost of the deterministic methods is high due to the complexity of their formulations. Therefore, in several situations, empirical procedures to determine losses or the level of the field strength are preferred. Empirical methods are based on extensive measurement campaigns and a subsequent correlation of measures with the general characteristics of the propagation medium. From these campaigns, closed expressions are derived to obtain an estimate of the propagation loss. Therefore, they are easy to apply, and the knowledge of the environment necessary is lower than in the case of deterministic methods. However, some basic knowledge of the environment is necessary.

1.2  Introduction to Geographical Information Systems A GIS [3, 4] is a computer system that gathers, analyses, stores, checks, manipulates, manages, and visualizes data related to spatial locations on the earth’s surface. A GIS works as a database with geographic information (alphanumeric data) that is associated with an identifier common to the graphical objects of digital maps. In this way, when we point to an object, we can know its attributes and, inversely, when we query a record in a database, we can know its location on the map.



1.3  Advantages of Combining Empirical Propagation Methods

15

The use of this type of system facilitates the visualization of the data obtained on a map to reflect and relate geographic phenomena of any kind, from road maps to systems for identifying agricultural plots or population density. In addition, they allow queries and representations of the results in web environments and mobile devices in an agile and intuitive way to solve complex planning and management problems, becoming valuable support in decision-making processes. The fundamental reason for using a GIS is the management of spatial information. The system allows the information to be separated into different thematic layers and stores them independently, allowing quick and easy analysis and making it easier for professionals to relate existing information through the geospatial topology of objects to generate a new one that we could not obtain otherwise. Many services take advantage of this kind of system because GISs can use any information that includes location. Currently, many GISs exist. Some of them have a commercial purpose, and others are open source.

1.3  Advantages of Combining Empirical Propagation Methods with Geographical Information Systems One of the most important tasks when developing any type of mobile communication system has traditionally been the study of propagation. To accomplish this task, software tools are widely used as an inexpensive alternative to the costly and complex measurement campaigns that must be performed otherwise. For this reason, several software tools [5–9] have been devised based on statistical and deterministic methods. Empirical models assume that the transmitting antennas are in a significant site and that the receiving antenna is shadowed by some barriers, such as mountains and buildings. In these cases, the empirical methods provide suitable estimations. Although the amount of information that these methods require is not as extensive as in the case of the deterministic methods; they need certain information of the scenarios to obtain the required data for the propagation algorithm developed in a software tool. �These specific details are normally acquired from government

16   Applications of Geographic Information Systems for Wireless Network Planning

sources, architectural blueprints, city planners, satellite images, and so forth. Nevertheless, if the computation must be conducted in a rural region, extracting the information can be complicated and even dangerous. In fact, years ago, this geographical information was very difficult to obtain, and therefore, data taken from previous statistical analyses were used. However, this problem can currently be solved because all of the information required can be taken from the data provided by most of the GISs available on the market. Therefore, the design of a tool that combines the use of empirical methods for the calculation of the propagation with the information provided by geographical information systems can improve, in a very inexpensive way, the performance of existing propagation tools. Therefore, such a tool can be used by engineers, academics, and geospatial professionals alike in wave propagation models.

1.4  Structure of the Book After this introduction, Chapter 2 is devoted to a review of the most important empirical methods used to calculate propagation in open environments. The chapter is focused on the geometrical information needed to indicate the necessity of obtaining some geographical information when these methods must be applied to realistic network planning. Chapter 3 reviews the most important GISs. The advantages and disadvantages of every system are analyzed from the point of view of its integration with an empirical propagation method. Chapter 4 fully describes an application that combines a geographical information system with an empirical propagation method. The practical features of this integration are studied in this chapter to allow an engineer to use wisely the tool and even to develop his or her own tool. Finally, Chapter 5 presents a description of the most important applications for the tool described in Chapter 4. Some examples of its uses are depicted in a very detailed way.



1.4  Structure of the Book

17

References [1] McNamara, D. A., C. W. I. Pistorius, and J. A. G. Malherbe, Introduction to the Uniform Geometrical Theory of Diffraction, Norwood, MA: Artech House, 1990. [2] Saez de Adana, F., O. Gutiérrez, I. González, M. F. Cátedra, and L. Lozano, Practical Applications of Asymptotic Techniques in Electromagnetics, Norwood, MA: Artech House, 2010. [3] Information Resources Management Association, Geographic Information Systems: Concepts, Methodologies, Tools, and Applications (four volumes), Hershey, PA: Information Science Reference, 2012. [4] Armand, N.A., and V. M. Polyakov, Radio Propagation and Remote Sensing of the Environment, Boca Raton, FL: CRC Press, 2004. [5] Roullier-Callaghan, A., “A Radio Coverage and Planning Tool,” 6th IEEE High-Frequency Postgraduate Student Colloquium, 2001, pp. 35–40. [6] WINPROP, Software tool (incl, demo-version) for the Planning of Mobile Communication Networks and for the Prediction of the Field Strength in Urban and Indoor Environments, http://winprop.ihf.unistuttgart.de, Jan. 1999. [7] Cátedra, M.F., J. Pérez,, F. Saez de Adana, and O. Gutiérrez, “Efficient Ray-Tracing technique for Three-Dimensional Analyses of Propagation in Mobile Communications: Application to Picocell and Microcell Scenarios,” IEEE Antennas and Propagation Magazine, Vol. 40, April 1988, pp. 15–28. [8] Kanatas, A.G., and P. Constantinou, “A Propagation Prediction Tool for Urban Mobile Radio Systems,” IEEE Transactions on Vehicular Technology, Vol. 49, April 2000, pp. 1348–1355. [9] Ozgun, O., “New Software Tool (GO+UTD) for Visualization of Wave Propagation [Testing Ourselves],” IEEE Antennas and Propagation Magazine, Vol. 58, June 2016, pp. 91–103.

2 Empirical Propagation Methods This chapter reviews the most important empirical methods to calculate propagation in open environments. The chapter mainly focuses on the formulation of these methods to determine the required geometric information for the application of these methods to realistic network planning.

2.1  Fundamentals of the Empirical Propagation Methods The necessary parameter to characterize when the signal in a mobile receiver is obtained is the path loss or propagation loss (L), which is defined by

L(dB) = 10 log

Pt = Pt (dB) − Pr (dB) Pr

19

(2.1)

20   Applications of Geographic Information Systems for Wireless Network Planning

where Pt: transmitted power ht: received power Since the transmitter power is known, if the propagation loss can be obtained, the received power is very easy to calculate at the mobile station. Then, the main objective of a tool that aims to obtain the received power based on the positions of the transmitter (Tx) and the receiver (Rx) is to characterize the propagation loss in a given environment. For a free-space situation, in which there are no obstacles between the transmitter and the receiver, the propagation loss is given by Lu = 32.44 + 20 log d + 20 log f



(2.2)

where f: frequency (MHz) d: distance between Tx and Rx (km) Obviously, this situation does not happen in a real environment, and the loss is modified by different natural (mountains, vegetation) and artificial (buildings) obstacles in the environment. The objective of the so-called propagation models is to predict the propagation loss from the geometric information of the environment. Traditionally, the propagation models are classified into three types: 1. Empirical models: these models are described using equations derived from statistical analysis obtained from a large amount of measurements. These models have closed expressions, which do not require a highly detailed description of the environment. Therefore, these models are very easy to apply. 2. Deterministic models: in this case, electromagnetic techniques are applied to a specific description of the environment. The degree of precision of these techniques depends on the en-



2.2  Okumura-Hata Model

21

vironment description, and the application is sometimes difficult due to the electromagnetic formulation. 3. Semiempirical or semideterministic models: these models are based on equations derived from the application of deterministic methods to generic environments. These models usually require more detailed information about the environment than the empirical models, but since they have closed expressions, their level of difficulty in application is similar to that of the empirical models. This chapter will focus on the empirical and semiempirical methods. These methods are the most suitable to combine with GIS regarding the compromise between the geometric information provided by the GIS and the simplicity of application. Moreover, these methods are truly accurate in almost every situation except for areas that are very densely populated, where the transmitter is very close to the receiver. As mentioned above, these methods provide closed expressions for propagation loss. This chapter will present these expressions for the most important empirical and semiempirical methods. In all cases, it is a modification of the free-space formula including some information related to the geometry of the environment. Therefore, the units of the parameters such as the frequency and distance, are identical to those in the case free-space (MHz and km, respectively). However, all parameters that take into account the geometric information of the environment and the transmitter and receiver heights are measured in meter.

2.2  Okumura-Hata Model The Okumura-Hata model is an empirical model developed from measured data obtained in Tokyo at frequencies of 150, 450, and 900 MHz [1]. The model provides the propagation loss for different environments according to the population density of the environment [2]. For an urban environment, the path loss is

Lu = 69.55 + 26.16 log f − 13.82 log ht − a( hr ) +(44.9 − 6.55 log ht ) log d

(2.3)

22   Applications of Geographic Information Systems for Wireless Network Planning

where f: frequency ht: Tx effective height hr: Rx effective height d: distance between Tx and Rx a(hr): factor of correction by the Rx height This factor of correction depends on the frequency and size of the coverage area. For small- and medium-sized cities, it is

a ( hr ) = (1.1 log f − 0.7) hr − (1.56 log f − 0.8)

(2.4)

For large cities

a ( hr ) = 8.29(log 1.54 hr )2 − 1.1 when f ≤ 300 MHz

(2.5)



a ( hr ) = 3.2(log 11.75 hr )2 − 4.97 when f ≥ 300 MHz

(2.6)

In a suburban environment 2



  f  Lsu = Lu − 2 log    − 5.4  28   

(2.7)

In a rural environment

Lr = Lu − 4.78 log 2 f + 18.33 log f − 4.94

(2.8)

2.3  Cost 231-Hata Model The Cost 231-Hata model is obtained from Okumura-Hata to cover the frequency range of 1,500–2,000 MHz [3]. The path loss in this case is given by

L = 46.3 + 33.9 log f − 13.82 log ht − a( hr ) +(44.9 − 6.55 log ht ) log d + CM

(2.9)



23

2.4  Ibrahim-Parsons Model

where CM = 3 dB in metropolitan centers and 0 dB otherwise.

2.4  Ibrahim-Parsons Model Two different models were derived by Ibrahim and Parsons from measurements in London at frequencies of 168, 445, and 900 MHz in urban areas of 500m × 500m [4]. These areas were characterized by two parameters: the land usage factor (L) and the degree of urbanization factor (U). L is the area factor of occupied buildings, and U is defined as the percentage of building site area occupied by buildings with four of more floors. The models provide the propagation loss for different environments according to the population density of the environment. The two models differ in the procedure to obtain the propagation loss. In the first case, the model is obtained from the multiple-regression analysis of the measured data. The second model begins from the plane earth equation and corrects it using the parameters that influence it. The loss in the first case is L = −20 log 0.7 ht − 8 log hr +



f k  f + 100  + 26 log − 86   156  40 40

  f + 100   +  40 + 14.15 log  log d + 0.265L − 0.37 H + K  156   

(2.10)

where K = 0.087U-5.5 for highly urbanized areas; otherwise, K = 0 f: frequency ht: Tx effective height hr: Rx effective height d: distance between Tx and Rx H: difference in average ground height between the area containing Tx and the area containing Rx The path loss in the second model is

24   Applications of Geographic Information Systems for Wireless Network Planning

L = 40 log d − 20 log ( ht hr ) + β



(2.11)

The factor of correction β is

β = 20 +

f + 0.18 L − 0.34 H + K 40

(2.12)

where K = 0.094U-5.8 for highly urbanized areas; otherwise, K=0

�2.5  McGeehan and Griffiths Model Similar to the second model of Ibrahim and Parsons, the McGeehan and Griffiths model is obtained by modifying the plane-earth equation [5]. The loss is given by L = 120 + 40 log d − 20 log ( ht hr ) + 30 log f + A



(2.13)

where f: frequency ht: Tx effective height hr: Rx effective height d: distance between Tx and Rx A: correction factor, which varies for different environments as follows  45 ± 5  55 ± 5  A= 65 ± 5 75 ± 5

for older cities with narrow, twisting streets for modern cities with long, straight, wide streets (2.14) for suburban areas with some rural areas for open areas

2.6  Atefi and Parsons Model Atefi and Parsons developed a model from measurements taken in London at 900 MHz [6]. The results of the measurements allow



2.7  Sakagami-Kuboi Model

25

the prediction of the dependence of the propagation loss on the Tx and Rx heights, while the effect of the frequency and terrain loss are included according to Okumura-Hata model and EpsteinPeterson diffraction construction [7], respectively. With all of these effects, the propagation loss is L = 82 + 26.16 log f + 38 log d − 21.82 log ht − 0.15 log hr + LD (2.15)

where f: frequency ht: Tx effective height hr: Rx effective height d: distance between Tx and Rx LD: correction term that considers the diffraction loss

2.7  Sakagami-Kuboi Model The Sakagami-Kuboi model uses other geometric parameters that attempt to introduce more detailed information of the environment under analysis [8]. The propagation loss is given by L = 100 − 7.1log w + 0.23 ϕ + 1.4 log hs 2   H  +6.1log H1 −  24.37 − 3.7    log ht  ht 0    + ( 43.2 − 3.1log ht ) log d + 20 log f



+ exp 13 (log f − 3.23)

where f: frequency ht: Tx effective height with respect to Rx ht0: Tx effective height with respect to the ground level hs: average height of the buildings near Rx d: distance between Tx and Rx

(2.16)

26   Applications of Geographic Information Systems for Wireless Network Planning

H: average height of the buildings near Tx H1: average height of the buildings near Rx w: width of the street where Rx is located (Figure 2.1) ϕ: angle formed by the street axes and the direction of the incident wave (Figure 2.1)

2.8  Ikegami Model The Ikegami model [9] can be considered a semideterministic model because it takes the expression for the propagation loss from a two-ray approach, in which we have a nonline of sight (NLOS)�situation, and the geometrical optics (GO) and geometrical theory of diffraction (GTD) techniques are applied [10]. �This application only takes into account the presence of two rays: the diffracted ray at the last edge before Rx (DR in Figure 2.2) and the reflected ray at the next building wall (RR in Figure 2.2). By applying GO and GTD formulations to these two rays, we obtain the propagation loss L = 26.65 + 30 log f + 20 log d



 3 −10 log  1 + 2  − 10 log w lr   +20 log( hB − hr ) + 1 0 log (sin ϕ)

Figure 2.1  Top view of the street scenario.



(2.17)



2.9  Walfisch and Bertoni Model

27

Figure 2.2  Side view of the street including the rays considered in the model.

where f: frequency ht: Tx effective height hr: Rx effective height d: distance between Tx and Rx w: width of the street where Rx is located (Figure 2.1) ϕ: angle formed by the street axes and the direction of the incident wave (Figure 2.1) lr: parameter that depends on the reflection coefficients of the building faces. Typically, 3.2 in the UHF band

2.9  Walfisch and Bertoni Model Walfisch and Bertoni developed a semideterministic model [11] that considers the geometry in Figure 2.3 to obtain the expression for the propagation loss. In this case, an NLOS situation is assumed, where the main mechanisms contributing to the propagation are the diffractions at the top of the closest buildings to Rx. Other possible contributions (multiple reflections and diffractions) are considered negligible. This situation is particularly suitable for North American cities, where the streets have an almost uniform width, and the buildings are very high; therefore, Rx is

28   Applications of Geographic Information Systems for Wireless Network Planning

Figure 2.3  Side view of the street including the parameters considered in the model.

always placed under the building. In this situation, the propagation loss is given by L = 89.55 + 21 log f + 38 log d − 18 log ( ht − h )   d2 + A − 18 log 1 −   17 ( ht − h ) 





(2.18)

where f: frequency d: distance between Tx and Rx ht: Tx effective height h: average height of the buildings near Rx A: factor of correction by the effect of the buildings, which is given by  b  2 2 A = 5 log   + ( h − hr )  − 9 log b  2  



  2 ( h − hr )   +20 log tan −1   b    

where



(2.19)



2.10  Xia and Bertoni Model

29

hr: Rx effective height b: row spacing (Figure 2.3)

2.10  Xia and Bertoni Model The Xia and Bertoni model is an improvement of the previous model and allows Tx to also be above the rooftops [12]. In this case, the propagation loss is the sum of three terms: L = L0 + L1 + L2



(2.20)

where L0 is the free-space propagation loss in (2.2), and L1 is the loss caused by the last diffraction produced below the rooftop level. L2 considers the multiple diffracted fields produced by the rooftops at the last edge immediately before Rx. L1 is obtained from the GTD formulation as  D2 (θ)  L1 = 10 log   π k cos ϕ r 



(2.21)

where r: distance from the edge to Rx (Figure 2.4) k: wave number θ: angle formed by the diffracted ray and the horizontal (Figure 2.4) ϕ: angle formed by the street axes and the direction of the incident wave (Figure 2.1) θ: angle formed by the diffracted ray and the horizontal (Figure 2.4) D(θ): GTD diffraction coefficient given by



D (θ ) =

1 1 − θ θ + 2π

(2.22)

30   Applications of Geographic Information Systems for Wireless Network Planning

The above expression is not valid for low values of θ because the GTD coefficients cannot be applied to that situation. This situation corresponds to the case in which the diffracted ray is in the edge transition zone, and the uniform theory of diffraction (UTD) coefficients must be used [10]. Finally, L2 is derived from the application of the physical optics (PO) to the multiple knife-edge diffraction. The expression is

L2 = 20 log Q ( g p )

(2.23)

Q ( g p ) = 3.502 g p − 3.327 g p2 + 0.962 g p3

(2.24)

where

where gp is a dimensionless parameter given by

 H  g p = tan −1   1000 d 

b cos ϕ λ

(2.25)

2.11  Cost 231-Walfisch-Ikegami Model The Cost 231-Walfisch-Ikegami model was proposed during the COST 231 project as a combination of Walfisch-Bertoni and Ikegami models to improve the path loss prediction, particularly for European cities [3]. Similar to the previous case, the propagation loss is the sum of three terms:

L = L0 + L1 + L2

(2.26)

where L0 is the free-space propagation loss (2.2), and L1 is the loss caused by the last diffraction produced below the rooftop level. L2 considers the multiple-diffracted field produced by the rooftops at the last edge immediately before the Rx. L1 is expressed as

L1 = −16.9 − 10 log w + 10 log f +20 log ( h − hr ) + L11 ( ϕ)

(2.27)



31

2.11  Cost 231-Walfisch-Ikegami Model

Figure 2.4  Side view of the street including the parameters considered in the model.

where w: width of the street where Rx is located (Figure 2.1) f: frequency hr: Rx effective height h: average height of the buildings near Rx ϕ: angle formed by the street axes and the direction of the incident wave (Figure 2.1) and



−10 + 0, 3571 ϕ  L11 ( ϕ) = 2.5 + 0.075 ( ϕ − 350 )  4 − 0,1114 ( ϕ − 550 ) 

0 < ϕ < 350 350 ≤ ϕ < 550 550 ≤ ϕ ≤ 900

(2.28)

Finally, the expression for L2 is

L2 = L21 + k a + k d log d + k f log f − 9 log b

(2.29)

where b is the row spacing (Figure 2.3), and

 −18 log (1 + ht − hr ) L21 =  '0

ht ≥ hr ht < hr

(2.30)

32   Applications of Geographic Information Systems for Wireless Network Planning

54  k a = 54 − 0.8( ht − hr )   54 − 0.4 d ( ht − hr )



ht ≥ hr ht < hr ht < hr

18  kd =  15 ( hB − hR ) 18 − hR 



∧ d ≥ 0.5 m ∧ d < 0.5 m

ht ≥ hr



(2.32)

ht < hr

 f  k f = −4 + k f 1  − 1  925 



(2.31)

(2.33)

where ht is the Tx effective height, and kf1 is 1.5 in metropolitan centers and 0.7 otherwise.

2.12  Stanford University Interim Model The Stanford University Interim model was developed for Worldwide Interoperability for Microwave Access (WiMAX) applications in suburban environments [13]. To calculate the propagation loss, the environment is categorized into three different groups with their own features. Type A is a hilly terrain with moderate to heavy tree densities, which results in the maximum propagation loss. Type B is a hilly environment with rare vegetation or high vegetation with flat terrain, which results in an intermediate path loss. Type C is a flat terrain with light tree densities, which corresponds to minimum path loss conditions. The propagation loss is given by  d L = A + 10 γ log   + X f + X h + S (d ≥ d0 )  d0 

where

(2.34)



2.12  Stanford University Interim Model

33

f: frequency d: distance between Tx and Rx d0: reference distance (0.1 km) γ: path loss exponent Xf: correction term for the frequency Xh: correction term for the Rx antenna S: correction for shadowing. This value is 8.2–10.6 with the presence of trees and other clutters on the propagation path. Parameter A is calculated by  4 πd0  A = 20 log   λ 



(2.35)

where λ is the wavelength The path loss exponent (γ) is obtained by  c γ = a − bht +    ht 



(2.36)

where ht is the Tx effective height Constants a, b, and c depend on the types of terrain. The values of these constants are shown in Table 2.1. The correction factors by the frequency and receiving antenna are given by  f  X f = 6 log   2000 



(2.37)

Table 2.1 Parameter Values of Stanford University Interim Model for Different Types of Terrain Parameter Model

A B C

Terrain A

Terrain B

Terrain C

4.6 0.0075 12.6

4 0.0065 17.1

3.6 0.005 20

34   Applications of Geographic Information Systems for Wireless Network Planning

  hr   −10.8 log  2   Xh =   −20 log  hr     2



for terrain type A and B



(2.38)

for terrain type C

2.13  Ericsson Model The Ericsson model is used in the propagation software developed by Ericsson [14] and modifies the Okumura-Hata model by introducing parameters that allow it to take into account different propagation environments. The loss is given by L = a0 + a1 log d + a2 log ht



2 + a3 log ht log d − 3.2 log (11.75hr )  + g( f )  



(2.39)

where f: frequency ht: Tx effective height hr: Rx effective height d: distance between Tx and Rx g(f): factor of correction by frequency, which is given by

g( f ) = 44.9 log f − 4.78 (log f ) 2

(2.40)

The values of different ai parameters are shown in Table 2.2.

2.14  Eibert and Kuhlman Model This particular approach calculates the propagation loss by summing the following items [15]: the propagation loss and other loss in free space (L0), the diffraction loss (Adif), the loss due to land usage along the profile (Alu), and the street orientation loss (Aor). The propagation loss in free space is obtained as follows



35

2.14  Eibert and Kuhlman Model

Table 2.2 Parameter Values of Ericsson Model Environment a0

Rural Suburban Urban



45.95 43.2 36.2

a1

a2

100.6 68.63 30.2

a3

12 12 12

0.1 0.1 0.1

L0 = 69.55 + 26.16 log f + Ad + At + Ar

(2.41)

where f is the frequency; Ad represents is the loss over an open irregular terrain; At and Ar are the loss due to the effective antenna height at transmitter and receiver, respectively. The equations for these losses are





Ad = 10 γ1 log d1 + 10

m+1

∑ γ (log d − log d ) i

i

i −1

i= 2

At = −13.82 log ht

h Ar = −3 − ε log r 3

10  ε = 2hr 20 

(2.42)

(2.43)

hr < 5 5 ≤ hr < 10 hr ≥ 10

(2.44)

In (2.42), di is the range location of obstacle i that causes the diffraction, and γi is the propagation constant for section i of the profile. The constants are retrieved from [16] for the first section and obtained from Fresnel parameter [17] for the other sections. In (2.43) and (2.44), ht and hr are the effective antenna height at the transmitter and the receiver antenna height, respectively. Adif is calculated considering the approach proposed in [18] to estimate the Kirchoff diffraction integrals for a multiple diffraction. An empirical correction is added as follows: 5 dB for one knifeedge, 9 dB for two knife-edges, 12 dB for three knife-edges, 14 dB for four knife-edges, and 15 dB for more than five knife-edges.

36   Applications of Geographic Information Systems for Wireless Network Planning

Alu is computed as shown in [15] using a polynomial approximation, which considers the Fresnel zone clearance analyzes for three different environments. Finally, Aor is obtained as follows: Aor = a0



 10   αmod − 35º  1 + log   25º  dmod   

(2.45)

where αmod: minimum between α and 50º α: azimuth angle difference between the street and the profile dmod: minimum between the maximum of 5 km and d, and 100 km a0 has different values depending on the analyzed area: 2.5 for urban areas, 1.5 for suburban areas, and 0.5 for rural areas.

2.15  Analysis of the Required Geometric Parameters to Apply the Empirical Propagation Methods In any of the models described above, it is necessary to obtain the parameters corresponding to the case under analysis to obtain the propagation loss. The advantage of a tool that integrates empirical propagation methods with GIS to obtain the radio coverage in open environments is that the user does not need a priori knowledge of the environment to apply the model and obtain the loss, since all information can be obtained from available data from the GIS. The user must only indicate the position of the transmitter of the receiver, the frequency of operation and the height of the transmitter and receiver above the ground. Regarding the positions of the transmitter and receiver, their exact coordinates are not required because the GIS provides this information when the user clicks on the map. However, the positions can be introduced using the exact coordinates if the user estimates it to be necessary. Chapter 4 will describe the procedure of introducing these data in more detail. When this initial data has been introduced, the remaining required parameters can be obtained from the GIS through the



2.15  Analysis of the Required Geometric Parameters

37

corresponding application program interface (API) (see Chapter 4). Since the API enables the user to have the exact coordinates of the transmitter and the receiver, it is easy to obtain parameters, such as the distance, effective antenna transmitter and receiver heights, and angular orientation between both. These parameters are required by all models. For other parameters that are more specific for a given model, it is necessary to make the corresponding extraction from the information obtained from the GIS. If the model must know some parameters of the obstacles between the transmitter and the receiver, the necessary information can be obtained in a different manner depending on whether the obstacles are natural (mountains) or not (buildings). In the first case, most of the GISs can trace a profile of the terrain between the transmitter and the receiver. From this information, it is easy to obtain the height of different natural obstacles and their distances from the transmitter. This information also allows the classification of different environments according to the density of natural obstacles between the transmitter and the receiver. For buildings in an urban environment, not all GISs in the market provide information about this kind of obstacles. If this information is provided, it is very easy to obtain the parameters associated with the urban environment when the propagation model needs them. From the knowledge of the buildings near the transmitter and receiver, it is very easy to obtain the average values of the building heights and street widths, which are parameters that the propagation models use in their formulation. Similar to natural obstacles, the density of the buildings enables the tool to classify the urban environment if the model requires this information. If the GIS does not provide information about the buildings, the information must be introduced in the tool and can be normally acquired from government information, architect blueprints or city planners. In this case, the user must know this information to introduce it to the tool. Therefore, if these parameters are very important to the model, it is better to select a GIS with this information when designing‑ the tool to decrease its dependence on the information provided by the user. Therefore, it can be concluded that all the presented propagation models require more or less detailed information for their

38   Applications of Geographic Information Systems for Wireless Network Planning

application. It is clear that the accuracy of the model depends, in a certain way, on the accuracy with which this information is introduced in the model. The use of information provided by the GIS enables one to automatize the process, increase the accuracy and avoid the dependence on previous knowledge of the user about the environment.

References [1] Okumura, Y., E. Ohmori, T. Kawano, and K. Fukuda, “Field Strength and its Variability in VHF and UHF Land-Mobile Radio Service,” Review of Electrical Communication Laboratory, Vol. 16, Sep.–Oct. 1968, pp. 825–873. [2] Hata, M., “Empirical Formula for Propagation Loss in Land Mobile Radio Services,” IEEE Transactions on Vehicular Technology, Vol. 29, Aug. 1980, pp. 317–325. [3] EURO-COST 231, “Urban Transmission Loss Models for Small-Cell and Micro-Cell Mobile Radio in the 900 and 1800 MHz Bands,” Propagation Models Report No. COST231 TD(90) 119, Vol. 1, Sep. 1991. [4] Ibrahim, M. F., and J. D. Parsons,, “Signal Strength Prediction in Built-Up Areas. Part 1: Median Signal Strength,” IEE Proceedings, Vol. 130, Part F, No. 5, 1983, pp. 377–384. [5] McGeehan, J. P., and J. Griffiths, “Normalised Prediction Chart for Mobile Radio Reception,” Proceedings of the 4th International Conference on Antennas and Propagation, 1985, pp. 395–399. [6] Atefi, A., and J. D. Parsons, “Urban Radio Propagation in Mobile Radio Frequency Bands,” Proceedings Communications 86, pp. 13–18. [7] Epstein, J., and D. W. Peterson, “An Experimental Study of Wave Propagation at 850 MHz,” Proceedings of IRE, Vol. 41, No. 5, 1953, pp. 595–611. [8] Garg, V. K., and J. E. Wilkes, Wireless and Personal Communications Systems, Upper Saddle River, NJ: Prentice Hall, 1996. [9] Ikegami, F., S. Yoshoida, T. Takeuchi,, and M. Umehira, “Propagation Factors Controlling Mean Field Strength on Urban Streets,” IEEE Transactions on Antennas and Propagation, Vol. 32, Dec. 1984, pp. 822–829. [10] Saez de Adana, F., O. Gutiérrez, I. González, M. F. Cátedra, and L. Lozano, Practical Applications of Asymptotic Techniques in Electromagnetics, Norwood, MA: Artech House, 2010. [11] Walfisch, J., and H. L. Bertoni, “A Theoretical Model of UHF Propagation in Urban Environments,” IEEE Transactions on Antennas and Propagation, Vol. 36, Oct. 1988, pp. 1788–1796.



2.15  Analysis of the Required Geometric Parameters

39

[12] Bertoni, H., W. Honcharenko, L. R. Maciel, and H. H. Xia, “UHF Propagation Prediction for Wireless Personal Communications,” Proceedings of the IEEE, Vol. 82, Sep. 1994, pp. 1333–1356. [13] Erceg, V., L. J. Greenstein, S. Y. Tjandra, S. R. Parkoff, and A. Gupta, “An Empirically Based Path Loss Model for Wireless Channels in Suburban Environments,” IEEE Journal on Selected Areas in Communications, Vol. 17, July 1999, pp. 1205–1211. [14] Milanovic, J., S. Rimac-Drlje, and K. Bejuk, “Comparison of Propagation Model Accuracy for WiMAX on 3.5GHz,” Proceedings of the 14th IEEE International Conference on Electronic Circuits and Systems, 2007, pp. 111–114. [15] Eibert, T. F., and P. Kuhlman, “Notes on Semiempirical Terrestrial Wave Propagation Modelling for Macrocellular Environments. Comparison with Measurements,” IEEE Transactions on Antennas and Propagation, Vol. 51, Sep. 2003, pp. 2252–2259. [16] Hata, M., “Empirical Formula for Propagation Loss in Land Mobile Radio Services,” IEEE Transactions on Vehicular Technology, Vol. 29, Aug. 1980, pp. 317–325. [17] Parsons, J. D., The Mobile Radio Propagation Channel, London: Pentech, 1994. [18] Whitteker, J. H., “Near-Field Ray Calculation for Multiple Knife-Edge Diffraction,” Radio Science, Vol. 19, Jul.–Aug. 1984, pp. 975–986.

3 Geographical Information Systems Throughout this chapter, GISs and their components will be reviewed. Examples will be provided for the most important GISs and GIS components analyzing their advantages and disadvantages from the point of view of their integration with an application that calculates propagation through empirical methods. How each component relates with the others to create a complete GIS and when to use each of them depending on the requirements of the applications will also be discussed. Additionally, basic knowledge about spatial data formats, map types, coordinate systems, and map projections will be provided. Some recommendations about which data types to use on an application that calculates propagation and how to use them will also be provided. The chapter will conclude listing some of the most known and used GISs creating solid bases from which to begin developing an application that calculates propagation using empirical methods.

41

42   Applications of Geographic Information Systems for Wireless Network Planning

3.1  GIS Definition ��A GIS is a computer system that gathers, analyzes, stores, manipulates, manages, and visualizes data linked to spatial locations on a map. A GIS works as a database specialized at managing geographic information. Every piece of data stored contains a spatial reference that locates it over a map, generally a geographic map. In this way, when a location is provided, it is possible to know the data to retrieve depending on its proximity to the location. Inversely, if a record of the database is queried, it will be possible to retrieve its location in the cartography. GISs capabilities are far more diverse than only providing access to the data. They create complex structures that allow faster data analysis, visualization, or edition capabilities, depending on how the data will be used. For example, in the case of having spatial data that is updating constantly, like in a mobile or Internet of Things (IoT) environment, two different storing systems would probably be used: one to keep track of the current state and other to save a sampled historic of the data. In this case, the GIS would be in charge of managing both storage systems transparently to the mobile devices. In a different context, in case of managing raster data like terrain height, the data will be static and probably stored on different files on the file system if its size is too big. In this other case, the GIS will provide fast concurrent read only access to it. A real use case for these systems is to monitor the temperature of a frigorific chamber. In the example IoT devices communicate to a real time database through a high-level API. Asynchronously a back-end program or user coder makes uses of spatial libraries to transform this data to a raster map. This map is combined with a vector map of the building and allows users to receive updated maps with the current inferred temperature of the building. Figure 3.1 represents the architecture of such GIS. ��From the GIS point of view, a map is just a coordinate system. Each type of data lives within a layer, so layers are containers for data with a specific structure. The only restriction of this format is that spatial references of the data need to make sense on



3.2  GIS Architecture and Components

43

Figure 3.1  Example of GIS for IoT.

the map coordinate system. With this structure, GIS can handle many layers of different types of data and integrate them to provide answers to complex spatial questions. Different data types on different layers can be referenced through the coordinate system because the spatial references relate them. Many services take advantage of GISs because they can use any information that includes location to determine local data relations and data patterns by analyzing the spatial references. GISs can be used as decision support systems [1].

3.2  GIS Architecture and Components The GIS definition is quite broad and describes the characteristics of a generic service. The reality is that a GIS can be implemented in many different ways. In fact, some GISs are built to be used alongside others, with a composition pattern, by providing them with functionality. A GIS must allow to store, manage, visualize, and analyze data, but it does not specify how to do any of those things. A new GIS could be borne by using an existing spatial database and building over it some visualization tools, or by taking data provided by a set of users and serving it through a map server. On the other hand, other GIS contain all the components that could be part of a GIS, from data gathering, to its visualization and manipulation.

44   Applications of Geographic Information Systems for Wireless Network Planning

Figure 3.2 presents a graph of a general GIS architecture. This graph will be used throughout this section to organize the explanation of the GIS architecture and components. Depending on the requirements of the application, it will have more or less GIS components, or even all of them. Some examples of successful GIS implementations are provided as a reference [2–5]. 3.2.1  Spatial Databases

Databases are used to store data. Inside a database, at least one of three types of data can be usually differentiated: dynamic data, static data, and historical data. Dynamic data are constantly evolving, changing their stored values. They can grow in size, but they do it at a small rate. Sometimes, dynamic data are stored in a special database that stores data only on random access memory (RAM) and not on the disc, with the intention of achieving faster accesses. However, this is only possible if the amount of data to manage is small enough to fit on the RAM. Dynamic data reflect the current state of the application and are only suited for applications that just need to know the current state of the system in real time. Static data are stored once but read and accessed many times. Static data need to be permanent; the data need to be saved on

Figure 3.2  GIS architecture.



3.2  GIS Architecture and Components

45

disk, so they are still available once the user ends the session, closes the application, or turns off the computer. Even if some catastrophic accident like a cutdown of power were to happen, the database must try to preserve as many data as possible. On spatial databases, static data often represent the geography, the terrain altitude, or other data that should not change. On the other hand, historical data reflect the past and present at different points in time of the application’s state. If it were useful to know how the spatial data has changed over time, this storing system would be the way to go. The data will be permanently stored with both time and spatial references. Social data would be a great candidate to be stored like this. Some of the most important features of databases are transnationality, failure recovery, and data duplicity. These features make databases a reliable tool to storage data that will be changing by preserving their integrity at all cost. Spatial databases are a specific type of database built with the intention of storing and managing spatial data, or data that are linked to location references over a map. These databases are designed to offer better performance when storing or accessing spatial data than general purpose databases. Also, they usually provide special functionality in the form of more advanced queries that support commonly performed geometric operations. A traditional database could be used to store spatial data, but some of these features would be missing [6, 7]. It is common for spatial databases to provide implementations for spatial query algorithms. Some examples of this would be to retrieve every record whose spatial reference is inside a certain geometry or to calculate the distance between the spatial references of two records on the database. Spatial databases fulfill every requirement to be considered a GIS. They can store spatial data, they allow users to access them, manage and analyze them, and they even support data visualization. Nevertheless, all of these actions are performed in a general and low-level way that only developers should have access to. Therefore, to use a spatial database on the backend and allow real users to access it, other tools would need to be built around it, to protect it or to allow a higher-level way to access it. This would

46   Applications of Geographic Information Systems for Wireless Network Planning

be either an edition or visualization tool, a map server or custom user code. 3.2.2  Edition and Visualization Tools

Edition and Visualization tools, often implemented as desktop tools, allow users to visualize and modify spatial data through a higher-level interface compared with the one provided by spatial databases. Today, the user interfaces of edition and visualization tools are implemented in various ways, a webpage being one of the most popular ones. A web server, running locally or remotely, will serve as a front-end that would allow interacting with the spatial data. For the clarity of the diagram, web servers have not been included since they do not have to have a direct relation with spatial data. Web servers are a more general component not restricted to only the GIS context. High-level APIs are implemented to be run on the client’s browser, just like some visualization tools, but there are big differences between them. Visualization tools are software ready to use. They could be customized through an interface designed to do so, but not through their code, because it is usually not accessible. Implementing a custom plugin that integrates with the visualization tool, would be the closest to code-level edition. On the other hand, high-level APIs are meant to be used to create custom code that manages and displays spatial data through a high-level interface. The last point on this introduction to edition and visualization tools is that they can use a spatial map server between them and the data. There is no need for them to directly access raw spatial data files or spatial databases directly. This map server will be transparent to the user and the developers so they would not know that it is even there, it would just be a proxy providing an interface to the data. Therefore, for clarity, this use of map servers has been taken out from the diagram and it is considered to be included inside the edition and visualization tools. Edition and visualization tools are often used by developers or employees and rarely by final consumers. Through these tools, the data are exposed, and any change can be made. Their functionality



3.2  GIS Architecture and Components

47

can often be extended by adding plugin to them. Those extensions could be classified into three categories. There are extensions that allow to import or export data to other formats that are less common or simply that are not supported by the core of the application. Other extensions provide algorithms to analyze the data. The last type of plugin are visualization ones, which provide sometimes better looking, sometimes higher customizable properties, or sometimes more complex data visualizations than the default visualization tool. Algorithms implemented in these tools could be similar to the ones often implemented inside spatial databases, allowing to query for data inside a certain geometry or to ask for the distance between different types of data. Other algorithms treat the data in a higher-level way allowing retrieving higher-level answers. Association relations between data stored in two different layers could be looked for, also the shortest path between two features without trespassing a third one or simply the retrieval of any feature highly related spatially to another one. As code developers, plugins that increase the functionality provided by the tools could be created, fulfilling the necessities to process the data, to visualize them, or to import and export them. In order to do so, spatial libraries that provide general algorithms and data types to manage spatial data could be used. It is worth noting that editing tools might certainly be built with those same libraries available to create the plugins. From the corporate point of view, these tools are ideal to allow employees to manipulate spatial data. Some companies could take great advantages of knowing spatial relations between the data they manage to guide future decisions. In other fields, spatial representations are the input and output of their whole production chain, like in the case of a company that makes weather predictions or a researcher that studies signal loss propagation over a region. In any of the cases in which spatial data are studied and manipulated internally, these applications are great tools to have. Editing and viewing tools are related to data providers, spatial databases, and raw spatial data. They can load local raw spatial data often by previously importing it inside a spatial database.

48   Applications of Geographic Information Systems for Wireless Network Planning

These last two relations are bidirectional because the users could always be able to edit and visualize the data that could be exportable to any of the formats supported by the tool. Nevertheless, the relation with the data providers is quite different. Sometimes, they allow accessing their data not only through a partial or complete download but also through a data stream directly hosted by them. In those cases, the data could be visualized online without the need of a previous download. This access is not bidirectional and could only be used to view the data but not to edit them. Unfortunately, there are few data providers, like OpenStreetMap, that allow data edition. 3.2.3  Map Servers

Map servers can access spatial data in different formats and make them available through a Universal Resource Locator (URL). By using map servers, an interface to access raw spatial data or database stored data is created. This interface can be used internally or can be opened to the users. This is a maintainable and controlled way to provide data, preferred over giving direct access to them. The client-server architecture is quite extended and map servers are no exception to this. They are implemented as a separate application to control and measure user access to the data. Queries that the users are allowed to perform over the data can be declared in the map server interface. By doing so, the users will be able to do simple requests to the map server, which will perform the necessary actions to actually retrieve the data. Users will not have to take care of the complexity of the actions performed by the map server to create the response. The way in which the data are stored is transparent to the users. Map servers are useful to protect the data by giving the users permission to only see and do what they are allowed to. They also create a simple interface to do those allowed actions. Map servers allow high-level control on how the spatial data is accessed. Users, roles, and access policies can be implemented by using some of them. Thus, it is possible that certain users can only access certain information. Another type of restriction often



3.2  GIS Architecture and Components

49

implemented is to limit the number of accesses depending on the user role. This is common when the GIS wants to charge users by the number of requests they do or just to limit this number depending on their role. The most important feature of map servers is that they allow creating a layer of abstraction between the data storage system and the data access. To retrieve some spatial information there will be a single access point through the port and IP in which the server is located. It will not matter if the data are stored over a raw file or inside a database, and it will not even matter the location of the data inside the machine in which the server is running. With redirections from each URL to the data that is meant to refer, all this internal information is occulted to the users. In a nutshell, by using map servers, there will not be a necessity to write database petitions or to know the correct name of the raw files. The information will be queried through high-level petitions using an URL. Most of the map servers are able to export any requested map section, vector or raster, to an image, so just with raw spatial data and a map server, spatial information could be displayed over a webpage. One of the disadvantages of this method is that without any further work, every map presented to the user will be static and they will not be able to interact with its content. To create dynamic content, extra logic in the client side is needed. However, there is no need to worry about that, because this work has already been implemented in high-level APIs. Data providers often allow users to access their data through an online stream. In order to do so, they use a map server. The access point of the server is a set of valid URLs. The basic information that those URLs must provide to the map server are the map in which the information needs be looked for, the desired zoom level of the output, and the desired map tile. The map tile is expressed by two coordinates and determines a specific region of the map within the selected zoom level. By making request to that access points, the data will be retrieved. This method works with any type of data just by changing the data format that encodes the retrieved information.

50   Applications of Geographic Information Systems for Wireless Network Planning

3.2.4  High-Level APIs

High-level APIs implement two different types of functionality with the aim of providing interactive map content to final end users. These APIs can render maps allowing users to interactively inspect the data represented on them. In addition, they provide tools to edit those maps and to display detailed spatial information. Developers can use the APIs to create the user interface and define the user’s interaction workflow with the maps. The APIs will provide enough tools to manage different layers, create custom legends, and handle user actions over the maps. This, for example, could be used to display detailed information when the users click on the map or to restrict the maximum and minimum zoom level, as well as the maximum and minimum longitude and latitude that users can visualize. Also, developers can allow the users to perform local map edition by creating new elements over their local layers like lines, pop-ups, polygons, or embedded pictures or videos. While using those APIs, one of the most important data structures to use will be the layers. This data structures are not the same structures used to store spatial data (this will be discussed later on). In this section, local layers are the ones provided by the API, and stored layers are the ones used to permanently store or to share spatial data. A local layer could be a raster element like an image or a vector layer like a line or a polygon. Layers and elements have a common interface. Each element is treated like a layer to allow actions like stop rendering it on the map without deleting it. To control different elements or layers as a single one they will be grouped in a treelike structure applying the same actions to each one. Since local layers are copies of stored layers, they are editable. However, those changes do not apply to the stored data, they just happen on the local copy. High-level APIs do not implement stored layer edition like the previously mentioned edition and visualization tools. In addition, they cannot access raw spatial data nor spatial databases directly. Their only way to access data is through map servers. To save user data modifications or the spatial data contained in local layers, a custom implementation or an implementation



3.2  GIS Architecture and Components

51

provided by other GIS components is needed. This makes sense since these APIs are meant to provide custom interactive visualization of the data but not full data access to it. Additionally, it is common to use third-party data directly from their map server, which does not allow editing the data it provides. High-level APIs are often implemented by big data providers or built to be compatible them. Data providers and their APIs use common spatial data formats to communicate with each other. �In the case of building a custom API or plugin for one of them, as well as in the case of making public spatial data, an already defined data format should be used, so any other software could use it and understand it right away. 3.2.5  Data Providers

Collecting data is always a hard task and spatial data is no exception, especially if the application to develop has a global scope. Nevertheless, sometimes the data have been already collected and are ready to be injected on the architecture of the application. Current approaches provide a map server to which to connect and receive data streams to be used directly into the application front end. In these cases, the data provider will either provide a high-level API to integrate its data on other applications or provide compatibility with general high-level APIs. The map servers will occlude an undelaying fully featured GIS that the data provider could be using [8]. To not directly rely on third-party map servers, or to be able to modify the data they provide, raw spatial data files need to be downloaded from the data provider. If editing the data files were a requirement, it would be recommended to have a spatial database to store the data and to use edition tools. In case users also needed to edit the data, the creation of a map server would be recommended. Other times, data have not been previously collected. In those cases, it would be nice to share them with others. That can be achieved by letting others download raw spatial data files that contain the data using one common spatial data format to encode them. If the data is in a nonconventional format, libraries, or plugins to convert them to other formats must be created to let APIs

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and tools understand and manipulate them. Of course, the use of standard formats is preferred. �Another option is to provide access to the data through a stream using a map server and a standard data format. The same steps would be needed to just use the data internally, so why not share them? 3.2.6  User Code

Depending on the managed data, and depending on the GIS architecture, user code may have three possible definitions. The code that developers create as users of other already implemented GIS components is referred as user code. In addition, user code could be the code that other users need to create in order to use the data and services that they provide. User code could be plugins to data edition and visualization tools. In this case, the libraries of the edition and visualization tool will be available, as well as other third-party spatial libraries. In this case, the code would need to conform to the communication protocols of the tool that the plugging has been created for. If the plugin needs to be compatible with different edition and visualization tools, it would be nice to create an interface that does not depend on any of them. Through that interface, the core of the plugin would be separated from the tool-dependent code. This is a good practice and should be done even if initially the plugin was not intended to be compatible with different tools. User code could also be a custom map server. Currently, creating a web server is quite accessible for any former or new developer. In the case of managing custom spatial data, it could be easier to create a custom map server that conforms to a well-defined spatial communication data protocol. A server could be used to implement that protocol making the data accessible and usable from the high-level APIs or other maps server that would redirect requests to the one being created. Additionally, user code could be code that uses high-level APIs and spatial libraries to provide interactive map experiences to other users. This is the most common case of user code since interactive maps can provide so much more information and features than static maps.



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3.2.7  Full-Stack Frameworks

Full-stack frameworks consist of highly cohesive pieces of software that are meant to work together. Sometimes, data providers also develop edition and visualization tools that use an internal spatial database and a web server to which the application can connect. Also, the application can use their high-level API if one is provided to display its data to the final end users. Fortunately, even though these full-stack frameworks do exist providing great developer and user experiences as well as great performances, they usually are compatible with other general-purpose software that are meant to manage spatial data from many different providers. Therefore, different pieces of the frameworks can be used alone or with other components not provided by the framework, instead of using the whole pack. It just depends on the application needs and on the GIS architecture. To get the big picture of how full-stack frameworks for GIS and GIS in general are intended to be used, Figure 3.3 shows how different role-based actors could be injected into the general GIS architecture.

Figure 3.3  Role-based actor in GIS Architecture.

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3.3  Spatial Data Manifestations Until now, GIS architecture and its components have been explored. As mentioned above, all these components manage spatial data. In this section, the most common formats in which data are stored and managed will be explained. Data formats are the physical data representation. The data will be stored and shared with this shape. Data formats have a grammar and a syntax that represents the data. They can be raster formats or vector formats. On the other hand, maps are the logical representations of the data; they have a purpose and information to give to the user. Many formats can be used to represent the same map type; the specific one to use will be determined by the structure of the data. 3.3.1  Raster Data

Raster data formats are the simplest data representation. They consist on a grid in which each cell contains a value. Every cell has the same fixed dimensions over the map. Many raster layers can combine their data easily and rapidly, especially when every layer shares the same grid size. Raster data formats are great to represent high-density data or data with high variation frequency. Some examples of these are satellite image maps, height maps, and propagation maps. For those cases, there is one value for each point on a grid with the dimensions of the sampling precision. A high sampling precision will create raster representations of high-quality information, but they will be highly difficult to share and manage due to the big size they can get to. Additionally, since the format is so simple, some data relations would be hard to represent or even impossible to do in just two dimensions. The main problem with raster formats is that they can reach enormous sizes when the sampling is low and the cell content is big. To make the file usable, there are different techniques that need to be considered. Raster format can be rescaled by losing quality and precision. They also can be compressed using different



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methods. Some of the file formats that are going to be presented are compressed formats with different compression rates. To do not lose precision, there is a cleverer way to manage raster files. Raster files can be divided into pieces. Instead of having a huge file impossible to fit in RAM, smaller files and a data structure that linked them together could be used. Usually, that data structure will be a quad tree [9]. ��In this section, the most common formats in which raster data are stored will be explained. 3.3.1.1  ESRI Grid

ESRI grid is raster format developed by Environmental System Research Institute (ESRI). Data files that can be either binary or American Standard Code for Information Interchange (ASCII). 3.3.1.2  GeoTIFF and �Cloud Optimized GeoTIFF

Cloud optimized GeoTIFF (COG) is the industry standard data file. It is often used in tele-detected information like height. 3.3.1.3  JPEG 2000

JPEG 2000 was created by the open geospatial consortium (OGC). This file format is compressed reaching compression rates of 20:1 with minimum information loss. 3.3.1.4  MrSID

MrSID stands for multiresolution seamless image database. It is an open compression standard for raster data with an approximate compression rate of 22:1. This format is widely used by its compression rate, which is achieved with minimum to none information loss by using a nonfix grid size. 3.3.1.5  ECW

ECW stands for enhanced compression wavelet. It is a proprietary compressed file format with high compression rates between 10:1 and 50:1 but also high loss of quality. 3.3.1.6  ASCII

ASCII data format is useful to represent small amounts of data that need to be easily understand by a human in its raw representation.

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3.3.1.7�  ERDAS IMAGINE

ERDAS IMAGINE is a proprietary raster format to encode spatial referenced images. 3.3.1.8  GEOPACKAGE

GEOPACKAGE is a universal file format used to represent raster and vector spatial data. It is built to work with SQLite databases. This allows exporting the data and prebuilt indexes over it from one database to another by just sharing only one file. 3.3.1.9  MBTiles

MBTILES is a semi indexed file format created by MapBox. It also works with SQLite databases. This file format targets duplicated data by detecting patterns and groups of cells with the same content and indexing them in order to reduce the size of the file. ��3.3.2  Vector Data

Vector data provides a geometrical representation of the data. The information is encoded in points, lines, and shapes that have their own attributes. This allows representing sparse spatial data in a compact format, as well as representing complex relations. The main disadvantages of this data format are its high complexity and the difficulty to create efficient algorithms that perform certain operations over it. Sometimes, in order to process vector data, it is required to load it over a data structure different from the one used to encode it like a quad tree or a hash map. Vector data can create more complex data representation than raster data. They are better suited to complex relations between data like hierarchy and composition. Vector data offer object-oriented representations of the data to create these relations. In addition, they can be rescaled without losing precision. Next, the most common formats in which vector data are stored will be explored, as it has been previously done with raster data. ��3.3.2.1  OSM

OpenStreetMap (OSM) is a file format that encode OSM data.



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3.3.2.2  SHAPEFILE

SHAPEFILE is an extended vector file format developed by ERSI that is easy to convert to other formats. It is usually used as an intermediate file format to convert between two other ones when direct conversion is difficult. 3.3.2.3  Spatial Databases

Spatial databases use different file formats to encode vector spatial data (PostGIS, Oracle spatial, mySQL spatial, MongoDB spatial, etc.). These formats are developed to allow multiple simultaneous requests and are easier to index to allow faster data access. 3.3.2.4  CSV and GeoCSV

Comma separated value (CSV) and geoCSV files store commaseparated values that represent data in table like format. 3.3.2.5  DWG

Drawing, better known as DWG, is a file format used in AutoCAD, a vector editor and visualization tool designed by Autodesk. 3.3.2.6  DXF

Drawing exchange file (DXF) is often used as a vector data format compatible with most vector editing and visualization tools. 3.3.2.7  DGN

DesiGN (DGN) is a different 2D and 3D CAD format, in this case developed by Bentley Systems. 3.3.2.8  IGES

Initial graphics exchange specification (IGES) allows representing 2D end 3D geometries using a set of 80 ASCII characters. 3.3.2.9  GML

Geographic markup languages (GML) that uses the OGC defined tags for the XML syntax to represent vector spatial data. 3.3.2.10  GPX

Global positioning system exchange format (GPX) is a file format to represent global positioning system (GPS) data with XML syntax. It mainly describes waypoints, tracks, and routes.

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3.3.2.11  GeoJSON

Geographic Javascript Object Notation (GeoJSON) is a spatial vector exchange format built to be easy to use from the JavaScript language. 3.3.2.12  TopoJSON

Topographic JavaScript Object Notation (TopoJSON) is built on top of GeoJSON. It is an extension that allows a faster representation of topology. 3.3.2.13  GeoRSS

Geographic rich site summary (GeoRSS) is a layered file format for spatial vector databases on points of interest and annotations. It is often used to encode complex maps with multiple layers. 3.3.2.14  KMZ

Keyhold markup zip (KMZ) �was originally built for Google Earth and now is the OGC standard for vector data. It uses extensible markup language (XML) syntax. 3.3.2.15  KML

Keyhole markup language (KML) is an extension of XML to represent 3D spaces. 3.3.2.16�  MapBox Vector Tiles

MapBox vector tiles (MVT) is a binary file format developed by MapBox to store vector tiles. 3.3.3  Map Types

Maps are composed of one or many layers. Each layer is managed through a high-level API without care of its format. Maps represent information with one of the following high-level meanings. Map types create an abstraction layer over the file format in which the data is encoded. Choosing the most appropriate data encoding is related to how the information is going to be distributed and where this information is going to be used and stored. Map types, instead, are related to the meaning of the data and how they are going to be presented to the final-end users.



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Maps, as well as other visualization tools, can deceive the users by not properly representing the data that they are meant to represent. This can be achieved by not using the proper coordinate system or by using an incorrect map type [10–11]. While working with maps or any visualization tool, incorrectly representations of the data must be avoided to do not confuse the visualization. ��3.3.3.1  Raster Images

They are spatial located images that encode the spatial data that are represented by their pixels. They are used to represent dense information with high change frequency. The images have an anchor point around which they are drawn, the height and width of the images are fixed. The images must contain metadata to specify the dimensions of a pixel in map units. The information contained is often the result of sampling a function at fixed intervals. Raster images usually are used to represent terrain height, as well as propagation. Terrain height is taken as an input on algorithms that calculate propagation using heuristic methods. It is an important information when propagation is calculated on rural and urban environments. ��3.3.3.2  Dissymmetric Maps

These maps represent absolute values over map areas. Each area has its own shape and value. Each area can only be linked to one value. The areas and the values can be managed separately. 3.3.3.3  Choropleth Maps

These maps represent density. In contrast with dissymmetric maps, in choropleth maps, the absolute values are divided by the area of the shape they are linked to. The resulting values are the density. Choropleth maps are an option to avoid the illusion created by big areas having big absolute values just because of their size. The opposite thing occurs with small areas that have small absolute values just because of their size. The illusion makes viewers of the map believe that big areas have even bigger absolute values and small areas even smaller than they actually do. This happens because both factors, the area dimensions and the color

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used to represent the value are indicating so. If density is used, the color of the areas mitigates the illusion. Maps, as well as other visualization tools, can easily be used to deceive the user and that should be avoided. The examples (Figures 3.4 and 3.5) represent the same data using different map types. The map type and a coordinate system should be carefully chosen to avoid nontrustful representations of the data. 3.3.3.4  Heat Maps

A type of map in which, in contrast to dissymmetric or choropleth maps, the values represented are not linked to regions. Instead, the values are linked to points in the map. The value represented at each point decays with the distance to the point creating gaussian like fields around each point with a known value. Heat maps infers the value of points in which it is not provided through this mechanism. The color of the representation is linked to a lower or higher value. The creation of a heat map is usually done in two steps. First the inference method is adjusted to properly represent the data. Then, heat maps are translated into raster images in which the

Figure 3.4  Dissymmetric map of the United States population.



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Figure 3.5  Choropleth map of the United States population.

inference has already been done and the representation is fixed for every value on the raster grid. 3.3.3.5  Isopleth Maps

The main element of these maps are isopleth lines or lines that link points in the map with the same value. The values between lines are not known but they can be inferred by the values of the two lines that delimit the region in which the point is. The density of the lines represents the derivative of the values. If the lines are too close, that means that the value changes in the region at a stepper rate than if they are widespread. 3.3.3.6  Dot Density Maps

This map represents density by having more dots in the region in which there are more instances of a certain value. Each dot of the same class should have the same color allowing representing different classes with different colors. The size of each dot should be the same. 3.3.3.7  Graduated Symbol Maps

In these maps, symbols represent classes and its size represents the value of the class. This type of map also has problems with

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different zoom levels. The size of the symbols needs to be scaled with the zoom level of the map. Classes that are not well differentiated spatially should not be represented with this type of map. The symbols will overlap making the map not as clear as it could be with other representations. 3.3.3.8  Road Maps

Roads tracks and streets are represented on this type of map. It is common that each type of row has a fixed representation with a certain width and visual effect depending on its type. Other times, the road, which is a polyline, has metadata about not only its type but its width and direction. 3.3.3.9  Network Maps

Network maps represent relation between different points on a map. There are two main elements, nodes, and links between them. Depending on the information represented, the main element can be the nodes or the links. Nodes are the locations in which the links begin and end. Information that starts on a point and ends on another is often represented with this type of maps because it allows to visually see the flow of the data. The flow could be directional of bidirectional. 3.3.3.10  3D Maps

Usually 3D buildings or other elements with a 3D representation in the real world are represented with this type of map. Elements that could be represented just with two dimensions like symbols or charts should avoid being represented in 3D, leaving the third dimension to elements that need it because they have it on the real world. 3D maps are hard to render and manipulate because they need to take care of the orientation of the camera and the source of light to create shadows that produce a 3D effect. Sometimes, the source of light is fixed and the user can only manipulate the point of view of the camera. Other times, the point of view of the camera is also fixed creating a just a 3D effect without allowing rotations. 3D maps and road maps are often used together to create models of urban environments that are taken as input of an algorithm



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that uses the model to calculate propagation. These propagation algorithms use ray-tracing techniques to calculate diffraction, reflection, and refraction of the signal waves on the buildings [12]. 3.3.3.11  Scattered Information Maps

Scattered information maps are interactive maps that display light structured data over a map. Usually, users can interact with the information displayed (images and videos),and can visualize it by passing their cursor over the information. Other times, this information represents points of interests like stores, relevant buildings, or historical buildings. 3.3.4  Layers

Layers are the elements that contain the data of a map. A map should be seen as a blank canvas that declares the coordinate system to use. Layers are tree like structures that allow managing the data. A common pattern is that every element of a map is contained on its own layer. Then elements of the same kind are also contained on a higher-level layer. At last, elements of the same data type will be contained on another one. Actions will be applied to a layer and will be propagated to each element inside it. A layered data structure will allow to interact with each element through a common interface and treat groups of elements as one. The layers are created with a composition pattern that allows the addition of decorative elements to the layers. These elements transform what the layered data should look like when they are displayed. This has the advantage of separating the meaning of the data from its representation and visualization. The visualization component could also be empty, leaving the representation to the data itself. That it is not ideal, but it could be done. Layering elements of a map will help to make them reusable through different maps. If the map’s elements have been layered, creating a new map that uses some of those layers would be much easier than if all the elements are mixed up in a single layer. The composition pattern applied to the layers in Figure 3.6 is also used by high-level APIs to manage the data that is placed on their maps.

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Figure 3.6  Layers data pattern.

��3.3.5  Coordinate Systems

Coordinate systems determine the meaning of the spatial references. A spatial reference needs to be unique for each point on the Earth’s surface. The coordinate system must consider the Earth as a 3D surface in the large scale to avoid or minimize distortions on the measurements. If the working dimensions are small enough, this could be avoided using a local Cartesian system. Those Cartesian coordinates would need to be translatable to a global coordinate system through an anchor point whose coordinates are known in both coordinate systems. There are different coordinates systems defined and maintained by international associations like EPSG codes. On Postgres databases, the spatial references are called spatial reference system identifiers (SRIDs). �����3.3.5.1�  World Geodetic System

The World Geodetic System (WGS) is the standard global coordinate system. It places points on the Earth’s surface through three coordinates: latitude and longitude expressed in degrees, minutes and seconds, and height expressed in meters. This coordinate system is used in many applications like the Global Positioning System (GPS). Since this coordinate system considers the Earth as a 3D surface, it is not possible to directly calculate distances as straight lines with it. To know the distance between two points, the Earth’s curvature must be taken into account when points are far apart [13].



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3.3.5.2  Map Projections

The global coordinate systems use 3D coordinates, but maps are rendered as 2D planes. Map projections are used to translate the tridimensional surface of the Earth to a bidimensional plane. There are different approaches to do so, and each one will have more or less distortions at different points of the representation. The main projection types are cylindrical, semi-cylindrical, conical, and azimuthal. Web Mercator projection represents the surface of the Earth inside a rectangle without gaps by using cylindrical projection. This is the most used projection in GIS. Other projections leave gaps on the sides of the projection (or inside it) to minimize distortions. Maps represented with Mercator projection will show larger the surfaces near the poles as seen in Figure 3.7. Depending on what the maps are going to be used for, a different projection should be chosen to avoid misleading representations of the surfaces [14]. An option often used to minimize the distortions is to have different projections and switch between them depending on the region that is being visualized [15]. Maps are usually visualized on Web Mercator projection, but they use WGS coordinates as spatial references.

Figure 3.7  Each projection produces its own distortions.

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��3.4  Which GIS Components to Use? At this point, the knowledge needed to understand how a GIS works from an architectural point of view, its components, and how they relate to each other should have been acquired. In addition, the difference between how spatial data are stored and how they are presented to the users have been explained. Now it is the time to use that knowledge to start designing the architecture of an application that calculates propagation using empirical methods. In this section, a step-by-step thought process to decide what GIS components to use on the application depending on its requirements is proposed. This through process aims to cover any application size. Questions will be asked and depending on the answer to them, a certain GIS component will be suggested to be used on the application. The first question to be asked is if new data is created or if just already created data is used. If new data is created, which would be the case of an application that calculates propagation, at least a map server component would be needed. Users will connect to the map server to retrieve the data. This will launch a certain propagation algorithm to calculate them. An already built map server could be used to redirect to the algorithms. On the other hand, the propagation algorithm could be embedded on a custom map server implementation, this would mean that the input for the algorithm and its output would need to conform to an already established protocol to transfer spatial data. This would make the data accessible through a standard interface so it could be used by almost any high-level API, visualization tool, or any other user. If new data have not been created, it would mean that data already exists, and so the needs would be to manage and visualize them. The next question to be asked is if internal employees will be editing the data. If so, it would be useful to integrate an edition and visualization tool on the GIS architecture, just for internal use. These tools already exist, are well tested, fully featured, and can provide maintenance. It would be faster and easier to integrate one and learn how to use it than to create one or to edit maps manually.



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Obviously, the next question is if users will be editing that data. If they need to do so, it would be necessary to implement a web server. The web server will occlude how the data is managed internally providing a controlled and uniform access to them. If the data is only edited and accessed internally, this would be desirable but optional. An internal visualization tool could be linked to an internal map server having another, or even the same, but linked to a different map server to be used by the users. There are different situations in which it would be recommended to use a high-level API to visualize, and even allow, the edition of spatial data. If a specific workflow is needed on the application to define custom actions to let users edit the data, one should be used. Edition and visualization tools do not integrate as well as high-level APIs with user code and do not usually allow the level of customization that APIs do. Additionally, to display maps embedded on a webpage, even if they are static, high-level APIs would be the best way to do so instead of using static images. If all the data used can be accessed directly from third party sources and only read access is required, a high-level API that just allows displaying the data could be enough. ��Talking about the back end, the desired option would be to avoid direct use of raw spatial files. The best practice would be to load the data into a spatial database and access the database through a maps server. As it has been previously said, the map server could be omitted if the data is only going to be used internally. There is just one situation when the spatial database could not be mandatory and that is if the data is only read and never modified. In this case, a map server to provide access to them, without the database as an intermediary, would be enough. If this was done, it would be recommended to create an index that speed up the data access, as well as to split the data on files of small size that could fit on the RAM.

3.5  How Will Each Type of Data be Useful? In this section, a list of some data types that could be useful to be used in an application that calculates propagation using empirical

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methods is provided. For each type of data, a brief resume on how that data could be used is also provided. ��3.5.1  Maps

While working with spatial data, maps will be the main information to manage. Maps are used as the main output for the user. They need to be appealing, not distracting and helpful by displaying other information above them in a proper way. Almost all data managed by a GIS should be able to be placed over a map. Maps give context to the data that is being managed and provided. A complete application that calculates propagation could be constructed without using any map. The user could input, for example by a command line, the shape of a region in which to calculate the propagation and the location of the antennas. Then, the propagation could be outputted as a raster image, saved on a specific location on the file system, and the work would be done. However, this would be a poor implementation for the user interface of almost any application. A better approach would be to do those actions through an interactive map. In this case, the user could draw over the map the area where the propagation is calculated. Then, the application could output a raster image that represents the propagation on the region and display it over the interactive map with a certain transparency as seen in Figure 3.8. Maps give context to the data and to the spatial references that are used. It is hard to know each latitude and longitude of each point of interest but recognizing them on a map is much easier. It is recommended to let a third-party library or framework manage map information. Map information is usually based on images that overlap ones over the others with different resolutions based on the height at which the map is being observed. ��3.5.2  Terrain Height

Terrain height maps are used by some of the most precise empirical methods to calculate signal loss using height information. Height information is often represented in a raster format so an important factor to consider is the sampling resolution of the data. The sampling resolution of the terrain height is directly propor-



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Figure 3.8  Maps provide contextual information about the antenna placement.

tional to the sampling resolution of the output been provided as well as the time it will take to calculate the propagation. It is a common practice to provide different terrain height resolutions, ones to maximize performance and others to maximize precision. The first ones would be used to test and develop the algorithms and the other ones to create results to share and compare with real world measurements. ��3.5.3  Roads

Road width and direction also affect how the antenna signal is propagated. For some empirical methods like COST 231, those are important parameters that need to be known in order to implement the algorithm. Roads are often used with 3D building shapes to implement ray-tracing algorithms to calculate propagation. Nevertheless, they could also be useful with empirical methods. For example, the signal loss along a road or the minimum signal loss a vehicle would be receiving along its journey could be calculated. ��3.5.4  3D Building Shapes

Building shapes are used to implement ray-tracing algorithms to calculate propagation. Since they are needed as an input to the algorithm, it would be handy to use them also as an output in which

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to draw the results. Figure 3.9 shows 3D buildings extracted from OpenStreetMap. 3D elements are harder to manage than 2D ones, but most of the high-level APIs and GIS are already capable of displaying 3D information over the maps. 3.5.5  Environment Type

Another key factor to know in order to calculate signal loss is the type of environment in which the signal will be propagating. Depending on the environment, some empirical algorithms will be more precise than others. For example, COST 231 would not be used in a rural environment and instead the Eibert-Kuhlman algorithm [16] would be preferred. Environment type could be used to suggest the best algorithm to calculate propagation over an area or even to select it automatically depending on the environment type. ��3.5.6  Geocoding

In case of building a professional application that would be used by real users, providing geocoding is an essential feature. Providing a search bar in which the user could enter a partially correct

Figure 3.9  3D Building shapes extracted from OSM.



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direction or the name of a point of interest and outputting a list of probably correct places with its latitude and longitude just gives a different look to the application. It is easy to implement; in fact, it is already implemented in most high-level APIs and spatial frameworks. The job of the developer would just be to add the module to the application. This can greatly improve the user’s workflow with a minimum cost for the developers. 3.5.7  Social Data

This data is not usually used by the propagation algorithms, so having it does not have a direct impact on propagation calculations as it happens with weigh information. For example, having access to height information immediately allows using a wider variety of propagation algorithms. Nevertheless, to provide a full and complete application that displays signal loss, it might be interesting to also provide some other tools that allow users to explore the field in which they are placing their antennas. As an example, having access to demographic information like population density, could enhance the quality of the antenna placing just by considering the amount of people that will benefit from having a better signal.

3.6  GIS Comparison As is has been explained at the beginning of the chapter, GIS are made of different components. However, a single component, depending on how it is used, could be considered a GIS itself. A data provider or a visualization and edition tool could have a complete GIS under the interface through which applications interact with it. These interfaces are created through map servers. Other times, the component is not a complete GIS and expects to be used along other components in order to be fully functional. In this section, a list of the most used GISs and GIS components will be provided reviewing their features and limitations, as well as its integration with others.

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3.6.1  Spatial Databases

Spatial databases can be used to manage custom data. They also can manage from the edition and visualization tools and not directly. Spatial databases could also be embedded inside other GIS components. 3.6.1.1  Post GIS

Post GIS is an extension for the open source database PostgreSQL. It allows for a great variety of high-level queries to raster and vector data. It is also compatible with most of the edition and visualization tools, as well as with most of the map servers. Its functionality can be increased by adding plugins. Internally, they use the Geospatial Data Abstraction Library (GDAL) to manage spatial data. 3.6.1.2  MongoDB

MongoDB is a no SQL database that allows the managing of spatial data. This database is not open source and, since its development is quite recent, it is not as well supported by other GIS components like PostgreSQL. Nevertheless, it is easy to use and offers great performance because it can work directly with the GeoJSON format without needing to translate the data to tables to store them. 3.6.2  Edition and Visualization Tools

Edition and visualization tools can have direct access to raw spatial files and spatial databases. In addition, that access could be granted through a map server. They help to manage and visualize spatial data. The reality of edition and visualization tools is that they offer other far more functionality than just editting and visualizing maps. They are fully featured GIS that can do more than just edit and visualize spatial data. They can also manage data by connecting to spatial databases that contain them and creating map servers to provide access to them. Its main difference with full-stack frameworks is that they do not provide their own data and do not provide a high-level API to manage spatial data from user code.



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3.6.2.1  QGIS

Quantum Geographic Information Systems (QGIS) is an open source ��edition and visualization tool that offers a desktop and web interface. QGIS can be used with almost any spatial database as a backend by using the right plugin. Both Post GIS and MongoDB are supported. Different sets of algorithms are provided by different toolboxes to edit and analyze the stored data. Additionally, QGIS is able to visualize third party data without downloading them if they provide access to them through a map server. QGIS can also be used as a map server by combing different layers into a map and publishing it through an URL. 3.6.2.2  GRASS GIS

Geographic Resources Analysis Support System (GRASS) GIS is an open source edition and visualization tool since the OsGeo published its code in 1999. It is a stable and traditional GIS with a long trajectory. It does not integrate well with the newer GIS components. For example, it provides support to work with Post GIS but does not with MongoDB. Also, its interface is only available as a desktop application. It provides a great variety of algorithms to analyze spatial data. GRASS GIS is fully compatible with QGIS through a plugin that QGIS provides. This means that all the functionality that GRASS has, is directly provided from QGIS. GRASS GIS can create a map server to publish the edited maps to the web. Other edition and visualization tools like Jump GIS or uDIG provide similar functionality. 3.6.2.3  Autodesk Geospatial

Autodesk Geospatial edition and visualization tool is not open sourced. It can read data from most of the web servers, spatial databases like MongoDB and Post GSI, and data providers, as well as from raw spatial data files. It provides algorithms to manipulate and analyze spatial data. Its main advantage is that it works great with 3D building visualization. Since it can load and render .dxf files, models can be created for the interior of the buildings as well as the exterior. These features are required to be able to build applications to calculate

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propagation inside buildings that with other option would not be as easy. ��With Autodesk Geospatial ��it is possible to create a map server that publishes the edited maps to the web. 3.6.2.4  Bentley Maps

Bentley Maps edition and visualization tool is not open sourced. Bentley Maps provides similar functionality as Autodesk Geospatial. Both can load data from many different sources, allow the editting and managing the data, as well as analyzing them. These two tools are oriented towards the design of 3D structures and not only to edit and manage maps. Autodesk Geospatial is more focused on providing tools oriented to architects and civil engineers. Bentley Maps also provide features related to this field, but it does also provide other oriented to water and electricity distribution. Since both tools work greatly with 3D models, they are suited to develop applications that calculate propagation on interiors. A map server can also be created with Bentley Maps. 3.6.2.5  Small World

Small World edition and visualization tool is not open sourced. Similar to Autodesk Geospatial and Bentley Maps, Small World is oriented to architecture and industry. It provides the ability to render, edit, analyze, and manage spatial data as well as 3D models. It is also suited to build applications that calculate propagation on interiors. With Small World it is also possible to create a map server that publishes the edited maps to the web. 3.6.3  Map Servers

Map servers create an interface between the data and the users. The data are accessed only through the map server and not directly in order to control how they are accessed and how they are viewed from the exterior. 3.6.3.1  GeoServer

GeoServer is an open source map server that uses open standards (web map service (WMF), web feature service (WFS), and web coverage service (WCS)) to publish and share maps. It allows the



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composing of maps by merging different layers. It also allows the performing of small editions to the rendered maps before sending them to the users, like data conversion or color adjustment. It is possible to redirect third party data, as well as to serve custom spatial data that conforms to compatible data formats. It is compatible with both MongoDB and Post GIS. Geo Server, through different plugins, allows defining user accounts to identify users and create a role-based access to the maps. 3.6.3.2  Map Server

Map Server is an open source map server focused on high performance. Map server is written in C whereas GeoServer is written in JAVA. They provide a caching mechanism to speed up frequent petitions to the data. They provide support for Post GIS but not for MongoDB. 3.6.4  High Level APIs

High Level APIs allow the creation of interactive maps and the defining of a custom workflow through which users will interact with them. They could also be used to display static maps. They are often web-based. 3.6.4.1  Leaflet

Leaflet is a light JavaScript open sourced high-level API that is easy to learn due to its simple syntax. It can increase its functionality with plugins. It can create good-looking user interfaces to display interactive maps provided by different map servers and data providers. It works great with Map Server, GeoServer, OpenStreetMap, MapBox, Google Maps, and ArcGIS. It cannot render 3D maps, but it can display them in semi 3D by not allowing map rotations. One functionality that it lacks is the ability to create images on the client. Every image that it displays needs to be provided and accessible from an URL. 3.6.4.2  Open Layers

Open Layers is an open source high-level API that provides more functionality than Leaflet. Nevertheless, it is a more complicated API with a harder to learn syntax. For a small project, Leaflet

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would be the way to go; but if extra functionality is needed, Open Layers will most certainly be a better option. Open Layers can create images on the client side and display them over the maps which is handy for an application that displays propagation as images on the map. Images can be created and edited directly on the client to then display them on the map without the need of external help. In addition, it can render 3D maps. This functionality is provided because Open Layers uses webGL, an image-processing library that allows editing images from JavaScript. 3.6.4.3  Mapnik

Mapnik is a server-side high-level API that allows the creation of maps with XML syntax. The core of the API is written in C++, but it also provides interfaces for NodeJS and Python. Mapnik can read data from Post GIS, GeoJSON, OSM, and raster files, render them, style them, and then output an image to be displayed. Mapnik can be used to create a custom map server or to display the maps in a desktop application. The main difference between this library and the others is that is does not run on the browser, so it does not take the advantages of web applications such as instant updates or complete independence of the running environment. 3.6.5  Data Providers

Data providers allow the use of the data they provide in two different ways. The data can be exported and then used as a raw spatial file or it can be loaded inside a spatial database. Also, the data can be accessed through the web directly from a high-level API or by redirecting to the map server of the data provider. Since most of the nonopen sourced data providers are included in the category of full-stack framework due to its other functionalities, every data provided covered in this section is open source. The first three data providers are the only ones listed here that present their data as a streaming service through which they can be used without downloading it. If the data provider does not allow this functionality, the data would first need to be downloaded. Then, they could be used through a spatial database or a map server in order to use them and manage them properly.



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���3.6.5.1  OpenStreetMap

Maps, roads, land use, and building data is provided by crowdsourcing. Everybody can update OpenStreetMap’s data that makes this data provider more powerful and flexible than others. Everyday more and more people add more and more data to OpenStreetMap correcting mistakes and updating the data. 3.6.5.2  OSMBuildings

OSMBuildings provides OpenStreetMap’s 3D building data in the GeoJSON format instead of OSM format. 3.6.5.3  Open Elevation

Open elevation publishes shuttle radar topography mission (SRTM) terrain elevation data. This data has a resolution of 1 second (30 meters) and is collected by NASA. Since the project is open source, the code is available to implement a custom map server that publishes these data. 3.6.5.4  Natural Earth Data

Natural earth data provides cultural and physical sematic maps in raster and vector formats with different resolutions. 3.6.5.5  ESRI Open Data

ESRI open data offers a great set of open source spatial datasets provided from different organizations. The datasets topics are diverse, but they mainly contain social and environmental data of concrete regions. 3.6.5.6  SEDAC

NASA’s socioeconomic data and applications center (SEDAC) provides global socioeconomic data: agriculture, climate, conservation, governance, hazards, health, infrastructure, land use, marine and coastal, population, poverty, remote sensing, sustainability, urban, and water. 3.6.5.7  UNEP Environmental Data Explorer

United Nations environmental data explorer’s (UNEP) online database provides access to different global spatial dataset including

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freshwater, population, forests, emissions, climate, disasters, and health data. 3.6.5.8  NEO

NASA earth observations (NEO)provides satellite images and satellite maps. 3.6.5.9  Terra Populus

Terra Populus integrates both census data from over 160 countries around the world, as well as environmental data describing land cover, land use, and climate, from the 1960s to the present. 3.6.5.10  FAO GeoNetwork

The food and agriculture organization (FAO)of the United Nations provides satellite imagery and spatial data to support sustainable development in agriculture, fisheries and food security. 3.6.5.11  ISCGM Global Map

The International Steering Committee for Global Mapping (ISCGM) provides different of social and environmental data like boundaries, drainage, transportation, population centers, elevation, land cover, land use, and vegetation. 3.6.6  GIS Offered as Full-Stack Frameworks

Full-stack frameworks offer a complete GIS experience. They provide data and at least high-level APIs, or edition and visualization tools and spatial databases. Depending on the framework, access is provided to every component or to just a few. �Any of the fullstack frameworks covered are fully open sourced or free to use. 3.6.6.1  Google Maps

Google Maps provide six types of data and three APIs to work with them. The data management is hidden to the users. The data provided are maps, street view images, road information including routing and online traffic, 3D building data, geocoding, and localization. The provided data are updated regularly and maintained by Google. The road information and the geocoding data have exceptional quality.



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Google Maps data can be used from other APIs like Leaflet or Open Layers, but there are three main APIs that Google provides. There is one API for iOS, another for Android, and another for web applications on JavaScript. With these APIs both interactive and static maps can be created. 3.6.6.2  Apple Maps

Apple has recently published their still-in-development map API for the web. They also have an API to embed their maps on iOS applications. They provide map data, road information, and geocoding. They are currently working on providing Street View like features as well as 3D indoor mapping for buildings. With their APIs it is possible to create both interactive and static maps. 3.6.6.3  ArcGIS

ArcGIS is the biggest spatial framework that is covered in this book. It offers a desktop edition and visualization tool, great integration to use and manage data from spatial databases, raw spatial files, and other data providers. It also offers multiple high-level APIs for different environments and languages, iOS, Android, JavaScript, .NET, C++, Python, and JAVA. Its APIs can access its public data but also its private ones like maps, terrain elevation, geocoding, routing, 3D buildings, social and statistical spatial data, and much more. Its APIs also have access to its spatial data analysis algorithms through a REST API. 3.6.6.4  MapBox

MapBox is the other big giant of spatial data management. MapBox offers similar functionality as ArcGIS but not as much variety of spatial data. It provides APIs for iOS, Android, Unity, and web development. The provided spatial data are maps, terrain height, road information, geocoding, geolocalization, and 3D buildings. They allow creating and editing custom spatial data through a web interface instead of a desktop application. Those maps can then be published with a map server that they will host. Those same maps can be accessed with their APIs but also through other high-level APIs like Leaflet or Open Layers.

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MapBox is capable of managing augmented reality maps through their software development kit (SDK). That SDK allows for object detection and classification, provides real time navigation guidance, displays driver assistance alerts, and detects and maps road incidents. These features make MapBox a great framework to develop automotive applications.

References [1] Murphy, L. D., “Geographic Information Systems: are They Decision Support Systems?” Proceedings of the Twenty-Eighth Annual Hawaii International Conference on System Sciences, Vo. 4, Wailea, HI, 1995, pp. 131–140. [2] Li, G., K. Zhou, J. Wang, L. Sun, Q. Wang, and Y. Qin, “Implementation of Publication System of Geological Data Using Open Source GIS,” The 2nd International Conference on Information Science and Engineering, Hangzhou, 2010, pp. 3765–3768. [3] Xia, D., X. Xie, and Y. Xu, “Web GIS Server Solutions Using Open-Source Software,” 2009 IEEE International Workshop on Open-source Software for Scientific Computation (OSSC), Guiyang, 2009, pp. 135–138. [4] Jing, T., X. Juan, and W. Li, “Open Source Software Approach for Internet GIS and Its Application,” 2008 Second International Symposium on Intelligent Information Technology Application, Shanghai, 2008, pp. 264–268. [5] Zhou, G., J. Lin, and W. Zheng, “A Web-Based Geographical Information System for Crime Mapping and Decision Support,” 2012 International Conference on Computational Problem-Solving (ICCP), Leshan, 2012, pp. 147–150. [6] Parker, A., G. Infantes, J. Grant, and V. S. Subrahmanian, “SPOT Databases: Efficient Consistency Checking and Optimistic Selection in Probabilistic Spatial Databases,” IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 1, Jan. 2009, pp. 92–107. [7] Li, G., and G. Wang, “Research on Optimized Spatial Data Query Algorithm in the Spatial Database,” 2009 International Conference on Image Analysis and Signal Processing, Taizhou, 2009, pp. 292–294. [8] Zhou, W., C. Chi, C. Wang, R. Wong, and C. Ding, “Bridging the Gap between Spatial Data Sources and Mashup Applications,” 2014 IEEE International Congress on Big Data, Anchorage, AK, 2014, pp. 554–561. [9] Wang, C., C. Guo, D. Liu, and Y. Liu, “Research on the Storage Method of Raster Image Based on File Directory,” 2013 International Conference on Information Science and Cloud Computing Companion, Guangzhou, 2013, pp. 595–600. [10] When Maps lie, https://www.citylab.com/design/2015/06/when-mapslie/396761 Accessed: 20 November 2019.



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[11] Monmonier, M., and H. J. Blij “How to Lie with Maps,” University of Chicago PRess, Third Edition, 2016 ISBN-13: 978-0226534213 ISBN-10: 9780226534213. [12] Kurner, T., D. J. Cichon, and W. Wiesbeck, “Concepts and Results for 3D Digital Terrain-Based Wave Propagation Models: an Overview,” IEEE Journal on Selected Areas in Communications, Vol. 11, No. 7, Sept. 1993, pp. 1002–1012. [13] Zhang, Y., and X. Shen, “Approximate Correction of Length Distortion for Direct Georeferencing in Map Projection Frame,” IEEE Geoscience and Remote Sensing Letters, Vol. 10, No. 6, Nov. 2013, pp. 1419–1423. [14} Hong, J., H. Luo, and G. Wang, “The Impact of the Map Projection on China’s Geopolitical Environment,��” 2015 23rd International Conference on Geoinformatics, Wuhan, 2015, pp. 1–8. [15] Jenny, B., “Adaptive Composite Map Projections,” IEEE Transactions on Visualization and Computer Graphics, Vol. 18, No. 12, Dec. 2012, pp. 2575–2582. [16] Zhou, G., J. Lin, and W. Zheng, “A Web-Based Geographical Information System for Crime Mapping and Decision Support,” 2012 International Conference on Computational Problem-Solving (ICCP), Leshan, 2012, pp. 147–150.

4 Description of the Application This chapter presents the implementation of a web-based application to estimate the path loss in outdoor environments. The tool provides the results of a semiempirical formulation in a user-friendly interface. The required information of the environment is obtained from OSM, a GIS, which allows the access to the geographic data required for the calculation of the propagation everywhere easily. In addition, the formulation is applicable to both rural and urban environments. The web application has been implemented in JavaScript using HyperText markup language (HTML5) and Cascading Style Sheets (CSS3) and the open source library Leaflet has been used to include interactive maps (http://propamap.cc.uah. es). The simulation tool has been validated with measurements in a real environment and a good agreement between simulated and measured results has been found as will be shown in the next chapter. Throughout this chapter, the practical features of the application will be described, and the reader will learn how to use the application, as well as how to develop his/her own application. 83

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4.1  Combination of a GIS and a Semiempirical Propagation Method One of the most important tasks when developing any type of mobile communication system has traditionally been the study of the propagation. To accomplish this task, software tools are widely used as an inexpensive option to the costly and complex campaigns of measurements that must be done otherwise. That is why several software tools [1–8] have been devised based on empirical and deterministic methods. This section of the chapter deals with the combination of a particular GIS, OSM, and a particular propagation method, Eibert and Kuhlman, in order to provide a convenient simulation tool able to compute the path losses on a certain area. With this overview in mind, let’s begin this section with a discussion about empirical and deterministic propagation methods. The interest in the development of both propagation methods has grown in the last decades due to the increase of mobile communications traffic. As explained in Chapter 2, traditionally, empirical or statistical methods [9] have been chosen for mobile communications both in indoor and outdoor environments since they are suitable for both macrocell and microcell scenarios. They are simple to implement and are widely used when the accuracy of the data is not a critical requirement. These methods normally consider the data of the scenario under analysis, such as the height of buildings and the width of streets, only from a statistical point of view. As well, empirical models consider that the transmitting antennas are in a significant site, and that the receiving antennas are shadowed by some barriers, such as mountains or buildings. In these cases, the empirical methods provide suitable estimations. On the other hand, deterministic models, also called site specific propagation models, are based on the theory of electromagnetic wave propagation. Unlike statistical models, deterministic propagation models do not rely on extensive measurements, but on knowledge of greater detail of the environment, and they provide accurate predictions of the signal propagation. In theory, the propagation characteristics of electromagnetic waves could be computed exactly by solving Maxwell’s equations. Unfortunately, this approach requires very complex mathematical operations and requires considerable computing power. In other words, due to



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their low computational requirements, two dimensional empirical and semiempirical wave propagation algorithms are indispensable tools for radio broadcasting coverage predictions and terrestrial mobile communications in the very high and ultrahigh frequency bands. This is why the web-based application described in this chapter relies on a semiempirical method. The progress and spreading of new GISs, as reported in Chapter 3, has led to include them in most of the simulation tools that have been recently developed [10, 11]. In our case, OSM has been selected to provide the geographical data required to carry out the calculations of the propagation model. OSM is a collaborative project to create editable maps that was launched in July 2004 inspired by the success of Wikipedia. A study on the activity of its members showed that approximately 1.2 million nodes and 130,000 paths are added daily [12]. Especially in metropolitan regions, many building data have been added recently. The data from OSM are available for use in both traditional applications, like its usage by Facebook, Geocaching, Craigslist, OSMAnd, MapQuest Open, JMP statistical software, and Foursquare to replace Google Maps, and more unusual roles like replacing the default data included with GPS receivers [13]. OSM data have been favorably compared with proprietary data sources [14], although in data quality might vary across the world. Map data are collected from scratch by volunteers performing systematic ground surveys using tools such as a notebook, a handheld GPS unit, a voice recorder, or a digital camera. Then, the data are entered into the OSM database. The availability of aerial photography and other data from government and commercial sources has added important sources of data for manual editing and automated imports. Special processes are in place to handle automated imports and avoid technical and legal troubles. Regarding licensing terms, OSM data were originally published under the Creative Commons Attribution ShareAlike license with the intention of promoting redistribution and free use of the data. In September 2012, the license was changed to the Open Database License published by Open Data Commons in order to more specifically define its bearing on data rather than representation.

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The state of the art related to the study of the propagation shows that the use of digital building and terrain databases is increasingly becoming more popular for propagation prediction in urban areas, as can be seen in [15–18]. The works described in [15] and [16] use a digital elevation model and building databases to analyze the effects of four urban areas on the propagation channels of three land mobile satellite systems, respectively. In [17], an urban propagation model estimates the presence of buildings and obstacles along the signal path using information extrapolated from urban digital maps. Finally, [18] extracts real maps of 2D buildings from OSM to characterize the millimeter-wave propagation in urban outdoor conditions. In [19], it was concluded that the data extracted from OSM can be used to model the radio propagation channel in mobile communication systems. Although currently information on the height of the buildings is not available in its database, the solution of supposing an average height of all the buildings offered reasonable results. Moreover, although there are still many blank spaces, it presents a rapid development and seems to be a very attractive and promising alternative data source. Many authors also believe that it is only a matter of time before information on the height of the buildings is added to the existing information. Another very interesting aspect is that the data is constantly updated; while conventional data sources may be outdated, the OSM project is more flexible and members around the world can include minor modifications to the data set easily at any time. In [20], it is shown that OSM maps are a viable alternative to three-dimensional maps that include more details for the calculation of propagation loss by means of the ray tracing technique. The signal intensity distributions show a great similarity with the results that use more detailed geometric models, except in areas with a low signal intensity in general. It is concluded that the height of the buildings only plays a secondary role when it is established high enough. In [21], the development of a localization algorithm based on the fingerprinting technique in which the environment has been modeled with the OSM data is presented. In [22], the communications channel in an urban environment is characterized through ray tracing simulations using OSM to reconstruct the three-dimensional model of the environment under



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analysis. As can be seen, the interest of OSM has been growing during the recent years. Finally, it is important to remark that there are other many applications �that rely on OSM for core functionality such as navigation, augmented reality, track recording, travel planning, scientific research, and games. 4.1.1  OpenStreetMap

As mentioned before, empirical and deterministic propagation methods need certain information of the scenarios in order to obtain the required data to the propagation algorithm developed in the software tool. This spatial data is generally obtained from several sources (such as architect blueprints, city planners, satellite images, government, etc.). However, when simulations are carried out in rural regions or not well-developed areas, gathering those data can be unprofitable, complicated, and even risky. Lately, emergent technologies offer the possibility of getting the needed data from the internet. Using OSM [23] and the Leaflet library [24] is one possibility. OSM is a cooperative online service based on a project that was focused on the creation of a free editable map of the whole world. The motivations of its implementation are the limitations on use and the unavailability of certain data in some regions of the planet and the arrival of cheap portable navigation devices. Since the map edition in these devices is easy to use and fast to access, OSM has grown a lot and currently it is a good alternative to other map services such as Google Maps. On the other hand, Leaflet is a library written in JavaScript that is very useful to create user-friendly interactive maps like OSM. Its main advantages are that it is open source and that it allows using an API that makes easier the implementation of simulation tools and other applications. As the required information is mostly related with geographical data, OSM becomes a perfect candidate to carry out the development of an appl7ication that computes the propagation taking advantage of Leaflet. As a result, the web-based application utilizes the API of OSM to implement the graphical user interface and calculates the radio propagation by means of a semiempirical method that will be explained in detail in the next section. �Once the simulation outcomes are obtained,

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they are interpreted in the Leaflet graphical user interface, offering a useful tool to understand the provided results. OSM is a wide database that provides data from regions all around the Earth and anyone can contribute and add data (such as the position of a new building, a new road, etc.). This characteristic allows the calculation of propagation losses at any location, in rural and urban environments, with or without previous geographical knowledge regarding the analyzed area. The Open Database License is used to make available the aforementioned information. Next, the reasons why OSM has been chosen as the source of geographical information are listed: 1.� It is very similar to other map providers, such as Google Maps, but it has the advantage of being free. 2. Many map providers allow using some of their services free of charge for personal users, but they set excessive restrictions on the way they are used (printing, screenshots, redistribution of maps, etc.). 3. Normally, map providers do not allow the modification or improvement of their maps. Their APIs only allow new elements to be placed on the base map, but not to correct inaccuracies. Thus, for example, many commercial maps maintain navigation data for cars, but generally do not have data for cyclists, pedestrians, boats, and so on. In addition, the fact that OSM enables collaborative editing globally makes the update much faster wherever there are interested contributors. 4. Other map providers do not allow access to the underlying database in which data are stored; they only distribute rendered tiles. This prevents new and creative uses of your data. OSM not only allows access to the latest rendering because of data processing, but also allows access to the underlying data. 5. OSM integrates in a single database the data that third parties have released, combining in a single place and in a single format data of all types and from countries all over the world: from streets and highways, to buildings and parks,



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shops, nature sites, public transportation routes, power lines, and everything the reader can imagine. 6. The license to use the maps is included within the license of �Creative Commons Attribution ShareAlike 2.0. 4.1.2  Propagation Model

The tool includes a two-dimensional modeling approach that characterizes the communication link between a given transmitter station and a certain receiver position by evaluating the terrain profile along the corresponding great circle connection. The calculation of the path loss has been based on the model published by professors Eibert and Kuhlman [25], presented in Chapter 2, because it uses digital terrain data and all the parameters required for the calculation of the path losses can be obtained from the information provided by OSM. Therefore, their algorithm is a perfect compromise between the information available from the digital data and the accuracy in the loss’s prediction. More specifically, OSM provides information about the distance of the diffraction obstacles, which is one of the most important corrections introduced by Eibert and Kuhlmann in their method. The algorithm also combines the basic empirical propagation curves of Okumura et al. [26] �with terrain adaptive propagation curves derived from Fresnel zone clearance analyses of several terrain subsections. Moreover, a multiple knife-edge diffraction algorithm with approximate evaluation of the Kirchhoff diffraction integrals is applied when the line of sight is obstructed. A robust algorithm for determination of effective transmitter antenna heights further improves prediction quality, and land usage along the terrain profile different from open terrain is considered by a multilevel Fresnel zone blockage evaluation.

4.2  Technologies Used The technologies used to develop the web application are JavaScript, CSS3, and HTML5. They have been selected because the application must be compatible with mobile devices. Each one of them is used for a different objective: HTML is used to generate

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the content, cascading style sheets (CSS) for the design of such content, and JavaScript to give life to the content and convert it into an interactive web application. HTML is a markup language that is used for creating web pages. It is based on elements with attributes and content that shape the elements. CSS is used to define the presentation of the HTML code, and most of the stylesheets are defined by the Leaflet library. The language chosen for the development of the application has been JavaScript, since it is a lightweight web application that should run on all modern mobiles. JavaScript is an object-oriented, dynamic, and weakly typed programming language, and a dialect of the ECMAScript standard. Leaflet is the leading open source JavaScript library for mobilefriendly interactive maps. It is about 38 Kb in size and has all the mapping features that most developers need to interact with the maps. �It makes it easy to develop user-friendly mobile applications that include maps providing a high performance because it has all the mapping features most developer ever used. Leaflet is compatible with mobile and nonmobile systems and can be enhanced with a variety of plugins. It has been designed with simplicity, performance, and usability in mind. It works efficiently across all major desktop and mobile platforms, has a pleasing, easy-to-use and well documented API and a simple, readable source code. Nominatim is a different tool that has been used for searching data in OSM by name and address (geocoding) and to generate synthetic addresses of OSM points (reverse geocoding). It is basically a search engine for OSM data. Each result comes with a link to a details page where you can inspect what data about the object is saved in the database and investigate how the address of the object has been computed. Within our application, it is mainly used to perform searches by name of cities. JavaScript object notation (JSON) is a lightweight data exchange format that is used to manage input and output data. It uses human-readable text to transmit data objects consisting of attribute-value pairs and array data types (or any other serializable value). It is a very common data format used for asynchronous browser-server communication, including as a replacement for XML in some Asynchronous JavaScript and XML (AJAX) style



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systems. The simplicity of JSON has led to the generalization of its utilization, particularly as a good alternative to the XML in AJAX. The main benefit of JSON over XML is the simplicity when writing a parser. JSON texts are quickly parsed thanks to the eval() function, which has been instrumental in getting JSON to be accepted by the AJAX developer community because of the ubiquity of JavaScript in almost any web browser. In the application, all output or input data use this format, due to its simplicity. The front-end framework Bootstrap is used to develop a graphical user interface application that supports all screen sizes. Bootstrap is an open source toolkit for developing with HTML, CSS, and JavaScript. It is focused on web page design, since it is based on HTML and CSS, but also contains JavaScript extensions. One benefit of using this technology is that the web-based application can be run in any browser of any device. However, its main advantage is that drastically reduces the time in which the visual part of the application is developed, since it contains many interactive components such as buttons, icons, tables, and pop-ups. On the other hand, it offers a 100% mobile-friendly design, so the pages created with this framework using its responsive design part are restructured very easily when considering small screens. Finally, the MapQuest server is used to obtain the heights required in each analyzed point to obtain the propagation losses. It is also used to find the shortest route between two points (transmitter and receiver) in the trajectory-route mode. It allows obtaining three different routes depending on the selected vehicle: car, bike, or by foot.

4.3  Main Features of the Application Figure 4.1 shows the main screen of the application. At the top of the screen, the operating mode (coverage mode in this case) is displayed. Six different modes can be selected, as can be seen in Figure 4.2: Coverage mode, Point-to-point mode, Trajectory mode, Distance mode, Height mode, and Trajectory route mode. They can be selected by clicking on the MORE button. Under that button, the user can input the name of the place where he/she wants to perform the simulations (New York in this case). Latitude and

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Figure 4.1  Main screen of the application.

Figure 4.2  Selection of the six operating modes available.

longitude can also be indicated. Once they are set, the user must click on the SEARCH button to update the visualization of the map. If the simulations need to be performed in the area where the user is, the Find me button at the bottom of the screen can be clicked. Note that above that button, the latitude and longitude coordinates are continuously being updated when the user moves the mouse. In addition, the map includes three buttons to control the navigation on the map: zoom out, zoom in, and full screen. They are located at the left part of the map. Three more options can be seen at the top right part of the screen: Information, Load File, and Configuration. When the user clicks on the first one, a new panel where a brief summary of each operating mode is shown. �The Load File option allows loading the results of a simulation that has been previously saved. Finally, when we click on the Configuration button, a new panel is shown. Figure 4.3 displays its content. There, the user can set the transmitter and the receiver heights in meters, the frequency in megahertz, and the distance (given in kilometers) from the transmitting antenna to



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Figure 4.3  Configuration options.

the furthest point where the path loss is desired to be computed. The way in which the coverage is graphically shown (by means of colored squares or circles) can also be set using the option figure, as shown in Figures 4.4 and 4.5. Next, the functionality of every option available is described:

Figure 4.4  Example of the results of the coverage mode selecting the squares in the figure option.

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Figure 4.5  Example of the results of the coverage mode selecting the circles in the figure option.

1. Point to point mode: This option provides a point-to-point simulation in which the path losses between the transmitter and the receiver are obtained. The results are shown over the land profile, as can observed in Figure 4.6. If the user clicks on one of the analyzed points (as shown in the map), the information regarding elevation, latitude, longitude, distance from the antenna, and path losses is shown. Obstacles are represented by black squares and intermediate points are represented by white squares. A point is considered to be an obstacle when its height is higher than the imaginary line that links the transmitter and the receiver considering the profile graph. 2. Coverage mode: This option computes the coverage in the region near the transmitting antenna. Here, the location of the receiving antenna is not needed, but the user must indicate the area round the transmitting antenna where the analysis must be performed. The path losses are shown graphically using a color code. In addition, the user can click a certain point on the map to display the path losses numerically, as shown in Figure 4.7, as well as the elevation, latitude, longitude, and distance from the antenna. Regarding the profile graph, the x-axis represents the distance (in km) from the transmitting antenna and the y-axis represents the height above sea level (in meters). �A straight line just joins the height where the antenna is located and the height where



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Figure 4.6  Example of the point-to-point mode.

Figure 4.7  Example of the coverage mode.

the selected point is located. Thus, the profile shown in the graph represents the physical land profile between the transmitting antenna and the selected point. The profile graph can be included in the simulation tool with a simple call to

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HeyWhatsthat, a free service that provides a scaled view of the horizon and terrain from a specified location, defined as a map point on OpenStreetMap. It calculates a viewshed from the location based on local terrain contours and depicts a view from the location highlighting mountains in the distance. 3. Trajectory mode: This option is very useful to calculate the path loss on a certain trajectory created by the user. It allows clicking on several points in the map to create a trajectory. The losses at the selected points are shown. Moreover, they are saved in a file so they can be compared with the results obtained in a different simulation by simply loading a file. First, the antenna is placed and then the points of the trajectory are indicated. In addition, intermediate points represented by squares of different colors depending on the loss appear. The points selected by the user have a white mark in the center, to differentiate them from the rest of squares. This mode is particularly useful when the results must be compared with the path losses provided by a different simulation tool or if the user wants to compare simulations with real measurements. Figure 4.8 shows an example of this option. Note that once we have the results, we can click on items to access the panel with information (elevation, latitude, longitude, distance from the transmitting antenna, and propagation loss) about the selected point.

Figure 4.8  Example of the trajectory mode.



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Table 4.1 shows the color code used in Figure 4.8 to represent the losses. 4.� Distance mode: This option provides the distance between two points. When the user clicks on the first point, the coordinates of that position are displayed (latitude and longitude). When the second point is selected, the distance from the first point to that point is shown, as well as the coordinates of the second point. Figure 4.9 shows an example of this mode. 5. Height mode: This option shows the position and elevation (height above sea level) of a given point. Figure 4.10 shows an example of this mode. 6. Trajectory-route mode: This option automatically computes and shows the best route to go to a certain destination and computes the losses. As mentioned before there are three options to set the route: by car, by bike or by foot, as can be seen in the upper left corner in Figure 4.11. The losses are displayed in several colors depending on their value following the same grayscale as in the trajectory mode. Figure 4.11 depicts a screenshot of this mode. The graphical scheme of the application is displayed in Figure 4.12, where the aforementioned inputs and outputs of each mode are shown for a better understanding of the whole system architecture. Table 4.1 Color Code Used to Represent the Propagation Losses in the Application

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Figure 4.9  Example of the distance mode.

Figure 4.10  Example of the height mode.

4.4  How to Develop the Application As mentioned before, the whole application has been developed using three programming languages: JavaScript, HTML5, and CSS3. The visual part has been coded in HTML5, which is the language used to define the content of a web page. It is considered as a standard since every browser has adopted it in order to correctly visualize any web page. The HTML file contains the map where the propagation losses are computed and graphically visualized. It also includes the different menus and buttons that can be seen



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Figure 4.11  Example of the trajectory route mode.

Figure 4.12  Graphical scheme of the application.

in the main screen when the application is launched. It is called index.HTML and it is the only HTML file required. Figure 4.13 shows the structure of the directory that contains the code of the web-based application. CSS language has been used to set the appearance of the icons (size and position, basically) such as the bin, the car, and the bike. Only one file has to be coded, since the rest of the CSS files are obtained from Leaflet and from Bootstrap. Bootstrap is a free software framework focused on web page design. It is based on HTML and CSS3, but it also contains JavaScript extensions. Its main advantage and reason for incorporation into this application

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Figure 4.13  Directory where the project is being coded.

is that it dramatically reduces the time to develop the visual part of the application, since it contains a large number of components (such as buttons, icons, tables, pop-ups, etc.) with which it is very easy to interact. As well, it offers a 100% compatible design with mobiles, so the pages created with this framework using its Responsive Design part are very easily restructured when considering small screens. In short, Bootstrap has been used to develop a graphical user interface that supports all screen sizes due to its versatility to create pages compatible with mobile devices and its large number of add-ons. To use the framework, the developer must follow the next steps: 1. Download and unzip the last version of Bootstrap; 2. Copy the content of directories CSS/, js/ and fonts/ into the directory where the project is being coded; 3. Include the CSS files in the section of each of the HTML files found in the directory where the project is being coded;



4. The Bootstrap JavaScript files must also be included at the end of the HTML file, before closing the tag:





This is the code required to create the map: var map = L.map(‘map’).setView([51.505, -0.09], 13); L.tileLayer(‘https://{s}.tile.openstreetmap.org/{z}/ {x}/{y}.png’, { attribution: ‘© OpenStreetMap contributors’ }).addTo(map);

Note that latitude and longitude are 51.505 and -0.09, respectively, and that the zoom level is set to 13. Obviously, those data

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can be customized by the developer. The rest of the code must be written as is. MeasureControl and FullScreen are Leaflet plugins that allow measuring distances on the map and displaying of the map in fullscreen mode, respectively. Both can be downloaded from the Leaflet site [34]. Again, the CSS files have to be included in the section of the index.HTML file:



and the JavaScript files have to be included at the end of the HTML file, before closing the tag:



Finally, icons and images folders are added to store the icons and images used in the graphical user interface. The js folder contains all the JavaScript files. Each functionality of the application is coded in a different JavaScript file: • Init.js: This file is responsible for starting the map and all the variables when the application is opened. It is responsible for controlling in what mode the user is working at each moment. It also calls the functions necessary to start the calculations depending on the selected mode. It also contains the search part, both the geolocation and the searching by coordinates and by name. • LoadFiles.js: This file is responsible for reading the files and establish if they are in a correct format. It is also responsible for processing the data for future incorporation into the map. • TrajectoryMode.js: This file contains all the logic of the Trajectory mode; it is responsible for performing all the correspond-



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ing calculations and preparing the results for later painting on the map. • TrajectoryRouteMode.js: This file contains all the logic of the Trajectory-Route mode; it is responsible for performing all the calculations and preparing the results for later painting on the map. • CoverageMode.js: This file contains all the logic of the Coverage mode; it is responsible for performing all the calculations and preparing the results for later painting on the map. • PointToPointMode.js: This file contains all the logic of the Pointto-Point mode; it is responsible for performing all the calculations and preparing the results for later painting on the map. • TXTGenerator.js: This file is responsible for collecting the data as the user adds objects in each mode. Later, those objects are converted into JSON and downloaded into a .txt file. • Math.js: This file contains all the formulas and equations used to calculate the propagation losses. It is used by all modules that need to calculate the propagation losses. • PaintMarks.js: This file contains all the interactions with the map. It not only paints all the marks, but also stores them in groups depending on the mode the user has selected. Since MapQuest is used to obtain the heights and the routes between two points, the developer hast to include the following lines before closing the tag in the index.html file:



It is important to point out that when the application is launched and a simulation is made, a request is generated to the MapQuest server using the getJSON function. This method leads to a performance problem if it is performed synchronously.

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Therefore, all requests to the server are made asynchronously. This provides faster application execution but also has disadvantages, since the response is not obtained instantly. To clarify this concept, the Coverage mode will be used as an example explaining how it works from the moment the simulation data are obtained until the server returns the requested information. In the Coverage mode, the user selects a point in the map where the transmitting antenna is placed and then, the application starts to perform the following calculations: 1. It looks for the points at which the calculations will be performed. To do this, it takes into account the distance selected by the user in the configuration panel. 2. For each point, it looks for the intermediate points with respect to the transmitting antenna, which will be used to calculate the propagation losses. 3. For each point, the URL used to request the elevation is generated. This includes the point itself and its intermediate points with respect to the transmitting antenna. 4. The request to the server is made asynchronously, allowing the application to continue with its execution and not freezing the screen until the server provides the result. 5. Once the server answers, the required calculations to obtain and show the propagation losses are carried out. As it can be seen, step 4 is critical for the application performance, since it determines the speed with which the data will be displayed to the user. With this method, the results are displayed progressively, giving the user the feeling that the application is performing the calculations. The main disadvantage of this process is that, by not waiting for the response and continuing with the execution of the program, calculations must be carried out within the getJSON function, making the code not totally optimized. On the other hand, errors are frequent when requesting petitions to the servers, mainly due to the saturation of connections, the server can be down or simply for maintenance. The control established for this type of errors is based on checking if the server has returned the data erroneously for a specific point and, in that case, that particular point is skipped. Thus, the application



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does not hang up waiting for an answer that may not arrive and can continue calculating the propagation losses in the rest of the points. Regarding the data management, the input and output data are stored in text files containing the information in JSON format. These files do not only contain the data, but also extra information that will be useful to paint the results when loading them on the map. The structure of these JSON files is based on a collection of key/value pairs, where the first object is the one that identifies the mode. Table 4.2 shows the main data stored in the JSON files. After the identification data, the most relevant pairs are those referring to the information of each analyzed point. Table 4.3 shows the general data stored in the JSON file. Finally, the representation of the results is performed according to a color code. The colors are set depending on the calculated loss at each point. The color ranges are shown at the bottom right of the map (see Figure 4.7). These colors are declared as constants

Table 4.2 Identification Data Stored in the JSON Files Key

Mode Center Distance

Value

Contains the simulation mode. Contains the latitude and longitude of the transmitting antenna. In the coverage mode, it is the distance where the propagation losses are computed.

Table 4.3 General Data Stored in the JSON Files Key

latLng Loss Distance Obstacle Elevation

Value

Contains the pair lat, lng with the information related to latitude and longitude of the point, respectively. Contains the propagation loss for that point. Contains information about the distance from each point to the transmitting antenna. Contains a Boolean value that indicates if the point is an obstacle or not. Contains the height above sea level in that point.

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and can be modified quickly, automatically changing the legend. The code to implement the legend is the following: var colorsCoverage = new Array (“#1A8F03”, “#42F61F”, “#EBFF06”, “#FF5603”, “#FFC103”, “#FD5C5C”, “#D00303”, “#940505”, “#CBBDBD”, “#FFFFFF”); var lossRange = new Array (115, 120, 125, 130, 135, 140, 145, 150);

The propagation losses at each point are calculated and visualized as follows: function calculateLoss(pf) { //Propagation loss is computed. var loss = getLoss(userData, center, pf); pf.loss = loss; // Associated color is selected. var i = 0; var found = false; while ((!found) && (i < lossRange.length)) { if (loss < lossRange[i]) { found = true; } else { i++;}} pf.color = colorsCoverage[i]; return pf; }

where the getLoss function returns the propagation loss associated to the point pf by applying the equations described in Section 4.1.2. Later, that value is mapped into a color. A scheme of the structure of the application can be seen in Figure 4.14.

4.5  Future Improvements of the Application The tool could be extended to compute the path loss in indoor environments. It could also be improved by including different empirical methods focusing in outdoor environments. Even deterministic propagation methods such as ray tracing could be included. That would allow an easy implementation of an indoor localization system based on the fingerprinting technique [35]. The



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Figure 4.14  Schematic structure of the application.

localization method would use the information about multipath effects provided by the ray tracing model. This information would be stored in a data set during the first stage of the fingerprinting method. The direction of arrival (DOA) and received signal strength (RSS) would be used in the fingerprinting technique as a hybrid system. The localization estimation would be calculated while taking into account the Euclidian distance between the DOA and the RSS from each unknown position and the information of the fingerprints. Regarding the visual appearance of the application, three main modifications could be done: (1) make better use of space by always setting the map in full screen, (2) implement a graphical user interface based on floating elements on the map with small animations so that the result is more colorful and pleasant to interpret, and (3) use gradient colors and eliminate the current tessellation to represent the results of the propagation losses in the Coverage mode would be a good alternative. Improvements regarding loading and obtaining the data are also a good idea because current data loading is a bit slow. Therefore, improving loading times is advisable. The data could be loaded differently: instead of using four sectors (upper-left, lowerleft, upper-right, lower-right), a radial type load could be implemented with center in the transmitting antenna. Next, the reader can see the current way of calculating the points in the coverage mode.

108   Applications of Geographic Information Systems for Wireless Network Planning function referencePoints(centre, north, south, east, west) { var nPoints = 10; var distanceP = Math.abs((centre.point.x - east. point.x) / nPoints); var x = centre.point.x; var y = centre.point.y; for (var i = 0; i < nPoints; i++) { x = x + distanceP; east.intermediate[i] = L.point(x, y); } x = centre.point.x; for (var i = 0; i < nPoints; i++) { x = x - distanceP; west.intermediate[i] = L.point(x, y); } x = centre.point.x; for (var i = 0; i < nPoints; i++) { y = y - distanceP; north.intermediate[i] = L.point(x, y); } y = centre.point.y; for (var i = 0; i < nPoints; i++) { y = y + distanceP; south.intermediate[i] = L.point(x, y); } }

Regarding new features, it would be helpful to be able to include more than one antenna on the map, as well as to be able to place new transmitting antennas at any time, not only at the beginning of the simulation. Finally, regarding the architecture, it could be remodeled to take advantage of the object-oriented programming features that JavaScript offers. Using inheritance and polymorphism, it would be possible to use different algorithms to calculate the propagation losses so that this does not complicate the architecture of the application. It would also be possible to use different ways of data representation depending on the selected mode.



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References [1] O’Brian, W. M., E. M. Kenny, and P. J. Cullen, “An Efficient Implementation of a Three-Dimensional Microcell Propagation Tool for Indoor and Outdoor Urban Environments,” IEEE Transactions on Vehicular Technology, Vol. 49, No. 2, 2000, pp. 622–630. [2] Roullier-Callaghan, A., “A Radio Coverage and Planning Tool,” 6th IEEE High-Frequency Postgraduate Student Colloqium, 2001, pp. 35–40. [3] WINPROP, Software Tool (incl. demo-version) for the Planning of Mobile Communication Networks and for the Prediction of the Field Strength in Urban and Indoor Environments, http://winprop.ihf.unistuttgart.de, Jan. 1999. [4] Cátedra, M.F., J. Pérez, F. Saez de Adana, and O. Gutiérrez, “Efficient RayTracing technique for Three-Dimensional Analyses of Propagation in Mobile Communications: Application to Picocell and Microcell Scenarios,” IEEE Antennas and Propagation Magazine, Vol. 40, April 1988, pp. 15–28. [5] Kanatas, A.G., and P. Constantinou, “A Propagation Prediction Tool for Urban Mobile Radio Systems,” IEEE Transactions on Vehicular Technology, Vol. 49, April 2000, pp. 1348–1355. [6] Ozgun, O., “New Software Tool (GO+UTD) for Visualization of Wave Propagation [Testing Ourselves],” IEEE Antennas and Propagation Magazine, Vol. 58, June 2016, pp. 91–103. [7] Corre, Y., T. Tenoux, J. Stéphan, F. Letourneux, and Y. Lostanlen, “Analysis of Outdoor Propagation and Multi-Cell Coverage from Ray-Based Simulations in Sub-6GHz and mmwave Bands,” 2016 10th European Conference on Antennas and Propagation (EuCAP), Davos, pp. 1–5. [8] Sulyman, A. I., A. T. Nassar, M. K. Samimi, G. R. Maccartney, T. S. Rappaport, and A. Alsanie, “Radio Propagation Path Loss Models for 5G Cellular Networks in the 28 GHZ and 38 GHZ Millimeter-Wave Bands,” IEEE Communications Magazine, Vol. 52, No. 9, 2014, pp. 78–86. [9] Parsons, J. D., The Mobile Radio Propagation Channel, London: Pentech, 1994. [10] Juan‐Llacer, L., J. V. Rodriguez, J. M. Molina‐Garcia‐Pardo, J. Pascual‐García, and M. Martínez‐Inglés,, “RADIOGIS: Educational Software for Learning the Calculation of Radio Electric Coverage in Wireless Communication Systems,” Computer Applications in Engineering Education, Vol. 27, No. 1, Jan. 2019, pp. 13–28. [11] “Radio Mobile,” January 2020, http://www.ve2dbe.com/english1.HTML. [12] Neis, P., and A. Zipf, “Analyzing the Contributor Activity of a Volunteered Geographic Information Project-The Case of OpenStreetMap,” iSPRS International Journal ofGeo-Information, Vol. 1, No. 2, 2012, pp.146–165. [13] “OSM Maps on Garmin,” OpenStreetMap Wiki, January 2020, Available online at: http://garmin.openstreetmap.nl/.

110   Applications of Geographic Information Systems for Wireless Network Planning [14] Zielstra, D., and H. H. Hochmair, “Comparing Shortest Paths Lengths of Free and Proprietary Data for Effective Pedestrian Routing in Street Networks,” University of Florida, Geomatics Program, December 2012, https://www. semanticscholar.org/paper/Comparing-Shortest-Paths-Lengths-of-Freeand-Data-Zielstra-Hochmair/38c346cdf18216b1966f0353d35cf9019411 ad21. [15] Xavier, P. P. S., and E. Costa, “Simulation of the Effects of Different Urban Environments on Land Mobile Satellite Systems Using Digital Elevation Models and Building Databases,” IEEE Transactions on Vehicular Technology, Vol. 56, No. 5, 2007, pp. 2850–2858. [16] Costa, E., “Simulation of the Effects of Different Urban Environments on GPS Performance Using Digital Elevation Models and Building Databases,” IEEE Transactions on Intelligent Transportation Systems, Vol. 12, No. 3, 2011, pp. 819–829. [17] Giordano, E., R. Frank, G. Pau, and M. Gerla, “CORNER: A Radio Propagation Model for VANETs in Urban Scenarios,” Proceedings of the IEEE, Vol. 99, No. 7, 2011, pp. 1280–1294. [18] Solomitckii, D., M. Gapeyenko, S. S. Szyszkowicz, S. Andreev, H. Yanikomeroglu, and Y. Koucheryavy, “Toward Massive Ray-Based Simulations of mmWave Small Cells on Open Urban Maps,” IEEE Antennas and Wireless Propagation Letters, Vol. 16, 2017, pp. 1435–1438. [19] Nuckelt, J., D. M. Rose, T. Jansen, and T. S. Kurner, “On the Use of OpenStreetMap Data for V2X Channel Modeling in Urban Scenarios,” 7th European Conference on Antennas and Propagation, Gothenburg, 2013. [20] Hänel, T., M. Schwamborn, A. Bothe, and N. Aschenbruck, “On the Map Accuracy Required for Network Simulations Based on Ray Launching,” 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks, Boston, MA, 2015, pp.1–8. [30] Dai, Z. R. J. Watson, and P. R. Shepherd, “A Propagation Modeling Approach to Urban Navigation,” 11th European Conference on Antennas and Propagation, 2017. [31] Wang, L., B. Ai1, D. He, K. Guan, J. Zhang, J. Kim, and Z. Zhong, “Vehicleto-Infrastructure Channel Characterization in Urban Environment at 28 GHz,” China Communications, 2019. [32] OSM API documentation, Jan. 2020, Available at: http://wiki.openstreetmap.org/wiki/Develop. [33] Leaflet documentation, January 2020, https://switch2OSM.org/using-tiles/ getting-started-with-leaflet/. [34] Eibert, T. F., and P. Kuhlman, “Notes on Semiempirical Terrestrial Wave Propagation Modelling for Macrocellular Environments. Comparison with Measurements,” IEEE Transactions on Antennas and Propagation, Vol. 51, No. 9, 2003, pp. 2252–2259.



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[35] Okumura, Y., E. Ohmori, T. Kawano, and K. Fukuda, “Field Strength and its Variability in VHF and UHF Land-Mobile Radio Service,” Rev. Elect. Commun. Lab., Vol. 16, No. 9–10, Sept.–Oct. 1968. [36] Lee, W. C. Y., Mobile Communications Design Fundamentals, New York: Wiley, 1993. [37] Hata, M., “Empirical Formula for Propagation Loss in Land Mobile Radio Services,” IEEE Transactions on Vehicular Technology, Vol. 29, 1980, pp. 317–325. [38] Whitteker, J. H., “Near-Field Ray Calculation for Multiple Knife-Edge Diffraction,” Radio Science, Vol. 19, 1984, pp. 975–986. [39] Greenberg, E., and E. Klodzh, “Comparison of Deterministic, Empirical and Physical Propagation Models in Urban Environments,” 2015 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), Tel Aviv, 2015, pp. 1–5. [40] Nguyen, H. C., I. Rodriguez, T. B. Sorensen,, L. L. Sanchez, I. Kovacs, and P. Mogensen, “An Empirical Study of Urban Macro Propagation at 10, 18 and 28 GHz,” 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), Nanjing, 2016, pp. 1–5. [41] Dias, M. H. C., and M. S. de Assis, “An Empirical Model for Propagation Loss Through Tropical Woodland in Urban Areas at UHF,” IEEE Transactions on Antennas and Propagation, Vol. 59, No. 1, Jan. 2011, pp. 333–335. [42] Valcarce, A., and J. Zhang, “Empirical Indoor-to-Outdoor Propagation Model for Residential Areas at 0.9–3.5 GHz,” IEEE Antennas and Wireless Propagation Letters, Vol. 9, 2010, pp. 682–685. [43] Leaflet plugins, January 2020, Available at: https://leafletjs.com/plugins. HTML [44] Tayebi, A., J. Gómez, F. M. Saez de Adana, and O. Gutierrez, “The Application of Ray-Tracing to Mobile Localization Using the Direction of Arrival and Received Signal Strength in Multipath Indoor Environments,” Progress in Electromagnetics Research, Vol. 91, 2009, pp.1–15.

5 Applications This chapter describes the most important real-world applications for the tool described in Chapter 4. Some examples of uses (design of open-area wireless networks, calculation and optimization of propagation in open environments, design of automatized agricultural systems, etc.) will be depicted in a very detailed way. The chapter begins with a basic description of the electromagnetic spectrum and a brief explanation about the different types of cells involved in this type of computer tool. Two practical applications of the web tool implemented by the authors are described and a comparison between simulations and measurements is shown. The simulation tool has been successfully validated in a real environment and a good agreement between simulated and measured results has been found. Finally, a discussion of the main features of similar applications is also included in the chapter.

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5.1  Electromagnetic Spectrum It is well known that the fundamental resource exploited in wireless communication systems is the electromagnetic spectrum, depicted in Table 5.1. Practical radio communication occurs at frequencies from approximately 3 kHz to 300 GHz, which matches to wavelengths from 100 km to 1 mm. Table 5.1 shows the frequency range in each frequency band and Table 5.2 shows the main applications that can be deployed in frequency bands high frequency (HF), very high frequency (VHF), ultra high frequency (UHF), and super high frequency (SHF). Radio waves can be used for many different applications in a quick and effective way. As can be seen in the table, the uses strongly depend on the frequency band. This chapter will be focused only on communications at VHF frequencies and above, where the wavelength is typically small compared to the size of obstacles such as buildings, hills, and trees.

5.2  Types of Cells in Cellular Networks A cell is the area covered by a base station, also known as transmitting antenna, in a cellular network. The scope of the possible applications of the simulation tools, such as the one presented in Chapter 4, is restricted to macrocells. Macrocells are cells that provide network coverage over a broad area. They are typically used in cellular telephony. Macrocells are designed to provide mobile Table 5.1 Electromagnetic Spectrum Band

VLF LF MF HF VHF UHF SHF EHF

Frequency

3–30 KHz 30–300 KHz 300 KHz–3 MHz 3–30 MHz 30–300 MHz 300 MHz–3 GHz 3–30 GHz 30–300 GHz

Wavelength

100–10 Km 10–1 Km 1 Km–100m 100–10m 10–1m 1m–10 cm 10–1 cm 1 cm–1 mm

EHF: extremely high frequency, HF: high frequency, LF: low frequency, MF: medium frequency, SHF: superhigh frequency, VHF: very high frequency, VLF: very low frequency, UHF: ultrahigh frequency.



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Table 5.2 Main Applications in Each Frequency Band Frequency Band Applications VLF Used for long-distance communication, such as marine navigation services, communication with submersed submarines, secure military communication, and government time radio stations (i.e., broadcasting time signals to set radio clocks.) The main mode of propagation is the Earth ionosphere waveguide mechanism since radio waves can propagate as ground waves following the curvature of the Earth. Radio waves can diffract over obstacles, so they are not blocked by mountain ranges and can travel beyond the horizon due to their large wavelengths. LF Radio waves present low signal attenuation, so they are also appropriate for longdistance communications. They are mainly used in long-range navigation (LORAN), a terrestrial radio navigation system that enables ships and aircraft to determine their position and speed from low-frequency radio signals transmitted by fixed land-based radio beacons using a receiver unit. It is also used in weather systems, marine communication, and aircraft beacons. MF The propagation modes are ground wave and sky wave. MF radio waves are used in amplitude modulation (AM) radio broadcast (550–1600 KHz), maritime radio, and transoceanic air traffic control. It is also widely used for direction finding (DF), which refers to the establishment of the direction from which a received signal was transmitted. By combining the direction information from two or more receivers, the transmitter can be located via triangulation. HF The main mode of propagation is sky wave, but ground wave is also used for communications over short distances. The typical technologies that use HF are long-distance aircraft, international broadcasting, citizen band radios, and ship communication. VHF Diffraction and reflection take place within this frequency band, and propagation is performed beyond the horizon. Radio waves can propagate at large distances due to their good propagation within buildings. Some applications are broadcast television, frequency modulation (FM) radio (88–108MHz), AM aircraft communications, and radio beacons for air traffic. UHF Radio waves propagate in a very short distance, or in a long distance where line of sight (LoS) exists, because they are blocked by large buildings and hills, although the transmission through building walls is strong enough for indoor reception. In cases of NLoS, some signals may reach an obstacle and may not reach the receiver. Some applications of this frequency band are satellite communications including GPS, microwave links, wireless personal communication systems such as cellular, 3G, Bluetooth, unlicensed band communications, Wi-Fi, and local multipoint distribution service (LMDS). SHF Propagation distances become restricted due to the absorption by the atmosphere. The applications include low earth orbit (LEO) and geosynchronous equatorial orbit (GEO) satellite systems, satellite services for telephony and television, and possible future mobile communication systems. EHF

Due to the very small wavelengths, all particles (vapor, oxygen in atmosphere) become obstacles. High losses greatly limit propagation distance, so EHF is used for terrestrial short-distance communications (around a kilometer) where LoS is guaranteed.

EHF: extremely high frequency, HF: high frequency, LF: low frequency, MF: medium frequency, SHF: superhigh frequency, VHF: very high frequency, VLF: very low frequency, UHF: ultrahigh frequency.

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and broadcast services (including both data and voice), particularly outdoors, to urban, suburban, and rural environments with medium traffic densities. Transmitting antenna heights are typically greater than the surrounding buildings, providing a cell radius from around 1 km to many tens of kilometers. They mostly operate at VHF and UHF frequency bands and might also be utilized to provide fixed broadband access to buildings at high data rates, typically at UHF and low SHF frequencies. In macrocells, the important parameter for designers is the overall area covered instead of the specific field strength at particular locations. Therefore, models of a statistical nature are normally more appropriate than deterministic approaches. The propagation models included in simulation tools typically treat the path loss associated with a given macrocell as dependent on distance, considering that the environment surrounding the base station is equally uniform. Consequently, the coverage area predicted by these tools for an isolated transmitting antenna in an area of uniform environment type will be estimated as hexagonal or circular (see Figure 5.1). The modeling of propagation path loss is the main approach of estimating the range of a mobile radio system. The accuracy of the predictions is critical in deciding if a certain system design will be feasible. Empirical models have been applied successfully in macrocells, but deterministic models are being used increasingly in order to improve the accuracy. However, deterministic models have the disadvantage of increased input data requirements and higher computational complexity. Because each

Figure 5.1  Scheme of six base stations/transmitting antennas providing coverage to six macrocells. The shape of each macrocell is approximately circular/hexagonal.



5.2  Types of Cells in Cellular Networks

117

method has its own advantages and disadvantages, some simulations tools compute the propagation losses by applying empirical methods [1], others deterministic methods [2, 3], and others hybrid semiempirical methods [4]. Apart from macrocells, there are also microcells, which are low-powered radio access nodes. Microcells get their name from the fact that they are small compared to macrocells, with a range of 1m to 1 or 2 km rather than tens of kilometers. Microcells are designed for high-traffic densities in urban and suburban areas to users both within buildings and outdoors. In this case, transmitting antennas are lower than nearby building rooftops, so coverage area is defined by street layout. The cell length is up to around 500m. Microcells mostly also operate at VHF and UHF frequency bands. Similar to microcells, other small cells exist such as femtocells and picocells. Again, the difference between them is the range of coverage that they provide. On average, picocells reach 200m and femtocells 10m. This is why macrocells and microcells are utilized more outdoors, whereas picocells and femtocells are used inside buildings. Another important difference is that macrocells are used for providing coverage and microcells are mainly utilized for managing capacity. That is why in urban areas that are densely populated, it is easy to find microcells used to generate a cellular network that can cope with the high demand that macrocells cannot cope with. Table 5.3 summarizes the main characteristics of the different types of cells. As can be seen, macrocells and microcells are both used to provide radio coverage, but in very different ways, making each more effective in different situations. From now on, this chapter will be focused on macrocells. As mentioned above, it is important to note that macrocells provide radio coverage for cellular networks in large areas that can span a big city and that the transmitting antennas in macrocells are normally mounted on towers or masts to create cellular base stations. They are usually located in rooftops or elevated places higher than other buildings or terrains with a clear view of the surroundings so that the propagated signals are not blocked.

118   Applications of Geographic Information Systems for Wireless Network Planning Table 5.3 Main Characteristics of the Different Types of Cells Type of Cell

Macrocell

Microcell

Typical power

20–40 watts

2–5 watts

Typical height

15–25m

8–10m

Coverage area

25–40 Km

500m–3 km

Typical number of simultaneous users served Location

More than 200 per sector/per frequency Tower (urban/ rural), top of buildings (urban)

32–200

Picocell

250 milliwatts Indoor locations Typically 250m 32–64

Small towers, Indoors buildings, lampposts

Femtocell

100 milliwatts Indoor locations Typically 50 m 8 for residential, 16 for enterprise Indoors

5.3  Main Applications �In this section it is worthwhile to highlight the audiences for which these tools are aimed: 1. Engineers who need to predict propagation in complex scenarios with great accuracy and who are dedicated to wireless networks planning. 2. System designers in the field of wireless networks for a wide range of application, such as intelligent agriculture, rural communications networks, or any other network that involves propagation in open areas. 3. PhD, graduate, and upper-level undergraduate students in the field of radio propagation. The approach used will allow to engineers/researchers to implement a tool that helps wireless network planning. In Chapter 2, we noted that radio propagation models play a vital role in designing wireless communication systems. The models are typically used to estimate the location and number of transmitter stations as well as to predict the transmitter coverage area. Hence, the most important application of the simulation tool described in Chapter 4 is the planning of radio communication systems, and the potential clients or users are companies that need to plan radio communication systems such as Global System



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for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Terrestrial Trunked Radio (TETRA), Local Multipoint Distribution Service (LMDS), Multichannel Multipoint Distribution Service (MMDS), or wireless fidelity (Wi-Fi). Other important applications include broadcasting (Section 5.3.2), and private mobile radio and fixed wireless access applications including WiMax. For these systems, in principle, the empirical and semiempirical methods introduced in Chapter 2 could be used to predict the propagation loss over every path profile between the base station and every possible user location, also known as mobile station. Next, the more important applications of these tools are described. 5.3.1  Planning and Optimization of Radio Communication Systems

Frequency planning for a mobile network is a complex iterative process that is influenced by many factors. In practice, it is carried out by drawing up a frequency plan for the initial approximation network taking into account the requirements for coverage, number, and distribution of users, available frequency bands, communication quality, features of the standard used, and other conditions. Next, the radio coverage of the network is calculated considering the cochannel, and adjacent channel interference for the selected frequency plan, the optimization of the parameters of the transmitting antennas, and the frequency plan are performed in order to minimize the influence of the interference on the network coverage. Taking into account a particular frequency, radio network planning for cellular radio communications would consist of positioning base stations in strategic locations in order to provide enough coverage in every point within the area under study. On the other hand, radio networks are composed of a large number of cells with minimum overlap so as to minimize the number of base stations. However, some overlap is needed to allow for handovers for moving mobile phone users. Obviously, planners keep in mind that the fewer base stations required, the better. Essentially, the objective is to provide a good-quality service in the considered area with the minimum number or transmitting antennas. Based on this reasoning, some simulations tools

120   Applications of Geographic Information Systems for Wireless Network Planning

provide the possibility of optimizing the locations of the transmitting antennas with the aim of minimizing them [5]. In addition, in recent years the demand for mobile communications is constantly increasing, so the need for better coverage, improved capacity, and higher transmission quality also rises. This implies that existing networks might need to be optimized. In those areas where a radio communication system already exists, the simulation tool can be helpful in order to optimize the number and distribution of base stations. The output of that optimization could be a list of coordinates, given by latitude and longitude, that could refer to the location of every transmitting antenna. The optimization process could be done depending on a minimum signal level indicated by the user that must be received in a certain area. Internally, the simulation tool implemented by the authors follows the next steps to carry out that task. First, it computes the received signal strength taking into account an initial configuration of transmitting antennas. If every point under analysis receives the minimum signal level established by the user, the optimization ends. Otherwise, if there is some point whose received signal strength is not high enough, a new distribution (new location for every antenna) is set to perform a new calculation of the received signal strength in every point within the area under analysis. The optimization process ends when every point under analysis receives the minimum signal level established by the user, or when a previously established number of iterations is reached. The second case is included to avoid extremely long optimizations. 5.3.1.1  Validation of the Developed Tool

In planning a mobile communication network or developing mobile equipment, it is essential to characterize the radio channel to gain insight into the dominant propagation mechanisms that will define the performance experienced by users. This characterization allows the designer to ensure that the channel behavior is well known prior to the system deployment to validate the propagation models used in the design process and to ensure that the equipment used provides robust performance against the full range of fading conditions likely to be encountered. Furthermore, after a system has been deployed, measurements allow field engineers to validate crucial design parameters that show how the



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system is performing and how it may be optimized. Those steps have been carried out in order to validate the web tool developed by the users, as shown next in two practical applications. The first practical application is based on the Trajectory mode, which was explained in Chapter 4. An urban outdoor scenario has been simulated and measured. Figure 5.2 displays the area extracted from the application where the measurements took place. To perform the simulations, the transmitting antenna was placed on the roof of the Polytechnic School in Alcalá de Henares. The Trajectory mode was used to place the points where the propagation losses were computed. In this case, each coordinate or geolocation point is the same, both for the actual measurements and for the simulated measurements. Therefore, a direct comparison is made. To perform the measurements, the antenna Schaffner CBL6143A was placed on the roof of the Polytechnic School. The microwave signal generator R&S SMR20 was used to generate the transmitted signal. The antenna position was defined by the following parameters: • Latitude: 40.513160; • Longitude: -3.349274. The comparison in Figure 5.3 shows the obtained data from the Trajectory mode at 1.5 GHz, and the measurements carried out by calibrating a log periodic antenna at the same frequency. It can

Figure 5.2  Area where the measurements have been conducted (obtained from the tool).

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Figure 5.3  First comparison between simulations and measurements.

be seen that both curves are quite similar along the 20 different points that have been analyzed. The mean error is 6.6 dB and the standard deviation is 3.6 dB, which is a very good result for this type of application [6–10]. According to [6], the COST model, restricted to a frequency range from 800 to 2,000 MHz and distance link lower than 5 Km, provides mean errors of about 3 dB with standard deviations in the range 4–8 dB. In addition, the mean error and standard deviation obtained in this work fall within the range obtained in [7–9], and the model presented in [10] provides a root mean square error of 10 dB in an outdoor scenario similar to the scenarios that have been simulated and measured in this book. A second route was measured and simulated for a wider validation of the simulation tool using the same equipment and the Trajectory mode at 1.5 GHz. The comparison between simulations and measurements can be seen in Figure 5.4. The mean error in this case is 4.5 dB and the standard deviation is 2.2 dB.

Figure 5.4  Second comparison between simulations and measurements.



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The second practical application is based on the TrajectoryRoute mode, which was also described in Chapter 4. In this case, an example of how to obtain the path loss during a certain route is shown. First, the by foot option is used between two particular points in the map: the cities of Madrid and Vicálvaro, in Spain. The transmitting antenna is located in Madrid, whereas the final receiving point is located in Vicálvaro. Once both points are set, the path losses are displayed according to a color code. Figure 5.5 shows the output of the simulation. Note that the by foot option is selected, as can be seen in the left part of Figure 5.5. The same simulation has been carried out using the by car option and setting the same points to locate the transmitting and receiving antennas. The results of that simulation are shown in Figure 5.6. The reader can see that the trajectory is different in each case since, obviously, the pedestrian cannot walk along the motorway. Table 5.4 shows the color code used in Figures 5.5 and 5.6 to represent the losses. 5.3.2  Broadcasting

As pointed out at the beginning of this section, another important application of this tool is in broadcasting. In the last few years, more and more countries follow nationwide strategies to evolve radio broadcasting and analog terrestrial television into much more advanced digital services. Software simulators keep up to

Figure 5.5  Trajectory-Route mode using the by foot option.

124   Applications of Geographic Information Systems for Wireless Network Planning Table 5.4 Color Code Used to Represent the Propagation Losses in the Application

Figure 5.6  Trajectory-Route mode using the by car option.

date with new planning challenges and designed special functionality for digital video broadcast (DVB) and digital audio broadcast (DAB) standards DVB-H, DVB-T, T-DAB, and other relevant broadcasting standards. At the same time, broadcasting enterprises are expected to cover countrywide territories including rural areas without wireline connectivity, where microwave transmission is a single viable solution for distributing signals to radio towers. In this case, software tools provide convenience by handling multiple wireless technologies in the same planning project. Some spectrum engineering tools used to assist in the assignment of frequencies and planning of digital and analog broadcasting and land mobile services are presented in [11]. The authors also



5.4  Secondary Applications

125

developed their own planning software tool. In particular, they performed a case study of a single-frequency network of DVB-T in the Konya lowland of Turkey, which has a relatively flat terrain. The network consisted of six stations located at the corner positions of a hexagon that had edges of 27 km long plus one station at the center of the hexagon. The station at the center radiated 100W and the remaining six stations on the edge of the hexagon cell radiated 1 kW each. The stations were separated by 27 km and their operating frequency was 826 MHz.

5.4  Secondary Applications In this section, several secondary applications of signal propagation calculation tools are described. 5.4.1  Precision Agriculture Applications

With an increasingly competitive agriculture, the need to make the management of the activity more efficient means that currently farmers have to incorporate different tools of precision agriculture in their daily routine. Precision agriculture is a farming management concept based on satellite positioning data, information technology, remote sensing technology, and robotics with the aim of saving costs, reducing environmental impacts, and producing more and better outputs. Farmers declare that the most significant benefit of using precision agriculture techniques is the reduction of the drift or overlapping, since it is directly related to the cost of fuel, fertilizers, seeds, agrochemicals, and workforce. A high percentage of the savings from guidance systems arise from saved fertilizer and spray resources. Sustainable agricultural production systems are, then, possible thanks to the adoption of precision farming techniques. In open environments or rural areas, the kind of simulation tools described in Chapter 4 can be used for the improvement of guidance systems utilized in precision agriculture. real time kinematic (RTK) systems, together with global navigation satellite system (GNSS) networks, are real time location systems that are widely used in precision agriculture for auto guidance systems in

126   Applications of Geographic Information Systems for Wireless Network Planning

farm vehicles [12]. There are some applications for the auto guidance of RTK-based agricultural vehicles that set the working pattern depending on the shape of the field [13]. The most relevant feature of these programs is that they are able to generate a pattern from evaluating and analyzing the dimensions of the targeted agricultural terrain in advance. This is one of the main elements that establishes the path to be followed by the vehicle during guidance (see Figure 5.7). If the pattern is not properly established, then there will be areas where the vehicle will not pass (the pattern will not be detected), or it will pass more than once. Correct positioning at all times is crucial for the rover to accurately follow the pattern. The RTK uses a reference signal from a reference station, and the accuracy of the auto guidance system depends strongly on the signal quality arriving at the receiver that is installed in the vehicle or rover. Therefore, the user can predict the accuracy of the auto guidance systems by analyzing the propagation losses between the vehicle and the reference base station. Normally, several base stations are connected to the rover. The authors have experimentally found that the stations that present lower propagation losses result in less drift. The authors conducted some tests in a real rural environment located in Castilla la Mancha, Spain. One of the farming activities consisted of plowing the land with a piece of equipment that is 5m long. Table 5.5 shows that there are fewer drifts in the rover with respect to the established patterns with lower propagation losses (determined with the simulation tool). In summary, this software tool is very useful for determining the best reference station to which the rover should connect at any

Figure 5.7  Screen of the Cerea software when the farming vehicle is working.



127

5.4  Secondary Applications

Table 5.5 Relationship Between the Simulated Propagation Losses and the Measured Drift Antenna Used Location of the in the Base Base Station Station

Site 1 Site 2 Site 3 Site 4 Site 5 Site 6 Site 7

Spanish GNSS network Spanish GNSS network IGN Spanish GNSS network Spanish GNSS network Spanish GNSS network Tallysman 2710 Tallysman 2710

Antenna Used in the Rover

Novatel Ag-start Novatel Ag-start Novatel Ag-start Novatel Ag-start Novatel Ag-start Novatel Ag-start Novatel Ag-start

Drift Associated to the Initial Established Propagation Pattern Losses

12–17 cm

169 dB

15–20 cm

170 dB

15–20 cm

170 dB

15–20 cm

172 dB

15–20 cm

173 dB

4–5 cm

131 dB

2–3 cm

130 dB

moment, not according to the criterion of proximity, but rather according to the criterion of propagation losses. The developed web application is then very useful in the field of precision agriculture applications because it provides a quick and quite accurate estimation of propagation losses everywhere thanks to the use of OpenStreetMap. 5.4.2  Educational, Research, and Training Applications

Not only mobile communications companies and farmers, but also universities and training centers, can benefit from this type of web applications. For example, the work presented in [14] describes an educational tool called RADIOGIS, which is able to calculate the radio electric coverage in wireless communication systems. Through that simulation tool, students can learn about the calculation of radio electric coverage. The authors mention that the educational interest of RADIOGIS consists of the incorporation of five practice exercises with their corresponding instructions, which guide students in carrying them out. In addition, it is important for universities and training centers to have this type

128   Applications of Geographic Information Systems for Wireless Network Planning

of application because students should be able to carry out experiments that make the learning of concepts possible through their application in real life. Concretely, the authors mention that RADIOGIS incorporates various practice exercises with the objective of learning to • Calculate a link budget from certain parameters (in transmission and reception) of real equipment, in order to determine what the poorest link would be (uplink or downlink); • Estimate the radio electric coverage for a base station from the transmission and reception parameters used in the link budget; • Find the coverage map, and that of the best radio communication system server with multiple base stations; • Learn about the necessary procedures in the environment of a GIS to be able to calculate the percentage of coverage in a geographic area, defined by a vector layer of polygons, for example, of municipal areas; • Optimize a radio communication system, minimizing the number of transmitting antennas necessary to achieve a determined quality objective; • Know which propagation models should be used depending on the type of environment: rural, urban, or indoor; • Know the environment of a GIS from the perspective of the management and representation of geographic information (raster or vector, and descriptive), as well as the basic functionalities that are useful for the calculation and analysis of radio electric coverage. The Cellular Expert [15] company has also developed a special application for education institutions. According to its website, it is expected that students get practical skills, competencies and core technical abilities required to be successful as a professional. In particular, this type of tool would be very useful when teaching subjects such as wave propagation, radiation and radio communication, and mobile communications, which are included in degree coursework in telecommunication technologies



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129

engineering, telecommunication systems engineering, telematics engineering, and electronic communications engineering. It is a fact that practical exercises inspired by the real world help to reinforce the theoretical concepts acquired in the aforementioned subjects. Moreover, these tools may have some additional advantages: • They can be used in person during a class or used remotely, since it is not usually necessary to install any software on the student’s computer. Students only have to access a URL. • A brief theoretical reference guide can be provided to clarify the basic concepts related to each exercise in addition to the tool’s user guide that describes the different functionalities included. • A self-evaluation section can be included with test-type questions with which the student can assess their knowledge. For this, there can be extensive repositories from which 10 questions would be randomly selected so that the number of exams could be virtually infinite. • The feedback provided to the students after completing the exercises and the self-assessments can make the learning process more efficient. • Each user of the tool can register so that the simulations performed in each session can be saved, as well as self-evaluations, exercises completed or in progress, and so on. • It could also be possible to display a report with the monitoring of each user in which data on their progress could be included: self-evaluations carried out, average mark obtained, competencies acquired or to be acquired, and so on. On the other hand, radio propagation modeling is a key part in the design of wireless communication systems. As pointed out in [16], ray tracing methods can also be embedded in wireless communication systems to provide real-time channel estimation. As well, a lot of effort has been done recently to improve deterministic techniques like ray tracing due to the high computational resources required. Computer software that predicts path loss in outdoor and indoor environments is an interesting field for

130   Applications of Geographic Information Systems for Wireless Network Planning

researchers to investigate. In fact, several applications have been developed in universities and research centers. Some examples are I-Prop (Technical University of Prague, Czech Republic) [17], RADIOGIS (Politechnic University of Cartagena, Spain) [14], Cindoor (University of Cantabria, Spain) [18], and Covermap (University of Alcalá, Spain) [19]. These applications are continuously being improved with new techniques and updates, so the research activity deserves to be included here as a quite important application of this type of prediction tool. 5.4.3  Applications for Emergency Service Providers

According to [20], these tools can also be used to improve wireless communications for emergency service providers (firefighters and police) in large structures (e.g., supermarkets, large apartments and office buildings, sports stadiums, convention centers, and warehouses) when a disaster situation occurs. The paper states that researchers collected radio propagation data before, during, and after the implosion of a 14-story apartment building near Orleans, Los Angeles. Results showed that this building attenuated signals by 50 dB by just entering the building. Once the building collapsed, attenuation can increase much more than this. This research helps us understand the communication problems with which first responders are confronted when they enter large structures, and the changes in propagation that occur when a building collapses. Researchers carried out similar sets of experiments during the implosion of a large sports stadium and a convention center. The initial findings in these data sets were very similar; that is, attenuation by as much as 70 dB may be encountered when communicating in these large structures. Therefore, if this attenuation is considered by the simulation tools when performing the calculations of the propagation losses, we could be able to predict the propagation losses in case of a disaster situation. 5.4.4  Humanitarian Use, Military Defense, and Public Security

Some computer software like detailed in [15] enables tactical mission planners to design and optimize various types of networks



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131

for extreme situations management whenever or wherever the need arises. These applications provide essential tools to design professional mobile radio, point-to-point, and mesh networks for extreme situations quickly and effectively, perform automated frequency allocation, site planning, traffic modeling, and capacity estimation tasks, optimize radio networks to ensure staff safety during emergencies, calculate radio coverage for mobile and temporary base stations fast and precisely, use functions specially dedicated for military/security transmission planning as well as functions dedicated for high-reliability networks such as TETRA, Association of Public-Safety Communications OfficialsInternational Project 25 (APCO P25), and solve very specific tasks taking into consideration 3D geographical data (terrain, clutter, and obstacles such as mountains, trees, buildings, etc.). These two networks, TETRA and APCO P25, are used by dispatch organizations, such as fire, police, ambulance, and emergency rescue service, using vehicle-mounted radios combined with handheld walkie-talkie use. APCO P25 is mainly utilized in North America whereas TETRA is used in the European Union together with Digital Mobile Radio (DMR), which is another similar protocol standard. Table 5.6 summarizes the frequency bands used in each kind of network. 5.4.5  Terrain Exploration

In order to be able to calculate the signal propagation, the application needs to use different types of spatial data. That data describes the environment through which the signals are propagated. Depending on the type of environment, a certain algorithm can be selected (in the case of tools that include several algorithms) and

Table 5.6 Frequency Bands Used in Each Network Network

TETRA APCO P25 DMR

Frequency Band

UHF (380–430 MHz, 806–871 MHz) VHF (132–174 MHz) UHF (400–520 MHz, 806–871 MHz) VHF (136–174 MHz) UHF (350–520 MHz)

132   Applications of Geographic Information Systems for Wireless Network Planning

applied to perform the calculations. This would be a great advantage because each empirical algorithm works better (that is to say, is more adequate) depending on the orography and the environmental characteristics (open areas, urban areas with high buildings or narrow streets, etc.). It is worthwhile to mention that spatial data can also refer to the population density, so that data can also be used to describe the physical distribution of the application users. This aspect is extremely important because the planning and optimization of radio communication systems should take into account the number of users within the area under analysis. According to that information, more transmitting antennas will be normally required in those zones where the population density is higher and less transmitting antennas will be normally required in those zones where the population density is lower. On the other hand, just as it is desired for the application users to be able to understand the graphical propagation data that the application provides, it is also desired for them to be able to comprehend the data that the application needs to calculate that propagation. To do so, the same tools that are used to explore the signal propagation are offered to explore height terrain, population density, terrain type, and other spatial data that the application may need to use in order to perform the calculations. These tools offer the possibility to know the value for any of those data sets on a specific spatial location, just with a click. It is also possible to select an area in which the values will be represented as an image according to a color code. For example, Figure 5.8 shows the heights of a zone delimited by the top left and bottom right coordinates. As can be seen, both coordinates are given by their latitude and longitude. Additional information is depicted, such as the dimensions of the area (length and width), given in kilometers, and its surface, given in Km2. At the top of the pop-up, the location and height of the selected point is also indicated. This information is quite useful when looking for the position of a transmitting antenna, since it is normally placed in an elevated point and under these conditions, the propagated signals are less likely to hit an obstacle.



5.4  Secondary Applications

133

Figure 5.8  Graphical view of the heights in a certain area selected by the user. The dark zones are higher zones and the light zones are lower zones.

Additionally, it is possible to visualize the values along a line as a graph that plots the value against its spatial location as a vertical cut. For instance, Figure 5.8 shows a 2D plot that includes the heights along a line previously indicated on the map. In this particular example (see Figure 5.9), the point selected with the mouse is at 848m above sea level. These features allow for these applications to be great tools to explore unknown areas, since the exposed data describes the environment in terms of population, type, and elevation. This fact could be very useful, for example, in case of a natural disaster, to get a first contact with the affected area without the need to be physically there. The knowledge of the surrounding orography is

Figure 5.9  2D plot of the heights along a line previously indicated in the map.

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a great help for emergency services in case of an earthquake, fire, or a tsunami. They will be able to know the best way (it could be the shortest or the safest path) to access the affected area. In the end, these kinds of tools could be used anytime a description of the terrain is needed. 5.4.6  Other Applications

The recent work presented in [21] proposes the utilization of unmanned aerial vehicles (UAVs) to assist the existing terrestrial communication infrastructure in forthcoming 5G/Beyond 5G for improved wireless network coverage, particularly to difficult-toreach areas, scenarios demanding high data rate and low latency on emergency needs, transceiving sensor data from field to the ground servers, and providing wireless network coverage in a disaster where existing terrestrial communication infrastructure gets partially/severely damaged. It is expected that UAVs will play a vital role in 5G and Beyond 5G networks as flying base stations and/or relays. For this reason, it is a top challenge to model the radio propagation channel from a UAV (low-altitude platforms) to existing terrestrial base stations, the receiver on ground, and with other flying UAVs in a network. Consequently, simulation tools that focus on the radio propagation channel modeling taking into account the aforementioned features will be necessary to simulate functional low-altitude wireless networks. Moreover, the developed application tool could be improved to perform the modeling of wireless channels for radio wave propagation over the sea surface. Some challenging aspects, such as frequently changing weather, lack of statistical data, and fluctuating sea environment, should be taken into consideration since the sea surface is an unrest surface. Reflection, diffraction, scattering, and refraction would be challenging to calculate due to the fluctuant and uncertain nature of the marine environment. A typical application of this particular channel model would be the ship-to shore communication or the vessel-to-vessel communication. Some works already exist in this field, such as the one in [22]. This work presents a comparative study of over-the-sea channel



5.5  Computer Software Applications

135

models for radio wave propagation, so that it can help researchers and engineers in this field to design the adequate channel models based on their applications, classification, features, advantages, and limitations. Moreover, telecommunication regulation authorities can benefit from this type of computer tool. They are able to display and map wireless network nationwide, calculate network coverage and gaps, analyze interference of all operators and service providers, evaluate the pricing policy of the service providers at different parts of the country, monitor customer complaints and number of subscribers in covered areas, evaluate the easiness of service availability to the customer, import data from all operators, perform automated cell and frequency planning, easily store, collect, and find all information about base station configurations and permissions, easily get various reports, permissions, and other related information, and so on. Finally, the article presented in [23] outlines some of the ways that radio communication can be used to monitor the Earth’s climate. In fact, using radio communication networks is often the only way to observe and measure the various factors involved. According to the authors, satellites, as part of the global array of radio communication systems, provide a vital means of gathering global data on the climate and on climate change. Satellites are now being used to monitor carbon emissions, the changes occurring in the ice stored in polar caps and glaciers, and the way atmospheric temperatures vary. Remote sensing provides accurate and up-to-date information on land cover and any changes that are happening over wide areas, providing data from remote areas that are otherwise difficult to reach. In addition, repeated measurements have made it possible to create archives of remote sensing data spanning several decades, which can be used to construct time-series data on land cover and land use.

5.5  Computer Software Applications In this section, a discussion of the main features of similar applications is presented. As already mentioned, computer software applications must be developed for the prediction of path loss, the

136   Applications of Geographic Information Systems for Wireless Network Planning

design of broadcast networks, or the optimization of existing networks, among others. Some of the functionalities of the majority of the software tools with integrated models include: • Designing new broadcast networks; • Performing coverage prediction for various types of communication systems in order to design and optimize networks of transmitters; • Analyzing interference problems; • Exploring new coverage scenarios; • Diagnosing difficult coverage situations; • Evaluating new transmitter and receiver concepts; • Visualizing and analyzing predicted performance, as well as comparing simulation with experimental data. In addition, according to [24], the increase in the development of computer tools to predict propagation losses has been motivated and enabled by the following factors: first, the enormous increase in the need to plan cellular systems accurately and quickly; second, the development of fast, affordable computing resources; and finally, the development of geographical information systems, which index data on terrain, clutter, and land usage in an easily accessible and manipulable form. At the same time, several propagation path loss models have been implemented and included in a broad range of commercially available and company-specific planning tools [25]. Although most are based on combined empirical and simple physical models, it is anticipated there will be progressive evolution in the future toward more physical or physical statistical methods �as computing resources continue to become cheaper, clutter data improves in resolution and cost, and as research develops into numerically efficient path loss prediction algorithms. These are some of the currently available wireless system planning tools: Mentum CellPlanner, Forsk Atoll, ASSET, Broadband Planner, CelPlan, WinProp, V-Soft Probe, EDXWireless, x-Wizard, Overture, iBwave, CRC-COVLAB, FUN, Hi-Res, MbP, RedPredict, TAP, RFCAD, Astrix, ComSite, and Wireless InSite.



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Next, three of them (Wireless InSite, WinProp, and TAP) will be briefly described as examples. The application Wireless InSite [26] provides RF engineers with the tools to design wireless links, optimize antenna coverage, and assess key channel and signal characteristics for RF and millimeter-wave frequency bands. The application WinProp Proman [27] combines wave propagation and radio network planning. It includes wave propagation models for different scenarios and network planning simulators for various air interfaces. The WinProp indoor module can be used for applications such as • Cellular network planning (e.g., picocells/femtocells) inside buildings (incl�uding penetration of outdoor cells into buildings). • Planning of wireless local area (WLAN) networks in multifloor buildings. • Broadcasting analysis (e.g., indoor coverage of terrestrial transmitters or satellites). • Coverage analysis and network planning inside tunnels, mines, or underground stations. A different module is available because the propagation of electromagnetic waves in tunnels differs significantly from the propagation in outdoor environments. • Short range radio links (e.g., UWB in and around vehicles). • Planning of sensor networks in complex environments. The �Terrain Analysis Package (TAP) is a computer-based terrestrial RF propagation software. TAP is used by federal agencies, state and local public safety departments, energy companies, utilities, and consultants to evaluate radio transmitter sites; predict, map, and analyze radio coverage; plan land mobile radio and cellular systems; conduct intermodulation and adjacent channel interference studies; and design microwave, VHF, and UHF links. Finally, propagation models can also be used for the estimation of various characteristics of outdoor long range IoT systems [28] and smart city applications. It becomes especially important when deploying IoT networks in hard-to-reach areas [29]. In these

138   Applications of Geographic Information Systems for Wireless Network Planning

particular cases, the prediction of propagation characteristics is necessary to determine the coverage areas for calculations of the impact of the multipath propagation effect and for interference estimation and planning of the mutual arrangement of antennas.

References [1] Ibhaze, A., et al. “An Empirical Propagation Model for Path Loss Prediction at 2100MHz in a Dense Urban Environment,” Indian Journal of Science and Technology. Vol. 10, 2017. [2] Granda, F., et al. “Deterministic Propagation Modeling for Intelligent Vehicle Communication in Smart Cities,” Sensors, Basel, Switzerland, Vol. 18, No. 7, 2018, p. 2133. [3] Agelet, F., F. Perez Fontan, and A. Formella, Radio-Tracer: A Tool for Deterministic Simulation of Wave Propagation, European Space Agency Division, Vol. 146, 1999, pp. 135–138. [4] Wang, W., T. Jost, and R. Raulefs, “A Semi-Deterministic Path Loss Model for In-Harbor LoS and NLoS Environment,” IEEE Transactions on Antennas and Propagation, Vol. 65, No. 12, Dec. 2017, pp. 7399–7404. [5] Casado, J., et al., “Application of Bioinspired Algorithms for the Optimization of a Radio-Propagation System Simulator Based on OpenStreetMap,” ACCSE 2019 International Conference on Advances in Computation, Communications and Services, Nice, France, 2019. [6] Parsons, J. D., The Mobile Radio Propagation Channel, London: Pentech, 1994. [7] Greenberg, E., and E. Klodzh, “Comparison of Deterministic, Empirical and Physical Propagation Models in Urban Environments,” 2015 IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems (COMCAS), Tel Aviv, 2015, pp. 1–5. [8] Nguyen, H. C., et al., “An Empirical Study of Urban Macro Propagation at 10, 18 and 28 GHz,” 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), Nanjing, 2016, pp. 1–5. [9] Dias, M. H. C., and M. S. de Assis, “An Empirical Model for Propagation Loss Through Tropical Woodland in Urban Areas at UHF,” IEEE Transactions on Antennas and Propagation, Vol. 59, No. 1, Jan. 2011, pp. 333–335. [10] Valcarce, A., and J. Zhang, “Empirical Indoor-to-Outdoor Propagation Model for Residential Areas at 0.9–3.5 GHz,” IEEE Antennas and Wireless Propagation Letters, Vol. 9, 2010, pp. 682–685. [11] Topcu, S., et al., A GIS-Aided Frequency Planning Tool for Terrestrial Broadcasting and Land Mobile Services, NATO Science Series, Vol. 11, 2008.



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[12] Cordesses, L., C. Cariou, and M. Berducat, “Combine Harvester Control Using Real Time Kinematic GPS,” Precision Agriculture, Vol. 2, 2000, pp. 147–161. [13] Cerea software application, https://www.cereagps.com/. [14] Juan‐Llacer, L., J. V. Rodriguez, J. M. Molina‐Garcia‐Pardo, J. Pascual‐García, and M. Martínez‐Inglés, “RADIOGIS: Educational Software for Learning the Calculation of Radio Electric Coverage in Wireless Communication Systems,” Computer Applications in Engineering Education, Vol. 27, No. 1, Jan. 2019, pp. 13–28. [15] “Cellular Expert,” http://www.cellular-expert.com/. [16] Degli-Esposti, V., ‘‘Ray Tracing Propagation Modelling: Future Prospects,’’ Proc. 8th Europe Conference Antennas Propagagation, The Hague, The Netherlands, Apr. 2014, p. 2232. [17] I-Prop Computer Code User Manual, www.i-prop.cz; https://customer.active24. com/. [18] Cindoor Software, https://www.gsr.unican.es/es/CINDOOR.html. [19] Saez de Adana, F., et al., “Covermap: Computer Tool to Calculate the Propagation in Open Areas Importing Data from GoogleMaps,” Antennas & Propagation Conference, Loughborough, Nov. 16–17, 2009, pp. 229–232. [20] Holloway, C. L., et al., “Radio Propagation Measurements During a Building Collapse: Applications for First Responders,” Proc. Intl. Symp. Advanced Radio Technology, ISART 2005, Boulder, CO, March 2005, pp. 61–63. [21] Ahmad, A., A. A. Cheema,, and D. Finlay, “A Survey of Radio Propagation Channel Modelling for Low Altitude Flying Base Stations,” Computer Networks, Vol. 171, April 2020. [22] Habib, A., and S. Moh, “Wireless Channel Models for Over-the-Sea Communication: A Comparative Study,” Applied Sciences, 2019, Vol. 9, p. 443. [23] Radiocommunications and Climate Change, https://www.itu.int/en/ITU-R/information/Pages/climate-change.aspx. [24] Saunders, S. R., and A. Aragón Zavala, Antenna and Propagation for Wireless Communication Systems, 2nd Edition, New York: Wiley, 2007. [25] “Directory of Wireless System Planning Tools,” 2020, UBC Radio Science Lab, datasheet http://rsl.ece.ubc.ca/planning.html#MentumPlanet. [26] Wireless InSite, https://www.remcom.com/wireless-propagation/. [27] WinProp, https://altairhyperworks.com/product/FEKO/WinProp Applications-Wifi. [28] Suarez, C., et al., “Iot Quality of Service Based in Link Channel Optimization in Wireless Sensor Networks,” 2018 IEEE International Conference on Smart Internet of Things (SmartIoT), 2018, pp. 172–177.

140   Applications of Geographic Information Systems for Wireless Network Planning [29] Nazarenko, A. P., V. K. Saryan, and A. S. Lutokhin, “Using of Flying Internet of Things Before, During and After Critical Stage of a Disaster,” Electrosvyaz, No. 7, 2015, pp. 12–15.

About the Authors Francisco Saez de Adana is a professor at the Computer Science Department of the University of Alcalá in Spain. He worked as a faculty researcher at Arizona State University in 2003, as a visiting professor at the same university in 2013, and as a visiting professor at the University of Technology of Sydney in 2008. Prof. Saez de Adana has participated in more than 50 research projects with Spanish, European, American, and Japanese companies and universities related to the analysis of on-board antennas and radio propagation in mobile communication and RCS computation. He has directed four Ph.D. dissertations and has published one book, 32 papers in peer-reviewed journals, three book chapters, and more than 60 conference contributions at international symposia. His research interests are in the areas of high-frequency methods in electromagnetic radiation and scattering, on-board antenna analysis, and radio propagation in wireless network communications.

141

142   Applications of Geographic Information Systems for Wireless Network Planning

Josefa Gómez Pérez was born in 1984. She received a B.S. and M.S. in telecommunications engineering from the University Polytechnic of Cartagena, Spain, in 2005 and 2007, respectively, and a Ph.D. in telecommunications engineering from the University of Alcalá, Spain in 2011. She has worked as an assistant professor since 2012 at the University of Alcalá. She also worked as a faculty researcher at the Hong Kong University in 2011 and at the Instituto de Telecomunicaçoes of Lisbon in 2014. She has participated in 37 research projects with Spanish and European companies. She has published 23 papers in peer-reviewed journals, two book chapters, and more than 40 conference contributions at national and international symposia. Her research interests are optimization and analysis of antennas, design of graphical user interfaces, and the study of propagation for mobile communications or wireless networks in both outdoor and indoor environments. Abdelhamid Tayebi Tayebi was born in 1983. He received a B.S. and M.S. in telecommunications engineering from the University Polytechnic of Cartagena, Spain, in 2005 and 2007, respectively, and a Ph.D. in telecommunications engineering from the University of Alcalá, Spain in 2011. He has worked as an assistant professor since 2011 and then as a professor starting in 2019 at the University of Alcalá. He also worked as a faculty researcher at the Hong Kong University in 2011 and at the Instituto de Telecomunicaçoes of Lisbon in 2014. He has participated in 35 research projects with Spanish and European companies. He has published 21 papers in peer-reviewed journals, a book chapter, and around 40 conference contributions at national and international symposia. His research interests are design and optimization of antennas, electromagnetic radiation and scattering, and the development of web tools for the analysis of radio propagation in rural and urban environments. Juan Casado Ballesteros was born in 1998. He received a B.S. in Computer Science from the University of Alcalá, Spain, in 2020. He has participated in three research projects with Spanish companies. His research interests are spatial data processing, design of distributed architectures, web tools, and interactive graphical user



About the Authors

143

interfaces for the analysis of antenna signal loss, genetic, and evolutionary algorithms to look for antenna locations that minimize signal loss in rural and urban environments.

Index A APCO P25, 131 Apple Maps, 79 Application example about, 83 combination of GIS and semiempirical propagation method, 84–89 configuration options, 93 coverage mode, 94–96 coverage mode results example, 93, 94 development, 98–106 distance mode, 96–97, 98 framework use steps, 100–101 future improvements, 106–8 graphical scheme, 99

height mode, 97, 98 JSON files, 105 main features, 91–98 main screen, 92 operating modes selection, 92 OSM in, 85, 87–89 point to point mode, 94, 95 project coding directory, 100 propagation loss, 106 propagation model, 89 schematic structure of, 107 technologies used, 89–91 trajectory mode, 96–97 trajectory-route mode, 97, 99 Applications broadcasting, 123–25 cell types, 114

145

146   Applications of Geographic Information Systems for Wireless Network Planning Applications (continued) computer software, 135–38 in each frequency band, 115 educational, research, and training, 127–30 electronic spectrum, 114 emergency service providers, 130 humanitarian use, military defense, and public security, 130–31 main, 118–25 precision agriculture, 125–27 radio communication systems, 119–20 representation of propagation losses, 124 secondary applications, 125–35 terrain exploration, 131–34 validation of the developed tool, 120–23 ArcGIS, 79 ASCII data format, 55 Asynchronous JavaScript and XML (AJAX) style, 90–91 Atefi and Parsons model, 24–25 Autodesk Geospatial, 73–74

B Bentley Maps, 74 Bootstrap, 100, 101 Building shapes, 69–70

C Cascading Style Sheets (CSS3), 83, 89, 98–99 Cellular Expert, 128 Cellular network, cell types in, 114–18 Cerea software, 126 Choropleth maps, 59–60, 61 Cloud optimized GeoTIFF (COG), 55 Comma separated value (CSV), 57

Computer software applications, 135–38 Coordinate systems, 64–65 Cost 231-Hata model, 22–23 Cost 231-Walfisch-Ikegami model, 30–32 Coverage mode, 94–96, 103 CoverageMode.js, 103

D Data access, providing, 52 collection, 51 dynamic, 44 historical, 45 natural earth, 77 raster, 54–56 social, 61 spatial, 50–51, 54–65, 68 static, 44–45 types, usefulness of, 67–71 user modifications, saving, 50–51 vector, 56–58 Databases most important features of, 45 spatial, 45–46, 57 use of, 44 Data providers about, 51–52 ESRI open data, 77 FAO GeoNetwork, 78 ISCGM Global Map, 78 NASA earth observations (NEO), 78 natural earth data, 77 Open Elevation, 77 OpenStreetMap (OSM), 77 OSMBuildings, 77 SEDAC, 77 Terra Populus, 78



Index



UNEP environmental data explorer, 77–78 use of, 76 DesiGN (DGN), 57 Deterministic methods, 14, 20–21 Digital audio broadcast (DAB) standards, 124 Digital Mobile Radio (DMR), 131 Digital video broadcast (DVB), 124 Direction of arrival (DOA), 107 Dissymmetric maps, 59, 60 Distance mode, 96–97, 98 Dot density maps, 61 DWG, 57 DXF (drawing exchange file), 57 Dynamic data, 44

E ECW (enhanced compression wavelet), 55 Edition and visualization tools about, 46 algorithms, 47 Autodesk Geospatial, 73–74 Bentley Maps, 74 extensions, 47 functionality, 46–47, 72 GRASS GIS, 73 map servers, 74 plug-ins, 47 QGIS, 73 relations, 47–48 Small World, 74 in spatial data manipulation, 47 spatial map server, 46 use of, 72 user interfaces of, 46 Educational, research, and training applications, 127–30 Eibert and Kuhlman model, 34–36, 70 Electronic spectrum, 114

147

Emergency service providers, applications for, 130 Empirical propagation methods accuracy of, 38 Atefi and Parsons model, 24–25 Cost 231-Hata model, 22–23 Cost 231-Walfisch-Ikegami model, 30–32 defined, 20 Eibert and Kuhlman model, 34–36 Ericsson model, 34 fundamentals of, 19 geometric parameters to apply, 36–38 GIS combining with, 13, 15–16 Ibrahim-Parsons model, 23–24 Ikegami model, 26–27 introduction to, 13–14 McGeehan and Griffiths model, 24 Okumura-Hata model, 21–22 Sakagami-Kuboi model, 25–26 Stanford University Interim model, 32–34 Walfisch and Bertoni model, 27–29 Xia and Bertoni model, 29–30 Environment type, 70 ERDAS IMAGINE, 56 Ericsson model, 34 ESRI grid, 55 ESRI open data, 77

F FAO GeoNetwork, 78 Femtocells, 117, 118 FullScreen, 102 Full-stack frameworks, 53, 78–80

G Geocoding, 70–71 GeoCSV, 57

148   Applications of Geographic Information Systems for Wireless Network Planning Geographical information systems (GIS) about, 43–44 architecture and components, 43–53 architecture illustration, 44 buildings in urban environments and, 37 combining empirical propagation methods with, 13, 15–16 comparison, 71–80 components to use, 66–67 as a database, 14 data providers, 51–52 defined, 14, 42–43 diversity of capabilities, 42 edition and visualization tools, 46–48 full-stack frameworks, 53 functioning of, 42 high-level APIs, 50–51 implementation, 43 information use, 38 introduction to, 14–15 IoT example, 42, 43 map servers, 48–49 real use case, 42 reasons for using, 15 role-based actor in architecture, 53 spatial databases, 44–46 user code, 52 in visualization of data, 15 Geographic JavaScript Object Notation (GeoJSON), 58 Geographic markup languages (GML), 57 Geographic Resources Analysis Support System (GRASS), 73 Geographic rich site summary (GeoRSS), 58 Geometrical optics (GO), 26

Geometrical theory of diffraction (GTD), 26, 29–30 GEOPACKAGE, 56 GeoServer, 74–75 GetJSON function, 103, 104 GetLoss function, 106 Global navigation satellite system (GNSS) networks, 125 Global positioning system exchange format (GPX), 57 Global System for Mobile Communications (GSM), 118–19 Google Maps, 78–79 Graduated symbol maps, 61–62

H Heat maps, 60–61 Height mode, 97, 98 High frequency (HF), 114, 115 High-level APIs in creating custom code, 46 data providers, 51 developer use of, 50 implementation, 46, 50, 51 layers, 50 Leaflet, 75, 79 Mapnik, 76 Open Layers, 75–76, 79 recommended user of, 67 use of, 75 user code user of, 52 Historical data, 45 Humanitarian applications, 130–31 HyperText Markup Language (HTML5), 83, 89, 98

I Ibrahim-Parsons model, 23–24 Ikegami model, 26–27



Index

Initial graphics exchange specification (IGES), 57 Init.js, 102 ISCGM Global Map, 78 Isopleth maps, 61

J JavaScript, 83 JavaScript files, 102–3 JavaScript object notation (JSON), 90 JPEG 2000, 55 JSON files, 105

K Keyhold markup language (KML), 58 Keyhold markup zip (KMZ), 58 Kirchoff diffraction integrals, 35 Knife-edge diffraction, 30

L Layers actions applied to, 63 data pattern, 64 defined, 63 high-level APIs, 50 Leaflet, 75, 79, 87, 102 LoadFiles.js, 102 Local Multipoint Distribution Service (LMDS), 119

M Macrocells, 116–17, 118 MapBox, 79–80 MapBox vector tiles (MVT), 58 Mapnik, 76 Map projections, 65 MapQuest server, 91, 103 Maps about, 58–59 choropleth, 59–60, 61



149

data, 68 dissymmetric, 59, 60 dot density, 61 graduated symbol, 61–62 heat, 60–61 illustration of contextual information, 69 isopleth, 61 layering elements applied to, 63–64 layers, 58 network, 62 raster images, 59 road, 62 scattered information, 63 terrain height, 68–69 3D, 62–63 web-page embedded, displaying, 67 working with, 59 Map Server, 75 Map servers access, 49 in accessing spatial data, 48 export from, 49 GeoServer, 74–75 high-level control, 48–49 layer of abstraction, 49 Map Server, 75 third-party, 51 use of, 74 Math.js, 103 Maxwell’s equations, 84 MBTiles, 56 McGeehan and Griffiths model, 24 MeasureControl, 102 Microcells, 117, 118 Military defense applications, 130–31 MongoDB, 72 MrSID (multiresolution seamless image database), 55

150   Applications of Geographic Information Systems for Wireless Network Planning Multichannel Multipoint Distribution Service (MMDS), 119

N NASA earth observations (NEO), 78 Natural earth data, 77 Network maps, 62 Nonline of sight (NLOS) situation, 26, 27

O Okumura-Hata model, 21–22 Open Database License, 85 Open Elevation, 77 Open Layers, 75–76, 79 OpenStreetMap (OSM) in application example, 85, 87–89 as cooperative online service, 87 as data provider, 77 defined, 56 reasons for using, 88–89 3D buildings, 70 as wide database, 88 Organization, this book, 11–12, 16 OSMBuildings, 77

P PaintMarks.js, 103 Physical optics (PO), 30 Picocells, 117, 118 Point to point mode, 94, 95 PointToPointMode.js, 103 Polynomial approximation, 36 Post GIS, 72 Precision agriculture applications, 125–27 Projections, 65 Propagation characteristics, knowledge of, 13–14



study of, 14 See also Empirical propagation methods Propagation constant, 35 Propagation loss application example, 105 Atefi and Parsons model, 25 color code representation, 124 Cost 231-Hata model, 22–23 Cost 231-Walfisch-Ikegami model, 30–31 defined, 19–20 Eibert and Kuhlman model, 34–35 Ericsson model, 34 Ibrahim-Parsons model, 23–24 Ikegami model, 26–27 McGeehan and Griffiths model, 24 modeling of, 116 obtaining, 20 Okumura-Hata model, 21–22 Sakagami-Kuboi model, 25–26 simulated, measured drift and, 127 Stanford University Interim model, 32–33 Walfisch and Bertoni model, 28–29 Propagation models application example, 89 in company-specific planning tools, 136 in IoT system estimation, 137–38 site specific, 84 types of, 20–21 See also specific propagation models Public security applications, 130–31

Q Quantum Geographic Information Systems (QGIS), 73 Queries, 48



R Radio communication systems, 119–20, 128 RADIOGIS, 127–28, 130 Radio propagation modeling, 129 Random access memory, 44 Raster data, 54–56 Raster images, 59 Ray-tracing algorithms, 69 Real time kinematic (RTK) systems, 125–26 Received signal strength (RSS), 107 Road maps, 62 Roads, 69

S Sakagami-Kuboi model, 25–26 Scattered information maps, 63 Semideterministic model, 27 Semiempirical methods combination of GIS and semiempirical propagation method, 84–89 defined, 21 Semiempirical wave propagation algorithms, 85 SHAPEFILE, 57 Simulations and measurements comparison, 122 Site specific propagation models, 84 Small World, 74 Social data, 61 Socioeconomic data and applications center (SEDAC), 77 Software tools, 15 Spatial data manifestations, 54–65 saving, 50–51 working with, 68 See also Data

Index

151

Spatial databases, 45–46, 57, 72 Spatial libraries, 52 Stanford University Interim model, 32–34 Static data, 44–45 Super high frequency (SHF), 114, 115, 116

T Telecommunication regulation authorities, 135 Terrain Analysis Package (TAP), 137 Terrain exploration applications, 131–34 Terrain height, 68–69 Terra Populus, 78 Terrestrial Trunked Radio (TETRA), 119 3D building shapes, 69–70 3D maps, 62–63 Topographic JavaScript Object Notation (TopoJSON), 58 Trajectory mode, 96–97, 121–22 TrajectoryMode.js, 102–3 TrajectoryRouseMode.js, 103 Trajectory-route mode, 97, 99, 123, 124 Transmitter power, 20 TXTGenerator.js, 103

U Ultra high frequency (UHF), 114, 115, 116–17 UNEP environmental data explorer, 77–78 Uniform theory of diffraction (UTD), 30 Universal Mobile Telecommunications System (UMTS), 119 Universal Resource Locator (URL), 48 Unmanned aerial vehicles (UAVs), 134 User code, 52

152   Applications of Geographic Information Systems for Wireless Network Planning

V Validation of the developed tool, 120–23 Vector data, 56–58 Very high frequency (VHF), 114, 115, 116–17

Wireless system planning tools, 136–37 World Geodetic System (WGS), 64 Worldwide Interoperability for Microwave Access (WiMAX), 32

W

X

Walfisch and Bertoni model, 27–29 WinProp, 137 Wireless fidelity (Wi-Fi), 119 Wireless InSite, 137

Xia and Bertoni model, 29–30