Keywords

1 Introduction

Nowadays, wireless communications systems have become a medullary part of the modern society [1,2,3]. The range of applications of wireless communications range from 2G-6G cellular connections [4,5,6,7], Internet of Things (IoT) applications [8,9,10,11], microwave links [12,13,14], satellite communications [15,16,17], among many others. There are several factors that must be taken into account to design a successful wireless link, and they heavily rely in the frequency band to be used. Very relevant factors are the geographical zone (its topography, obstacles, etc.), the required transceivers, the communication protocol to be implemented, the propagation medium, the required coverage, among many others.

One typical approach to estimate the losses between the transmitter and the receiver, i.e. the attenuation that the power of the transmitted electromagnetic waves is to undergo in different propagation scenarios is through the establishment of propagation models, or path loss models, which in most cases have an experimental basis [18,19,20]. Although propagation models are aimed to simplify the computation of the losses in certain conditions, it is to be remarked that such models are of limited application as they should only be used in scenarios that are similar to the ones from where they were derived and tested.

One of the first path loss models was posed by Okumura in 1968 [21], which is an empirical model that considers the propagation frequency, the height of transmitting and receiving antennas, the distance between antennas, as well as buildings of different heights. It was the result of assessing losses experimentally in many Japan cities in the 1960’s. Further propagation models includes the well-known Hata (1980) [22], Walfisch-Bertoni [23] and Ikegami et al. (1991) [24] models, as well as combinations of them. Thus, if the propagation environment changes, the propagation model should as well change. In this sense, there are plenty of specific path loss models adapted to as much propagation scenarios as it can be imagined. For instance, [18] assess typical models in both urban and rural environments; [20] adapts some models to indoor propagation environments; [25] reviews propagation models adapted to high altitude mountainous areas for 2.6 GHz propagation; in [26, 27] they adapt propagation models to forestry environments; [28] study propagation models in coastal environments and; [29] studies path loss in UAV air-terrestrial links for farming purposes. Moreover, there has been much research in the effects of many direct and indirect phenomena in propagation models. For example, [30] studies the attenuation due to rain in short-range millimetric links; [31] studies he impact of tropical climate in 28 GHz propagation; [32] studies the effects of solar radio Emissions on outdoor propagation models at 60 GHz bands; [33] incorporates the effects of vegetation and vehicular traffic in urban microcells; [34] reviews the impact of the presence of diffraction and specular reflectors in millimetric propagation; among many others.

In order to better assess the success of a wireless link, path loss models are applied to specific cases through the computation of a link budget [35]. Link budgeting is aimed to take into account, under certain propagation model, all the power gains and losses in propagation of the electromagnetic waves for a specific communications system, including an specific transmitter, the transmission medium and the receptor [35]. In this sense, equations used for link budgeting are to provide the received power in the receptor, as function of the transmitted power and the gains and losses provided by the system and the environment (through propagation models) [35].

Several research efforts has been done in the performance of link budgets for several applications. For instance, [36] use adaptive methods to better estimate link budget parameters; [37] performs link budget modelling for NLOS submarine optical wireless links, [38] explores link budgeting techniques for LPWAN LoRa connections; while [39] explores link budgeting for high frequency wireless conditions in extreme weather conditions. On the other side, standardisation organisms as the International Telecommunications Union (ITU) and the European Telecommunications Standard Institute have released documents on link budgeting for different wireless technologies [40, 41].

As it can be observed, the main drawback of selecting a preexisting propagation model is that in many specific situations, they can be overly simplistic and unrealistic. As the application of specific path loss models may be complicated and cumbersome, many software have been developed with the aim of easing the computation of link budgets under certain propagation models. For instance, some non-free options includes the Communications Toolbox of Matlab®/Simulink® [42] and the Keysight® ADS software [43]. However, their high costs and the fact that they act as blackboxes make them a difficult option to implement. Also, for instance, Keysight® ADS is only for the microwave range and has very specific and limited components in its catalogue. On the free access software side, there are typical options as the Pasternack [44], Huawei [45] and Radwin [46] web-based calculators. However, they have important drawbacks: 1) as they are very generic, the computation of processes like the link budget is unreliable; 2) in many occasions, the standards and models they implement is not clear; 3) most of such calculators are set up by transceivers manufacturers, so they are only specialised to such devices. The later is the case of [45], which is specialised for optic fiber Huawei equipment and [46] for Radwin radio antennas.

Some interesting developments on this field can be found in scientific literature too, which perform calculations of parameters related to radiofrequency transmission using only one or more principles. For example, [47] only takes into account the Okumura - Hata Model [22]; although their calculations are performed correctly, it does not pose the ranges within which the model is known to be effective; it also does not include its limitations (advantages, disadvantages, uses); finally, it does not include propagation losses in suburban or rural environments. Likewise, the online system of [48] calculates the transmission loss using the Okumura-Hata [22] and Walfisch-Ikegami [49] models; although it provides the used formulas, the required variables and their units, taking into account the density of the environment, the ranges of use of the models are not mentioned, and they cannot compute the propagation losses of the Okumura [50] or Walfisch-Bertoni [23] models.

In this work we develop a link budget calculation web-based software in the range of cellular communications, that is reliable, accurate, of free use, easy implementation and well-founded. As the base of our development, we consider the COST 231 Walfisch Ikegami model [51, 52], which is a more updated model specifically designed for urban environments and cellular communications. As such a model is one of the most studied and updated, it will provide a robust link budget calculator. Moreover, it is implemented in free software, it have an adequate user interface, it is scalable to the parameters that requires the link budget to be calculated, the web application is compatible with any equipment, and it has proved to be accurate. This work is organised as follows: In Sect. 2 we feature theoretical background, including the basics of mobile communications systems and link budgeting, a review of the main propagation models, as well as the computational tools used for the web service. In Sect. 3 we pose the used propagation model, its adaptation for link budget calculation, as well as the development of the software calculator. Section 4 poses the results and the discussion of this research, while in Sects. 5 and 6 we pose some conclusions as well as future possible paths in which tis research could be expanded.

2 Materials and Methods

2.1 Mobile Communication Systems

The first cellular type networks began their operation in the 1980s, and since then different versions and improvements of such networks have been arisen. Since cellular communication arises as a need for users and the increasing use of mobile devices, each geographic region began to generate its technical specifications and therefore the implementation of cellular systems differed between areas [53]. These first systems are known as the First Generation (1G) of cellular systems [54, 55].

The second generation (2G) of cellular systems was developed in the late 1980s and early 1990s [54, 55]. They were based on digital interfaces and circuit switching, as well as the introduction of medium access techniques such as time or code division multiplexings. The two main technologies developed in this generation are Global System for Mobile (GSM) [56,57,58] and Code Division Multiple Access System One (CDMAOne) [59,60,61]. However, demand soon exceeded the capabilities of this generation.

The third generation (3G) was designed to solve the deficiencies of the second, allowing that in addition to increasing the connection capacity of mobile devices, it could also offer a greater number of services [54, 55]. The 3G standard was approved in March 1992 at the International Mobile Telephone by the Year 2000 (IMT-2000) [62, 63]. The main technologies included for 3G were the EDGE standard [64], Code Division Multiple Access (CDMA2000) [65,66,67,68,69], Wideband Code Division Multiple Access (WCDMA) [70,71,72,73] and Mobile WiMAX (World Wide Interoperability for Microwave Access) [74,75,76,77,78].

The fourth generation (4G) is defined in the IMT-Advanced standard [79]. The physical and logical capacities of this generation far exceed previous ones, since properties such as bandwidth, data transmission speed, quality of service, among others, have increased significantly. In this generation, technologies such as LTE (Long Term Evolution) [80, 81], a new version of WiMAX [82, 83], and the introduction of technologies for low-power systems, such as LTE-M [84,85,86,87] and NB-IoT [88,89,90,91], were considered.

In general, the operating frequencies of such generations of cellular systems range from 800–2000 MHz. Although there are new generations of “celullar” such as 5G and the emerging 6G, their operating frequencies are beyond the 3.4 GHz and up to the subterahertz [92, 93]. Such frequencies lie beyond those used in this work.

2.2 Propagation Models

Propagation models aim to model the behaviour of signals in a defined environment, focusing on physical characteristics such as input and output signal power, link losses, antenna gains, among others. As mentioned in Sect. 1 the propagation models are a statistical approximation, based on experiments that allow to model of these parameters prior to the installation and final testing of a communications system. The propagation models, in addition to the theory and experimental results that they include, also define a methodology for calculating the link budget, that is, the particular form of the budget equations, depending on the model used.

The Okumura Model. The Okumura model is an empirical propagation model used to calculate losses in urban environments, taking into account measurements obtained in different cities in Japan [50]. A characteristic that propagation models share in their structure are the initial parameters. In Table 1, the main parameters of Okumura model are presented.

Table 1. Okumura model parameters [21].
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An important result from this model is that it allowed obtaining the average attenuation curves relative to the free space losses as a function of the operating frequency and the distance between the mobile device and the base station. It is worth mentioning that the advantages of the Okumura model lie around the fact that it is a simple and useful model when it comes to irregular scenarios, that is, urban areas with buildings of different heights and high density of users [21].

The Hata Model. The Hata model is the result of a multiple regression analysis applied to the normalised propagation curves obtained in the Okumura model. From this analysis, an experimental equation was obtained to calculate losses in urban environments with a high density of devices.

The Hata model has certain advantages such as the possibility of computationally implementing the Okumura model. Although this model has a lower operating frequency than the Okumura model, its greatest contribution is obtaining the aforementioned equation and the normalised attenuation curves. Their initial parameters can be observed in Table 2.

Table 2. Hata Model Parameters [22].
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The Walfisch - Bertoni Model. This propagation model has its main application in urban environments with lower density of device use than the Okumura and Hata models. Some examples of application scenarios can be residential areas, internal networks of shops or small industrial complexes [23].

The idea of this model is to build the line of sight (LoS) above the obstacles (mainly buildings). The signals that reach the mobile devices are the result of the diffraction that they suffer when encountering the highest parts of the buildings, so the calculation of the losses does not directly take into account the rest of the signals generated in the building, or those that may interfere with the transmission. The parameters used in this model can be seen in the Table 3.

Table 3. Walfisch - Bertoni model parameters [23].
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This model does not necessarily require the height of the transmitting and receiving antennas, since they do not need to be in a fixed position; it only requires the distance between them. In fact, one of the advantages of this model, in addition to not requiring the height of the antennas, is that instead of obtaining attenuation curves empirically, it seeks to generalise the experimental results of previous models through analytical methods, so this model requires less specific technical parameters of the area and of the transmission devices, such that it allows a random distribution of the buildings, without necessarily knowing where they are or how many there are. The restriction is that the height of the transmitting antenna should greater than the average height of the buildings [23].

The Ikegami Model. One of the main considerations on which the Ikegami model rests is that it is a theoretical development, that is, the definition of this model is not based on experimental processes. An advantage is that the model is much more general than those that are totally empirical. However, it can lead to certain inaccuracies as it is not connected to a specific environment [94].

Despite the above, the Ikegami model is applicable in case of having specific parameter values, such as the operating frequency (usually 200–800 MHz), as well as the non-consideration of environmental phenomena and characteristics of the geographical area [94].

The Walfisch - Ikegami Model. This model is a combination of the Walfisch and Ikegami methods already described in Sects. 2.2, including some corrections and empirical adjustments to the parameters used in each model.

It must be taken into account that the model is statistical and not deterministic, since the topography of the place is not taken into account and therefore it is effective only in urban terrain without disturbances in the terrain. In turn, it makes it possible to evaluate the loss of propagation whether or not there is a LoS between the receiving antenna and the mobile receiver. The ranges in which this model is applicable are [95]. The main parameters of the model are shown in the Table 4.

Table 4. Walfisch-Ikegami model Parameters [95].
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2.3 Radio Communication Link Budget

According to [96], the link budget is defined as a method for calculating the effectiveness of a cellular communications link, taking into account the physical and geographic parameters of the environment and the devices used in the communications system.

Commonly, to calculate this budget, the gains and losses of the antennas and the attenuation due to the medium must be taken into account. Generally, the link budget calculation takes into account the following parameters [97]:

  • Output Power \(P_{Tx}\),

  • Transmitting antenna gain \(G_{Tx}\),

  • Transmission Loss \(L_{Tx}\),

  • Losses in free space \(L_{Fs}\),

  • Miscellaneous Losses \(L_M\),

  • Gain in the receiving antenna \(G_{Rx}\) and

  • Losses in the receiving antenna \(L_{Rx}\).

These parameters are used to obtain the power received at the receiving antenna, so that if the estimated received power is high enough with respect to the sensitivity of the receiver, the link will be useful enough to send information. Equation 1 is used to calculate the received power.

$$\begin{aligned} P_{Rx}=P_{Tx}+G_{Tx}-L_{Tx}-L_{Fs}-L_{M}+G_{Rx}-L_{Rx} \end{aligned}$$
(1)

2.4 React Framework

React.js [98] is an open-source front-end development framework built on the JavaScript language, which was first released by Facebook (now Meta) back in May 2013 [99]. Thanks to the growing popularity of web pages as a means of disseminating information, the need to generate user-tailored content over static pages became a priority [100]. In response to this need, multiple development frameworks were created, some of them being AngularJs [101], EmberJs [102] or Backbone.js [103].

Many of the web development frameworks take advantage of existing templates, that are elements within the HTML code that are not rendered when the page loads, but that will later be loaded and altered by means of JavaScript. However, these templates have all the abstract elements predefined to be used in the creation of a user interface. As a solution to this, React proposes developing with components in mind, which are abstract units that are part of the constitution of a user interface, ranging from a field within a form, to a complete web page [99].

The first time the web is accessed, React compiles all the components that make up the page and proceeds to render them on the user’s screen. If any of the components need to update the information it presents or its state, there is no need to recompile the entire page and re-render, as each component has its own rendering function [99]. To keep components updated as their information or state changes, React implements elements called hooks, being these pre-built functions within the framework that allow storing information about the state of components in the front-end and exchange information between components, these functions always start with the keyword “use” [104].

The first type of hooks are status hooks, which store information generated by user input (components within a form being an example). Within this category of hooks we find “useState”, which declares the state of a variable that can be updated directly, and “useReducer”, which does the same thing as the previous hook, but which implements logic for updating that data for validation or preprocessing. The second type is the context hook, which allow information to be sent from larger components to the minimum units that make them up. A good example of this is the use of a light or dark theme within our website; the main hook is used with the “useContext” keyword. Another type of hooks are the reference ones, which represent information that is not visible to the user and therefore do not need to re-render the state of a component, the main hook being the “useRef” mainly used to store references to the DOM (Document Object Model). For synchronisation and work with external systems to the main website, we implement “useEffect” hook, which allow us to store the connection status with web sockets, animations and widgets created with another framework or development library [104].

2.5 Tailwind CSS Library

An essential part of any web application is the user interface, which can be cumbersome to build since you have to worry about it being functional, attractive to the end user and adaptable to a large number of devices. The functionality problem is addressed through development frameworks (with React.js being selected for this work). In the case of the styling of the pages and how well they adapt to different devices with different resolutions (a concept known as responsiveness) is addressed through CSS (Cascade Style Sheet) libraries [105]. Currently, within the main CSS libraries we can find Bootstrap [106], Foundation [107], Bulma [108], Skeleton [109] or Tailwind [110], the latter being the one selected for the development of the calculator.

Adam Wathan [111] released the first version of Tailwind on November 1, 2017, being a library focused on the creation of utilities that allow to speed up the construction of user interfaces (an example of this being classes that allow changing the layout of containers, or centring the content). Among its main benefits we can find existing classes to style components when the mouse is over them (hover event), when they are clicked (focus event); On the other hand, we can develop our website with responsive design as a priority, we can also easily change from light to dark theme thanks to the pre-built classes, reuse styles and create custom ones [112].

3 Development

3.1 Cost231 Walfisch Ikegami Model

This model takes characteristics of the most widely used models in the design of cellular communications links, resulting in a semi-empirical model. This model is especially useful and efficient in urban environments. In particular, it is a statistical and deterministic combination of how communications behave in the frequency ranges of 800 to 2,000 MHz [51, 113]. Among the parameters considered by this model are [52]:

  1. 1.

    Operating frequency [MHz],

  2. 2.

    Distance between transceivers [m],

  3. 3.

    Height of antennas [m],

  4. 4.

    Attenuation in free space [dB],

  5. 5.

    Diffraction attenuation (ceilings) [dB],

  6. 6.

    Diffraction attenuation (obstacles) [dB],

  7. 7.

    Attenuation per path [dB] and

  8. 8.

    Urban orientation effects [dB].

As determined by the general theory of link budget calculation, the Cost231 Walfisch Ikegami model follows a mathematical structure to calculate the variables required in different link conditions.

Table 5. Cost231 Walfisch Ikegami equations [52].
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Table 5 shows the main variables that the model calculates. These calculations do not consider attenuations inherent to the zone and only consider distances and frequencies, so the calculation becomes simpler. The model allows to calculate different types of loss due to other circumstances, such as diffraction loss from ceilings

$$L_{rts}=-16.9-10log_{10}(\omega )+10log_{10}(f)+20log_{10 }(h_b-h_r)+L_{ori}$$

and obstacle diffraction loss

$$L_{msd}=L_{bsh}+K_a+K_dlog_{10}(d)+K_flog_{10}(f)-9log_{10 }(B).$$

The above expressions give an idea that this model is hybrid, as it includes both empirical and analytical parameters. The rest of the expressions that comprise the [52] model are listed below.

  1. 1.

    \(L_0=32.45+20log_{10}(d)+20log_{10}(f)\).

  2. 2.

    \(L_{rts}=-16.9-10log_{10}(\omega )+10log_{10}(f)+20log_{10}(h_b-h_r)+L_{ori}\), taking into account that \(\omega \) is the width of the building where the mobile antenna is, where

    1. (a)

      \(L_{ori} = \left\{ \begin{array}{ll} -10+0.35\alpha &{} \text {if } 0<\alpha <35^\circ \\ 2.5+0.0755(\alpha -35) &{} \text {if } 35<\alpha <55\\ 4-0.0114(\alpha -55) &{} \text {if } 55<\alpha <90.\\ \end{array} \right. \)

  3. 3.

    \(L_{msd}=L_{bsh}+K_a+K_dlog_{10}(d)+K_flog_{10}(f)-9log_{10}(B)\), where B is the average of the distances between buildings, d is the distance between stations, and where

    1. (a)

      \(L_{bsh} = \left\{ \begin{array}{ll} -18log(1+h_t-h_b) &{} \text {if } h_t>h_b \\ 0 &{} \text {if } h_t\le h_b,\\ \end{array} \right. \)

    2. (b)

      \(K_a = \left\{ \begin{array}{ll} 54 &{} \quad \ \text {if } h_t>h_b \\ 54-0.8(h_t-h_b)&{} \quad \ \text {if } h_t<h_b; d_{km}\ge 0.5km\\ 54-1.6(h_t-h_b)d&{} \quad \ \text {if } d_{km}<0.5km,\\ \end{array} \right. \)

    3. (c)

      \(K_d = \left\{ \begin{array}{ll} 18&{} \qquad \qquad \!\text {if } h_t>h_b\\ 18-15\frac{h_t-h_b}{h_b}&{} \qquad \qquad \! \text {if } h_t\le h_b\\ \end{array} \right. \)

      and

    4. (d)

      \(K_f = \left\{ \begin{array}{ll} -4+0.7(\frac{f_{MHz}}{925-1})&{} \qquad \ \text {if } \text {medium size city}\\ +1.5(\frac{f_{MHz}}{925-1}) &{} \qquad \ \text {if } \text {downtown}.\\ \end{array} \right. \)

The equations corresponding to the complete model were considered in the application developed in this work. The Cost231 Walfisch Ikegami model offers a complete model, since being developed through three different models, each with its own vision and context, a model is obtained that combines the theoretical and practical, unlike other models that are focus on a single aspect.

3.2 Link Budget Software Calculator

Looking for the software tool to be useful and accessible, a web application was developed to solve multiple requirements related to the calculation of the radio communication link budget. Figure 1 shows the corresponding mockup, the which follows a two-column layout.

Fig. 1.
figure 1

Mockup for web view of the calculator.

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The first column presents the graphical interface for calculating the link budget using the Cost 231 Walfisch Ikegami model. This section has a selector component that allows choosing the propagation model, then there is a descriptive text about the propagation model presented, followed by the block of parameters used within the model. Finally, a section for the presentation of results is placed.

In the second column, there is a dictionary that includes the most commonly used units in radio communications, which begins with a header that includes three parameters to display. In the lower right part of the dictionary, a unit converter is displayed that serves as a query tool, giving the possibility of choosing which conversion to perform and the units involved in it.

The design takes into account the use of the minimum number of decorative elements, seeking to simplify its use. Thanks to the use of the React.js framework [98, 104], all the functionality can be kept in a single window, reducing the number of events required to access the utilities of the calculation tool, resulting in a comfortable and highly efficient user experience.

4 Results and Discussion

4.1 Web Application

As described in Sect. 3.2, the mockup presents a preview of the final application. Each section of the final web application is described in detail in what follows.

The first part of the link budget calculation is shown in Fig. 2. The component has rounded edges, a propagation model selector, then an overview of the model used along with the first block of parameters needed for the calculation, each parameter having a custom units modifier. It should be noted that for the version of the software described in this work, only a single propagation model was implemented, as this is a work in progress. However, other path loss models can be added straightforwardly.

Fig. 2.
figure 2

Propagation model section.

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The second part of the calculation component is shown in Fig. 3, which contains a button to add buildings, text fields to enter the height of each building and a button to delete any particular building. At the bottom of these elements, there is the collection of text fields to enter the distance between buildings, which can be dynamically modified.

Fig. 3.
figure 3

Buildings section.

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In the last section of the component, shown in Fig. 4, there is a button for the calculator to process the input values and display them in the results section. The results obtained correspond to the reception power when there is a LoS between the antennas and when it does not exist.

Fig. 4.
figure 4

Results section.

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Figure 5 displays the unit dictionary and a unit converter that serves as a query and reference for users of the application. In the design of radio frequency links it is common to use different units for the same parameter, depending on the manufacturer, operator or developer.

Fig. 5.
figure 5

Measurement units dictionary and converter.

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It should be noted that although in this paper the calculation of the link budget is presented using the Cost231 Walfisch Ikegami model, however, the software tools used and the complete conception of the tool allow the inclusion of other propagation models, which can take into account in its equations different types of losses and conditions.

4.2 Study Case

In order to validate the operation of the software, the results were compared between a link budget calculation case already documented, with respect to the results obtained through the developed application.

Using the experimental values obtained in [114] by means of a computational simulation of losses using the Cost 231 Walfisch Ikegami model, a summary of the results obtained by [114] can be observed in Table 6.

Table 6. Parameters of the model for the case of study, obtained from [114].
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For the verification experiment, the parameters of the base case were taken as input into the developed web-based calculator. The values obtained are shown in the Table 7.

Table 7. Losses obtained using the developed calculation tool.
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A difference between the values obtained by the calculation tool and the reference computational simulation is observed. In this case, this discrepancy may be due to the fact that in the original experiment numerical adjustments made to benefit the results required for that experiment. This is common, since the link budget is made based on parameters and specific geographic areas [114].

5 Conclusions

In this work we developed a novel web-based link budget calculator considering the Cost 231 Walfisch Ikegami model for urban environments in the 800–2,000 MHz frequency band of cellular wireless networks, which allows the application to yield adequate and reliable accuracy of results. The use of software tools for simulation and design of communications systems is essential, since physically implementing a communications system implies the use of relatively high economic and infrastructure resources, depending on the type of system.

Because software tools are essential, it is vital to have applications that are useful, accessible and suitable for design. In the present work the philosophy of developing an application that allows users to carry out for link design is followed.

Although there are various propagation models available for the calculation of link budget in the same frequency range, we implemented the Cost 231 Walfisch Ikegami model because is one of the most studied and updated models for 2G–4G cellular wireless networks at urban environments. Nevertheless, the modularity of the full-stack application allows to add additional path loss models if necessary.

In this sense, the developed application allows to perform the link budget with the aforementioned model, as it is one of the essential processes in the design of radio frequency systems.

As reported in Sect. 4.2, the calculation precision of the implemented model is adequate, since the result provided by the application is corroborated with results already documented in scientific literature, with a small value lag. This lag is attributed to the way of implementation of the reference case.

The application is accessible, it is completely developed with free software tools, and because it is a web application, it is highly compatible with any computer where it is run. The design and implementation of the application is scalable, as other functions can be implemented without the need to modify the original design model.

In terms of advantages regarding the use of commercial applications or free online developments, the calculator proposed in this paper has a solid scientific and theoretical foundation, since its development is based on the Cost231-Walfisch-Ikegami model theory, therefore that the accuracy of their results are reliable. It should be noted that in the case of needing to scale the tool or extrapolate it to other models, it is possible, and the base theory would be used to continue providing precision to the resulting calculations.

The research in which the proposed software tool is embedded is in progress, so for the moment it is only available in development mode; however, it is intended to take it to production mode once the investigation is completed.

6 Future Work

This research can be extended in the following pathways:

  • To perform experimental tests to verify the resulting link budget.

  • To include the implementation of different propagation models in the software.

  • To include different operating frequencies in the software.

  • To determine a specific case study to test the software.

  • To consider other communication systems that require the calculation of a link budget.

  • To increase the options available in the graphical interface of the software.

  • To include graphic tools for visualising results.