## 1 Introduction  
In the current context of telecommunications, the evolution towards a fully networked environment is becoming more and more noticeable; an example of this is the concept known as IoT (Internet of Things) [1; 2] and its interoperability with traditional communications systems of any scale, which day after day is leading us to the so-called smart world [3]. With the emergence of autonomous systems reliant on real-time transmission, communication between systems, and the digitalisation of services, among others, massive data to be transmitted at high transfer data rates arise. This set of demanding requirements is being met by the leap to the fifth generation of mobile communications or 5G [4; 5; 6], which consists of applying new data transmission technologies at high power waves between 2.4 GHz and 300 GHz in wireless telecommunication networks [5; 7]. As the deployment of large-scale 5G infrastructure for the purpose of validation of the technology is extremely expensive, modelling and simulation play a fundamental role in the analysis of multiple implementation issues and potential project solutions, mitigating costs and developing time in prototypes and physical tests, as well as improving the development of the necessary infrastructure. In this sense, much of the current research is devoted to the modelling and simulation of different aspects mostly related to the technology's performance. Initially, as a 5G propagated wavelength is very short compared with the propagated distances, the ray tracing method became popular to model 5G paths. For instance, [PERSON] et al. [8] applied it to analyse the effects between a 5G wireless communication system and the industrial environment, including the present equipment, while [PERSON] et al. [9] found that since the attenuation and blockage of millimetre-wave propagation in urban environments are severe, Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) reflected rays should be the dominant propagation mechanisms. Further research has specialised in the study of propagation path loss models for 5G frequencies indoors and outdoors. For indoor environments, [PERSON] et al. [10] studied the propagation properties of LoS links at 3.7 and 28 GHz within long corridors in Korea to model and simulate the path loss, while [PERSON] et al. [11] studied the performance of 28, 39, 60, and 73 GHz waves in both LoS and NLoS scenarios in indoor environments over Tx-Rx separations of 1.5 m to 62 m with both measurements and software. [PERSON] et al. [12] simulated wave propagation including path loss, delay spread, and received power for outdoor 28, 39, 60, and 73 GHz LoS and NLoS links through Wireless InSite(r) program, finding that LoS paths have a high receiving capacity and fewer path losses than NLoS ones, while high frequencies such as 73 GHz have greater effects on propagation than low frequencies. [PERSON] et al. [13] studied 10, 17, 30, and 60 GHz links for outdoor to indoor antennas, using measurement analysis and ray tracing simulation, and found that an increase in frequency yields an increase in path loss and a decrease in the received signal strength (RSS), the delay spread, and the received power. [PERSON] et al. [14] provided a comprehensive review of propagation path loss prediction in enclosed environments for 5G networks. [PERSON]\(\acute{\text{n}}\)a et al. [15] implemented RF path loss models for cellular 5G links in a practical free link budget calculator. A further review on advanced simulation methods for 5G antennas and propagation can be found in [14; 16]. Other methods, such as those based on machine learning, have become popular for 5G [17; 18; 19; 20; 21; 22; 23]. Of particular concern to telecommunications developers is that several atmospheric conditions can affect free space wave propagation, which can complicate and degrade wireless communication links. Some of the effects with the most significant impact are the presence of aerosols, the concentration of gases, cloudiness, and precipitation [24].