Comparison of Outdoor Radiowave Propagation Models for Land Mobile Systems in the 3.6 GHz and 6 GHz Frequency Bands  
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and areas per base station at a given power level. This technological development trend implies the need to revise the previously used wave propagation models and to develop new wave propagation models that are accurate for high-frequency mobile telecommunication networks and that simultaneously apply modern soft computing techniques [2]. In order to understand what wave propagation models do, it is worth formalizing the problem. A wave propagation model can be considered as a system with \(n+m\) inputs and one output, describable by a \(\Gamma\{\cdot\}\) operator. The \(n\) inputs of the system describe the transmitter under investigation, while the \(m\) inputs describe the environmental parameters that affect the wave propagation characteristics, such as the terrain between the transmitter and the receiver, the weather conditions, and the vegetation conditions. It is not explicitly indicated in Figure 1, but it is emphasized that for most wave propagation models, the important parameter is the height above sea level and/or ground level of the receiving point, and its geolocation can be used to determine, for example, the terrain section referred to earlier. The first \(n\) inputs, which depend on the transmitter parameters, can be considered as direct or external inputs to the system, while the remaining \(m\) inputs are internal, indirect parameters of the system, which are not directly related to the transmitter parameters. The wave propagation model as a system can therefore be formalized as follows:  
\[\text{PL}=\Gamma(k_{1},k_{2},\dots,k_{m})\{x_{1},x_{2},x_{3},\dots,x_{n}\}. \tag{1}\]  
The number of wave propagation models used in network planning and spectrum engineering is large, but they can be divided into different categories depending on the internal behavior of the model, i.e., the \(\Gamma\{\cdot\}\) system operator in the example above. The first large category consists of empirical wave propagation models [3]. These models describe the propagation of electromagnetic waves in a given environment on a statistical basis, using relationships derived from measured data. The measurement data can be used directly, for example, to train a neural network-based empirical wave propagation model that, given a sufficiently large amount of measurement data, can quickly produce accurate results that match the measurement results using a simple algorithm [4; 5]. Another typical approach in this category is the use of wave propagation curves or curve sets constructed from measured data, where the wave propagation model algorithm can determine the path loss associated with a given input parameter set by interpolation and/or extrapolation. One such empirical wave propagation model is ITU-R P.1546 [6], which is widely used and accepted in international interference calculations. In this paper, we will discuss in detail this empirical wave propagation model, which can be appropriately applied to the 3.6 GHz frequency band. Although the model was only applicable up to 3 GHz until its previous version [7], it has been extended to 4 GHz and is now considered a suitable model for this band. A broad family of empirical models are those that do not work with curves built from measured data, but with closed mathematical formulas containing constants assigned to  
Figure 1: System model of a general wave propagation model. specific installation coverage, frequency bands of use, base and mobile station antenna heights, etc. The empirical models thus provide fast and simple calculations that can be used effectively when planning radio networks or performing spectrum management tasks. Importantly, they do not explicitly model individual propagation phenomena (e.g., reflection, scattering, and diffraction), but are based on averaged environmental effects.