## 3 System Model  
The proposal presents an intelligent system targeting telecommunication systems that need temporary support. We consider an urban region, \(x_{area}\times y_{area}\), based on a real area with multiple macrocells deployed at maximum capacity due to the large size of the region under study. We use a database with the user's geolocation to simulate the behavior of the mobile network with its temporal and spatial characteristics. We consider the help of UAV-BSs simulating a picocell to support regions affected by high user density leading to network congestion. The architecture defined for UAV-BS is designed to concentrate service in small regions and reduce instantaneous user access to macrocells. The relationship between users, macrocells, and UAV-BSs is defined by the quality of the link based on the signal-to-interference-and-noise ratio (SINR). This link is established in descending rank order between users with the best and worst link quality. Those who could not connect are considered to be disconnected from the network. Table 1 shows the most important symbols for understanding the formulations. UAV-BSs do not have a direct connection to the network operator's network but are directly connected to the macrocell via a dedicated backhaul connection. In summary, UAV-BSs are connected to the operator via the connection to the macrocell base, so traffic is offloaded to the macrocell and then sent to the network [36]. The link between the UAV BSs and the macrocell has a high capacity because a portion of the spectrum is reserved for this link, as described in [36; 37]. The network consists of a set of macrocells M = \(\{1,2,...,M_{m}\}\) defined based on the number of users by the K-means algorithm. K-means was used to define the location of each \(M\) in the region and was also used in the UCPA step where its use is described in detail. Two main parameters are required to use K-means: the user's geolocation database and the number of \(M\) groups defined by the author. K-means then returns the subsets of users and their respective centroids used to position the macrocell. Figure 1: Control center, 2. charging point, 3. macrocell with overload effect, 4. macrocell without overload effect, 5. UAV-BS, 6. scaling of UAV-BSs. The distance between the \(M\) in the center of the region is spread to about 1 km by the K-means algorithm, which helps to minimize the interference between the \(M\). The communication between the \(M\) and the user was defined by ranking based on the SINR mentioned earlier; the better the relationship between the \(M\) and the user, the higher the probability of connection. To calculate the SINR, you first need to know the distance between the \(M\) and the \(U\), which is given in Equation (1) as \(\text{M}=(m_{x})\) and \(\text{U}=(u_{y})\):  
\[d_{m}=\sqrt{(m_{x}-u_{y})^{2}}=\left|(m_{x}-u_{y})\right| \tag{1}\]  
Here, \(m_{x}\) represents the location of the macrocell in latitude, and longitude and \(u_{y}\) represents the location of the user. Then, we used the propagation model to calculate the path loss (PL). This information is transmitted by electromagnetic waves, which have propagation loss due to interaction with the environment. To simulate the propagation loss, models that can predict this loss are needed. Several models in the literature can make this prediction. The model used in this work is the Cost Walfisch-Ikegami [38; 39], which was developed for urban use and takes into account obstructions due to building height and street width, as well as other relevant conditions in the urban area. The Cost Walfisch-Ikegami model has three basic components. The total attenuation (\(PL\)) is specified for (\(NLoS\)) conditions and calculated in Equation (2). \[PL_{NLoS}=\left\{ \begin{matrix}L_{0}+L_{\text{rts}}+L_{\text{ms}}&L_{\text{rts }}+L_{\text{ms}}>0\\ L_{0}&L_{\text{rts}}+L_{\text{ms}}\leq 0\end{matrix} \right.