## 5 Simulation and Experiments  
In this section, we present the simulations and experiments we used to study the behavior of the mobile network in Manhattan, the prediction to locate the regions affected by congestion, resources used in each macrocell, and the UAV-BS, which will support the affected network. For all experiments, a simulation scenario was developed in Python to show the effectiveness of the proposed solution. For the simulation, a working terrestrial network with congestion problems was considered, and the need to temporarily balance the quality-of-service parameters in the region by the mobile base stations was determined. In this paper, we have divided the work into two simulations. The first one aims to generate the mobile network utilization; the network utilization database is used as input to the predictive models to find the congested \(M\). The second simulation is performed using the UAV-BSs to support the congested \(M_{s}\). For the simulation, we use the physical layer parameters and simulation parameters recommended by 3 GPP [57]. The simulation attempts to replicate the behavior of a large urban area, where there are effects that can directly impact the performance of mobile services on a day-to-day basis, and where it can be expensive to deploy new fixed structures. An example of events that can affect performance is the tidal effect, which represents the behavior of user traffic throughout the day. This effect refers to the daily dynamic movement of users leaving the semi-urban area for the urban regions and returning at the end of the day, similar to the movement of sea levels up and down. Identifying and defining traffic load patterns in \(M\) are important measures for network design and load balancing, as they provide an important solution for service operators facing network congestion problems or link capacity overcommitment [58]. For this study, we used the Uber platform's database of passenger boarding and alighting in the city of Manhattan in April 2014 from the repository [59]. The database contains date/time, geolocation, and codes related to the service offered by the platform. For the simulation, only date/time and geolocation were used to leverage the behavior of users in the Manhattan area. We analyzed daily behavior to identify seasonal variations or user trends between days of the week. We separated date and time and added day-of-week data to help the model understand behavior. In addition to the database treatment, it was necessary to define the location of each \(M\) in the study region. For this, we used the user base itself as a reference to determine the ideal point on the site. We used 60 \(M\) in the simulation, where \(M=60\) in the K-means algorithm, resulting in 60 positions related to the set of \(M\) in the Manhattan region. In the first phase, we considered a set of \(M\) deployed in the Manhattan region, which is one of the most populous regions of New York City, with an area of approximately 58.7 km\({}^{2}\) and a population of 1,694,251, with a population density of 28,873 hab/km\({}^{2}\). In this first phase, we simulated 30 days referring to April 2014, based on the user base. We used 60 \(M\), but the number of users depended on the day and time, depending on which database was used. We consider those who have downlink requests that need to be served as active users. These users have different characteristics, such as their location and their downlink transmission requirements. Throughput can be calculated in terms of perceived SINR, as shown in Equation (23); minimum throughput requirements are modeled as SINR requirements. The user's assignment to \(M\) is determined by ranking the \(M\) based on the SINR, and the possible assignment is determined. Otherwise, it will move to the next \(M\) with the best SINR. A constant bit rate (CBR) traffic is applied to users with a minimum range of 0.5 to 2 Mbps for downlink transmission. COST 231 Walsch-Ikegami [38,39] is used for signal propagation from \(M\) to user devices.