## 2 Related Work
The solution uses multiple UAVs simultaneously as a base station that can randomly adapt to the hovering environment. Moreover, AI helps iterate the UAVs in the swarm for cooperative optimization of the system and effectively improves resource allocation, path design, and network performance. On the other hand, a new approach based on machine learning methods for predicting the location of mobile users using UAVs is presented in [33]. The solution uses partial location information collected and stored in each UAV to train the deep neural network and perform the prediction. In this paper, a Lyapunov-based optimization framework is developed to decompose the original problem based on the prediction results. Then, an iterative optimization scheme is developed along with successive convex approximation techniques to solve the subproblems and efficiently improve the service coverage for users. Recently, the authors of [34] presented a new proposal for the predictive use of UAVs as a temporary base station to complete the terrestrial mobile communication network when the downlink is overloaded. An approach based on a weighted expectation maximization algorithm is proposed to estimate user distribution and traffic demand. To ensure the information exchange between BS and the UAV, a contract theory is derived analytically and implemented considering the necessary constraints. The proposal formulates an optimization problem for the optimal deployment of UAVs in the hotspot area such that the utility of the congested BS is maximized and UAV operators can provide efficient communication services to hotspot users. As shown in the above work, several solutions are presented that aim to solve the main problems that hinder both the service provided by UAVs and the communication networks, directly affecting users and service providers. Our work can be considered as an alternative to the coverage problems and overhead caused by the fluctuation of user demand in next-generation 5G networks. In contrast to the presented work, this paper proposes an intelligent system for regions that need to improve user coverage and services offered by concessionaires without having to set up new fixed structures. Our research presents a system that offers mobile base stations that take into account the temporal and spatial flow of users in the region, so that UAV-BSs will be able to support in strategic locations where the user density is higher, such as the peak hours presented in [35]. The advantage of a system that takes into account time and space is the flexibility that the concessionaire can have in deploying the mobile structure only when necessary, avoiding the idling that occurs with fixed structures in regions where the user density is dependent, for example, diurnal. Figure 1 clearly shows the behavior of these regions in a large city, which can be represented by traffic jams, commercial areas, or any type of temporary event with seasonality in time and space. In addition, the system can be used to solve other problems, such as compensating for areas where telecommunication service is difficult due to adverse conditions or network unavailability. Telecommunications service based on UAV-BS is ideal for problems of this type because of its flexibility and speed of deployment, as well as lower operating costs.