## 2 Related Work  
UAVs are taking over important roles in society, especially in the field of telecommunications, where they are the main solution when temporary support and a relatively low cost-benefit ratio are required. Works such as [6; 17] try to minimize the UAV battery cost, which increases efficiency and uptime. One of the most common solutions to this problem is to optimize the trajectory and find the optimal positioning of the UAV to increase energy efficiency. In [18], a method for tracking an energy-efficient trajectory is proposed to minimize the energy consumption of UAV BSs. This solution improves UAV communication and mobility by meeting reliability and latency requirements for IoT applications and users in hard-to-reach areas. Authors [19] present an intelligent positioning mechanism for UAV BSs. The SUAP system is used to improve the operation of mobile networks in emergencies. The placement of UAV-BS was modeled as an optimization problem to obtain the necessary network resources and provide temporary connectivity to improve system performance. In the work [20], a route-planning method was developed for UAVs deployed in areas with limited network coverage for applications that stream data. Unlike other planning, this requires the QoS served by the UAVs to preserve bandwidth. A mathematical model was presented to formulate a combinatorial optimization problem that attempts to minimize the flight cost of the UAVs covering the area of interest to be minimized. The heuristic method derives the number of UAVs and determines their flight path, minimizing energy consumption and improving link throughput. Similarly, the authors [21] propose the 3D deployment of a group of UAVs, called AAPs, for uplink communications while considering inter-cell interference and energy consumption. The positioning problem is divided into two parts: the vertical one, which is analytically derived from the inter-cell interference and the energy consumption of the AAP, and the second part, the horizontal positioning, which is treated with a circular packing problem that is solved with a multilevel regular polygon algorithm that maximizes the packing density of the coverage area of APP and also determines a necessary upper bound on the number of APPs that can be deployed in a circular area. We can find real-time solutions to problems that are extremely difficult to solve. In [22], an efficient strategy for optimal UAV positioning in terrain with real obstacles is developed. The strategy is adaptable to the degree of line of sight, depending on the average real-time position. The work proves that proper global positioning is sufficient to determine the linear search path of the target area. The proposal showed superior performance gains compared to strategies based on probabilistic models. For real-time problems, the authors [23] proposed an autonomous solution for 3D UAV locations without the need to use GPS or other detectable mobile signals transmitted by the UAV. The method uses existing mobile infrastructure to allow the UAV to determine its location based on the positioning of four neighboring base stations. Heuristics were used for the solution to determine the optimal boundary. This approach uses supervised learning and reinforcement learning to solve the problem in real time, which is extremely important for such applications, unlike other techniques such as optimization, which have difficulty working in real time. In [24], a form of computational learning is presented to achieve UAV self-organization in a wireless communication system. The approach uses the strategy of parameter mutation combined with virtual force to achieve flexibility in the instantaneous motions of UAVs to achieve significant improvements in key performance indicators. The work of [25] proposes an approach to minimize the time to detect power outages in the main grid and provide on-demand coverage based on UAV-BSs.