## 2 Related Work
The proposal uses UAV-BSs and a Q-learning algorithm to autonomously detect coverage gaps and then deploy wireless communication devices considering wireless backhaul and user demand. Machine learning assisted the UAV BSs to eliminate the human-in-the-loop (HiTL) model to optimize the 3D placement of UAV-BS at different angular positions and maximize the number of associated users. In [26], a UAV control system that follows Bezier curves is proposed to improve the approach to groups of users on the ground by increasing the coverage as the user moves. The proposed algorithm is based on a mixed-integer nonconvex programming formulation for an NP-complete coverage problem. The solution not only optimized the positions of UAVs in 3D space in polynomial time but also managed to assign flight paths by solving the assignment problem with Bezier curves. The authors in [27] study the problem of energy consumption in networks of cell-connected UAVs by considering the urban communication environment, UAV power consumption, mission duration, UAV path, and station-based communication to minimize UAV power consumption by ensuring stable connectivity throughout the flight in the coverage area. The proposal presents efficient results based on graph theory by applying the convex optimization technique and shortest path algorithm with dynamic weighting to the graph. The above studies present different solutions to solve the problem of optimal UAV positioning, coverage area, and energy efficiency of UAVs to improve communication with users and applications in the communication network. In the literature, several studies propose the use of machine learning to perform the optimization or prediction of certain variables such as the distribution of users, the positioning of UAVs, the use of the channel, and the congestion of the network to improve the scheduling and deployment of UAVs to offer a more efficient temporary infrastructure. In [28], an algorithm is presented that aims to optimize the positioning and transmission power of UAV-BS and also improve energy efficiency, minimizing the number of users not served by the network. The proposal uses a Q-learning-based algorithm where the reward can be adjusted and a balance between the number of unserved users and energy consumption is achieved. The main goal is to make the UAV-BS adaptive and enable it to make autonomous decisions to achieve better coverage, reduce interference, and achieve energy efficiency. In [29], visible light communications (VLC) is enabled by a new UAV deployment framework to dynamically optimize the placement and assignment of users to UAVs based on a VLC network. The proposal seeks to minimize transmission power while meeting the lighting and communication requirements of individual users. The solution uses an algorithm based on gated recurrent units (GRU) and convolutional neural networks (CNNs) to predict the illumination distribution in the long-term history to find the optimal position for UAV deployment and efficiently improve user communication and illumination. In [30], a solution using UAV-BSs is developed for transient user demand problems that overload the mobile network due to limited infrastructure capacity during peak hours in tourist regions. The solution defines the effective coverage area and the vertical derivative of the UAV altitude. Then, the automatic UAV-BS deployment algorithm determines the minimum number of UAV-BSs and their optimal positioning while improving user communications. Ref. [31] presents a solution to find the ideal location of UAVs in a heterogeneous network to support the base station serving ground users. The proposal formulates multiple UAV placement of multiple UAVs, resource allocation, and user assignment as an optimization problem to maximize the sum rate of all usersunder constrained QoS requirements. In [32], an artificial intelligence (AI) is proposed to overcome the challenges posed by random fluctuations of the wireless channel, blockages, and user mobility in a UAV-assisted network (UAWN).