# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 2 Related Work  
A heuristic technique was suggested by [PERSON] et al. [24] as a way to locate a drone BS in any area with a variety of user densities. With a set of users and a quality-of-service target in mind, the suggested solution looks for the fewest drones necessary to cover all of the users (QoS). After that, the drone BSs are eliminated, whose removal has no impact on the network's quality. Because properly situated drone base stations can serve the greatest number of users, [PERSON] et al. [25] emphasized the drawbacks of the drone BSs placement challenge. The impact of the environment on the line of sight should also be considered. For this reason, the authors developed a 3D placement challenge to increase the network's revenue as much as possible. A similar mixed-integer non-linear quadratically constrained optimization problem was also defined by the authors, and they suggest a numerical solution that is computationally effective by applying some mathematical tricks. [PERSON] et al. [26] emphasized the advantages of a drone's dynamic repositioning during the service in response to the active and mobile user. They increase the spectral effectiveness of small drone cells. Three algorithms for self-determining dynamic relocation for drone BSs were suggested and tested by the authors. Due to Wi-Fi's limited throughput and the uncontrolled nature of the unlicensed spectrum in which it works, [PERSON], et al. [8] chose LTE technology over it. LTE femtocell base stations are mounted on the drones. The deployment tool's algorithm, which the authors investigated, consists of four distinct steps. The development of the deployment tool enables determining the number of drones that are required as well as their best placements to maximize user coverage. The user is connected to the base station with the lowest route loss after the path loss is assessed and generated for the traffic. The first findings of this study indicated that it is promising for an examination of the usage of drones in emergency situations. Based on the scenario and drone assumptions taken into consideration, it was found that the optimal altitude of a drone BS that achieves the necessary coverage with the least transmit power also delivers supreme coverage with two drone BSs in the presence and absence of interference [27]. For a variety of network design factors, including the number of served users or the sum rate of served users for heterogeneous rate requirements in a clustered user distribution, [PERSON] et al. [28] suggested a backhaul restricted optimum drone BS placement algorithm. Additionally, the authors looked into the stability of drone BS positioning and the potential impact of user motions on the suggested ideal solution. The lack of dependable wireless backhaul links restricts the deployment of drone base stations (drone BSs). To determine user priorities, two methods are introduced: network-centric and user-centric. These approaches are based on sum rate, pricing differential, signal strength, and content demand. For each approach, a drone BS placement that is 3D backhaul-aware is discovered. In the network-centric and user-centric frameworks, respectively, the total number of serviced users and sum rates are maximized. A macro BS (MBS) and many DBSs that rely on the wireless links to the MBS for backhauling were suggested by [PERSON], et al. [29]. The authors suggested an approach to discover effective 3D placements of DBSs together with user-BS relationships and wireless backhaul bandwidth allocations to optimize the logarithmic sum rate of the users while considering both normal and uRLLC users. Allocating backhaul resources, user association considering user kinds and 3D placement of DBSs are all taken into account simultaneously. A fresh issue formulation that takes fairness into account is offered as well as a numerical solution approach. Using a decomposition method, the user-BS association and bandwidth allocations are determined.