# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 2 Related Work
A heuristic particle swarm optimization approach is then used to update the DBS sites, determining the user-BS associations while taking user type into account. Allotment of bandwidth for backhaul and access links [PERSON] et al. [30] focused on designing the dynamically positioned 5 G UAV base station algorithms. The main objective of this solution is to capture the spatio-temporal relationship between data demand points and depict the clusters and their centres during well-selected periods. This research simulates the behaviour of the algorithms relying on people's real routes in downtown Beijing. In a natural disaster in an extremely dense area the placement algorithm can be implemented with a little modification that forms a sample dependent on actual 3 GPP minimization of determination tests data and combines the offered algorithms in the perspective of big Internet of Things networks (IoT), optimizing the IoT gateway location equally in terms of battery life (spontaneously, less energy is needed if the IoT gateway is closer) and scalability. In another study, a new mobility model for drone-based stations is proposed, where UAVs can move freely in the network, overlooking the cell limit. To provide a responsible connection to launch backhauling, drones also need a high capability to connect via links with a fixed earthly wireless backhaul centre. Therefore, the operating cost of drones may be high. [PERSON] et al. [31] examined the latest technological advancements in drone networks and drone-assisted cellular networks. Then, while numerically demonstrating the ideal intensity and altitude of drones in various tiers, they evaluate the performance of a multi-tier drone network in terms of the spectral efficiency of downlink transmission. The study aims for the optimization of the altitude with power optimization, also dealing with traffic control of signals while keeping the quality optimized for users. The researcher in [32] overcame the complexity of bigger cellular network problems, by dividing the small cells into clusters so that they could be managed easily. Following that strategy, the small cell can be deployed through the drone along with the base station mounted on it. The study of [PERSON] et al. [33] combined two methods, first using the reference signal received power of each ground user to discover hidden mutual interference configurations and then using the K-means clustering algorithm to discover hidden mutual distance structures of the locations of serving users. The authors hypothesized that a temporary wireless network architecture might be efficiently created based on the two learning properties mentioned above. In [34], the authors presented a real-time adaptive cooperative transmission strategy as an energy-efficient UAV solution. The proposed solution utilized the infrequent battery power during data communication and live operations. The authors considered cooperative relaying communication techniques to minimize energy consumption and enhance the life of batteries operated in terrestrial nodes. To communicate in an energy efficient manner with the Low Altitude Platform (LAP), a source node is analyzed as an option to transmit the data through a direct link to the LAP and relayed links through different possible relay links in the network. This study also emphasized how a source node selects an energy-aware method to communicate with the LAP without negotiating the bit error rate to maintain a minimum level and better QoS. This solution is a hybrid architecture and is specially designed for emergency services. The game-theoretic approach is used for terrestrial terminals on relay selection in multi-LAP aerial-terrestrial systems. In [35], the authors identified the high traffic demand which leads to lower speed connections and proposed a demand-based network model by using multiple UAVs.