# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 5 Related Works
The machine learning technique is seen as a promising approach in this field since it can predict node mobility. Currently, prediction solutions are based on distance measurements. To address a two-dimensional issue, a categorization of movement to other classes based on nodes' prediction of near future positions has been proposed. Acceleration also has a significant impact on the likelihood of 3D node movement. The motion trajectory was calculated using state transition equations to determine the object class. The calculations were then used to clarify the mobility parameters. To complete these procedures, several steps must be accomplished. The most important step is to use an online class identification module to determine the classes and parameters that were unspecified but acquired from observed trajectories, while keeping in mind that each UAV has its tracking system, including Automatic Dependent Surveillance-Broadcast (ADS-B) technology and GPS positioning. The Kalman filter was used to achieve 91% accuracy in motion profiling. The online module generated more classes over time. Kalman filtering with intermittent observation forms the backbone of this approach, allowing for simultaneous estimation of the target vehicle's position, velocity, and acceleration using the relative position and velocity information provided by the radar system. Kalman filtering, which contains two sets of time updates, can be used to solve the state transition equations and obtain a reliable approximation of the state vector. The next state vector (position of flying item) is predicted using time update equations. Time update equations are used exclusively when no measurement is available to predict in the case of intermittent observation. The optimal state estimate of singular systems provides a solution for this system given an unknown input. In 2016-2021 [28; 47], the authors proposed an efficient HO mechanism for UAV networks. UAV network services differ from typical networks since the HO process is carried out in 3D rather than 2D. To enhance network services, this technology adjusts the height and distance between UAVs. The ideal coverage selection technique is assessed using the seamless HO probability and Pf. To ensure that each UAV covers the same area, the height of each UAV must be adjusted to account for physical limitations. A seamless HO is possible in certain circumstances. Ps and Pf have been modeled in numerous scenarios to examine how they change. A large number of graphs were obtained for investigation and evaluation. The vertical distance between the overlapping sections becomes smaller as Pf becomes higher. The overlapping area shrinks as Ps shrinks. Overall, this technique can help UAVs preserve the environment. The battery can last longer by avoiding frequent HOs. The proposed method can assist in optimizing a UAV network by determining the ideal overlapping region. By assigning the same RSS to all UAVs, UAV interference can be reduced. Although the study did outline the preferred method, several factors must be considered. The most serious issue is that adequate coverage for UAVs is difficult to achieve. If an obstacle prevents the UAV from flying to a lower altitude, for instance, the UAV's minimum height must be adjusted. The RSS level for moving UAVs must be raised to maintain smooth HO when the UAV is influenced by weather factors, such as wind. It is also necessary to consider the system's dependability and throughput rate. In 2018-2019 [99; 102], the authors realize that providers cannot sacrifice ground-level performance for aerial users by changing the BS antenna angle. According to several studies, the BS will be able to spatially separate users in 3D space by using directed antennas and beamforming, allowing for effective service of both ground and aerial users. Experts believe 5G is a good choice since it allows beamforming and high throughput connections while remaining significantly flexible.