# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 5 Related Works  
The literature is crucial when it comes to incorporating UAVs into future networks. This section includes a review of relevant studies. Several research techniques, as well as the findings and outcomes of these efforts, are briefly described along with suggestions for future improvements. These papers mostly discuss UAV HO decision algorithms based on mathematical models or machine learning techniques. UAV architectures and new use cases are also examined. The following research papers are listed in chronological order and a summary of challenges and previous contributions has been provided in Tables 2 and 3. This overview can provide insight into the best UAV integration techniques and serve as inspiration for future efforts. UAVs and cellular networks are becoming increasingly popular research topics. Recent proposals with unique solutions have been made to tackle scientific, technical, socioeconomic, and security issues. Several surveys, examples, and tutorials are also presented in the literature to provide clear information regarding this research topic. These works allow the research community to keep track of ongoing studies, and aids practitioners and researchers in acquiring necessary information. Several surveys and tutorials have focused on (a) the possibility of integrating UAVs with 5G/B5G cellular networks from the aspect of UAV-based cellular communication, (b) current advances, future trends, and challenges for UAV-based cellular communication, and (c) extensive analysis and performance studies regarding a specific communication challenge, such as channel modification. The authors have presented a method for determining a UAV network's coverage. Constraints in the UAV network, battery capacity, and HO management have led to communication disruptions and other challenges, such as regular HOs. Since UAVs are positioned at different coverage areas and heights, traditional HO algorithms do not work. To maintain coverage, the recommended solution uses RSS to change the height and separation distance of each UAV. Several simulations were accomplished to determine the likelihood of a smooth transition using seamless HO success probability (Ps) and false HO initiation probability (Pf). Since the coverage algorithm matches all UAV heights to a value (the lowest coverage and heights to the lowest feasible value), the spacing between the UAVs can be modified using Pf and Ps. Pf grows as the vertical space between the overlapping sections shrinks. These sections shrink as the distance between the UAVs increases and Ps decreases. The chances of achieving a smooth HO decrease as the average RSS measurement increases. The chances of an inaccurate HO begin to lessen as the average RSS measurement duration increases. According to the simulation results, the proposed technique is a strong candidate for UAV networks. The method performs admirab when it comes to simulations. However, a more realistic scenario must be considered, such as the UAV's payload, the radio range of the BS, and other factors. Moreover, the coverage algorithm equalizes the RSS of each UAV, which may or may not be acceptable in practice. In 2004, 2010, and 2012 [86; 87; 88], a novel HO decision technique was developed by establishing innovative HO criteria. The HO decision was made using a fuzzy inference method that considers several factors in HO decision situations. This paper examined fuzzy MADA methods and various proposed methods based on this approach, as well as their sensitivity. The HO approach based on an optimization algorithm was also suggested for cellular networks. In 2004, 2012, and 2016 [88; 89; 90], the HO method was discussed for 3D aerial networks. As we know, the 3D method differs from the classic 2D approach. The height of the UAV and the distance between UAVs must be adjusted. The likelihood of seamless successful HO and false HO was also evaluated for the best coverage assessment technique. The authors devised the HO decision to select the appropriate network.