# Handover Management for Drones in Future Mobile Networks--A Survey
## 1 Introduction
Due to spectrum availability concerns, current investigations are underway using Wi-Fi, 802.15.4, and remote-control channels [10; 25; 26]. Other existing technologies have also been analyzed for wireless drone support such as 802.11, 802.15.4, Third Generation (3G)/Long-Term Evolution (LTE), and infrared. The authors in [27] examined the issue of drone interference in the context of adopting drone communications in the cellular infrastructure. Cell coverage and drone support have also been explored in the literature. However, extensive studies are still required. Despite the potential prospects of drones, a range of practical challenges must be overcome to effectively apply them in each networking application. For instance, when using drone BSs, the most critical aspects to consider are performance characterization, drone implementation in optimal Three-Dimensional (3D) environments, wireless and computational resource management, flight time, trajectory optimization, and network planning. Handling channel modeling, low-latency control, 3D localization, and interference management are also key challenges in the connected drone concept. Among these challenges, efficient mobility (handover) management is a significant factor that must be addressed for drone BSs and drone UE scenarios [28]. To ensure smooth and reliable connection services while users are mobile, a secure connection must be established in addition to an efficient handover process. Handover technology is the method of maintaining a continuous connection when a user moves from one cell to another without disrupting service. Serving signal level reduction, load balancing, and high error rates are among the factors that lead to the formation of handover actions. When one or more of the aforementioned factors reach an undesirable level, the connection must transfer to a suitable alternative cell for more reliable, stable, and seamless service. Although this process regularly occurs, it creates many challenges when the UE is a drone. Several challenges must be overcome to manage handovers in mobile networks. System complexity increases with drone implementation due to their unique features. The drone's flight may be controlled via LoS paths, even though the interference scale is greater than that in conventional terrestrial networks. Compared to the ground UE, the drone UE has a lower coverage probability since its antenna is tilted downward and the drone's interference is overpowered by LoS [29; 30]. Due to the higher speed of drones compared to that of the ground UE, the handover rate is comparatively higher. Since drones are supported by the sidelobe of the terrestrial antenna, many handovers will probably occur [31]. Consequently, the Quality of Service (QoS) will noticeably degrade [32]. Handover of drones must be professionally and expertly managed in terms of the techniques used to address handover challenges compared to current handover management in terrestrial UEs. Techniques and algorithms employed in terrestrial UEs may not be suitable for drone network applications due to their distinctive features. The key objective for using such methods is to deliver high-quality service and reliable communication while maintaining seamless handover between drones. Solutions have been investigated in several related works, but many challenges still remain. The provided algorithms are for both scenarios: drones acting as BSs and drones serving as UEs. The former scenario is under examination using the previously suggested algorithms. Drone BSs are assessed in two separate movement scenarios: drone BSs travelling in random directions at the same constant speed and drone BSs moving at various constant speeds. In future mobile networks, node movement prediction is a key recommended technique for enhancing drone network service. Many contemporary methods are based on distance measurements and projections. Machine learning-assisted studies have been developed to support drone networks in acquiring certain patterns.