# Handover Management for Drones in Future Mobile Networks--A Survey
## 6 Related Works
### Deep Learning-Based Technique  
Deep learning is currently becoming a key solution technology for addressing connection and mobility challenges for connected drones. Moreover, deep reinforcement learning utilizes the combination of deep learning techniques and reinforcement learning principles. Most current research focuses on deep learning/machine learning-based techniques. With the help of developments in the AI field, handover decision making, and other features (such as security challenges), these techniques can now ensure further enhancements [37]. Since learning user behavior does not require periodic updates, the predictions' precision and efficiency of resource allocation can be improved. Recent years have witnessed overwhelming developments in deep learning methods. It is now possible to integrate the underlying methods in drone networks. Accordingly, various studies have been conducted to address different issues; some of these are summarized below. In 2017 [33], the authors conducted research on the handover mechanism based on deep learning for UAV networks. The aim of the work was to obtain seamless handover by utilizing deep learning mechanisms. The model's operation is based on trajectory predictions. The drone altitude was included by measuring the handover rate in 3D and LTE BSs were used. The assessment method consists of three fundamental steps, as suggested in [124]: handover establishment, handover employment, and handover culmination. The BS handoff was determined according to the TTT criterion using a reference power level. The position of the drones was defined using the trajectory prediction algorithm. A threshold function was then utilized to decide whether or not the BSs should switch between drones. The trajectory prediction method was implemented using neural networks instead of the Gaussian regression method, which is generally employed. For the system to have memory, the recurrent neural network (RNN) model and a supervised learning algorithm were applied. The location of the new data was determined by minimizing the root mean square error (RMSE) of the distance between the output generated by the method and the target output values obtained from the training data pairs. As previously mentioned, a threshold function was used based on the TTT values to determine whether or not handover should be performed. As with conventional handover mechanisms for ground UEs, the system's efficiency decreases as the TTT value increases. The RNN model proved to be more efficient than the traditional handover mechanisms, achieving a higher success rate and lower overhead assessment in overlapping regions. Table 3 also summarizes the most recent works on drone connectivity-related issues. In 2018 [87], the authors conducted research on deep reinforcement learning for user access control in UAV networks. The authors proposed deep reinforcement learning as a framework solution to address the access control challenges for the ground users with the consideration of mobility of UAV-BS. They aimed to enable the user to be able to intelligently perform access decisions, and maximize users' data rate and reduce the handover rate as much as possible. Based on the presented simulation results the authors reported that the results illustrated the effectiveness of the proposed solution and exposed its gain as compared to the other selected benchmark solutions from the literature. In 2019 [63], the authors conducted research on multi-user access control in UAV networks based on deep learning technology. In this work, a deep reinforcement learning method was proposed to provide a centralized control of multiple users in order to enhance the data rate and avoid unnecessary handovers. The proposed method was based on the concept of enabling each user to make its independent access decisions depending on the network information. Frequent handovers must be avoided without compromising the operation of the UEs. The sole purpose is to maximize the throughput amount.