# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
###### Abstract
For example, increasing TTT reduces HOPP, but increases RLF at high UE speeds. Thus, finding the optimal balance remains a challenge in managing and improving KPIs. Although several optimization methods are available in the literature, there is still a gap to be filled in finding the ideal solution for optimal triggering settings for HOM and TTT. Mobility management is considered to be a crucial design challenge in current and future mobile communication systems, due to the rise in mobility-based users and applications. The use of high frequencies for larger bandwidths, smaller cells, and HetNets for increased user capacity and constant data demands have already emphasized the significance of handover management. In this survey, we concentrate on the current and future requirements related to mobility and handover management in mobile communication networks. To gain an understanding of the necessities and techniques for effective handovers, we scrutinized different handover approaches including RSRP, velocity-based, and advanced methods such as machine learning, deep learning, and soft computing. It is vital to comprehend these recent approaches as they will play a crucial role in the intelligence and management of 5G and beyond 5G mobile networks [18; 19; 20]. There are numerous methods for managing HO that have been suggested in the literature, but none of them is perfect enough to provide a solution. There is still a lot of research required to be done in this area to find an optimal solution for 5G and B5G networks, where the number of HOs will increase significantly. Therefore, continued research into handover management in 5G and HetNets is imperative in order to fully realize the capabilities of these networks. This survey's primary contribution can be summed up as follows:  
* This research focuses on HO and mobility management procedures using self-optimization, soft computing, and ML/DL approaches. These approaches have been thoroughly discussed, highlighting the most cutting-edge methods available in various categories. As a result, this study will pave the way for future research on HOs in mobile networks. * To better understand the impacts of different parameters on HO, available methods are classified into different categories. * Highlight the available challenges for each grouped category and tabulate them in the form of a summary that includes the proposed scheme, flaws, and findings, as well as the KPIs and HCPs used. * The \"Open Issues and Future Directions\" section highlights potential future research areas for handovers in 5G, Beyond 5G (B5G), and 6G communications. This survey paper endeavors to furnish a comprehensive examination of handovers in HetNets. This paper starts with a review of related surveys. The background section provides an overview of handovers in HetNets, including handover control parameters, self-optimization network in HetNets, and handover challenges. The central focus of this paper is on handover decision techniques, which include velocity, RSRP, fuzzy logic, machine learning/deep learning, heuristic approaches, and spatial information. The paper also sheds light on open issues and potential future directions for handover decision techniques. The conclusion summarizes the key contributions of the survey paper, and provides recommendations for future research. The outline of this article is presented in Figure 2.