# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
###### Abstract  
With the rapid growth of data traffic and mobile devices, it is imperative to provide reliable and stable services during mobility. Heterogeneous Networks (HetNets) and dense networks have been identified as potential solutions to address the upcoming capacity crunch, but they also pose significant challenges related to handover optimization. This paper presents a comprehensive review of recent handover decision algorithms in HetNets, categorizing them based on their decision techniques and summarizing their input parameters, techniques, and performance evaluations. Our study highlights the technical challenges and opportunities related to handovers in HetNets and dense cellular networks and provides key findings from recent studies. The significance of this survey is to provide a comprehensive overview of handover decision algorithms in HetNets and dense cellular networks, which can aid in the development of more advanced handover optimization approaches. handover; self-optimization; heterogeneous networks; mobility management; handover control parameters; mobility robustness optimization; time-to-trigger; small cells; 5G networks; handover margin +
Footnote †: journal: _Sci_  
The small cell base stations (BS) are defined as low-power nodes that cover a limited range of up to a few hundred meters. These BSs can also operate independently or in conjunction with the macro cell [8; 9]. mmWave communication and network densification have greatly enhanced bandwidth, capacity, and data rate, as well as QoS, but at the expense of mobility management and handovers (HO). Because both concepts rely on the idea of small cells with a relatively limited coverage footprint, resulting in more frequent HOs. In the case of network densification, the coverage area of small cells is reduced intentionally to accommodate more user equipment (UE) by deploying more BSs based on the principle of frequency reuse. In contrast, increased propagation losses limit the expansion of the coverage area for mmWave communications [10; 11]. Due to the smaller cell coverage area, UE must perform a higher number of handovers in certain scenarios, leading to several handover-related issues that must be evaluated as key performance indicators (KPI), such as handover ping-pong (HOPP), unnecessary handovers (UHO), handover failures (HOF), handover latency (HOL), radio link failures (RLF), interruption time (IT), cell dropping ratio (CDR), and cell blocking ratio (CBR). This affects throughput, which is closely linked to the number of handovers, resulting in a decrease in QoS and user satisfaction. The research community has focused primarily on improving mobility and HO management to reduce the number of HOs and enhance related metrics such as throughput, cost, and user satisfaction [12; 13; 14]. Proposed HO approaches were designed based on various scenarios and situations, with soft computing methods like fuzzy logic controllers widely used in these networks for traffic management, congestion control, decision-making, and network optimization. Additionally, deep learning (DL), machine learning (ML), and metaheuristic approaches were also applied, along with several self-optimization algorithms developed based on parameters such as UE velocity, reference signal received power (RSRP), and geographical information. Figure 1: Mobile Cellular Generations with their Applications. The Third Generation Partnership Project (3 GPP) introduced the Mobility Robustness Optimization (MRO) and Load Balancing Optimization (LBO) functions as part of Self-Optimizing Networks (SON) techniques to adjust handover control parameters, including handover margin (HOM) and time-to-trigger (TTT) [15; 16; 17]. The main objective of MRO and LBO is to automatically optimize these parameters to maintain a high-quality connection between communicating UEs, evaluated using KPIs. These optimization methodologies have improved the overall performance of the system, although adjusting the HOM and TTT values affects multiple KPIs.