# A Survey of Handover Management in Mobile HetNets: Current Challenges and Future Directions
## 2 Related Surveys
They highlighted the key mechanisms, inputs, advantages, and limitations of each method, and explained how they were utilized in the decision-making process. In [28], the authors delve into the intricacies of SON operations. They trace its evolution through various mobile communication generations and present design approaches proposed for SON in B5G contexts. The authors also provide a comprehensive overview of SON applications such as MLB, CCO, MRB and Automatic Neighbor Relations (ANR). They highlight the potential of AI and ML algorithms in the successful implementation of SON in 5G and B5G scenarios and discuss recent advancements in the field of research. The authors conclude by identifying key areas of focus for future SON research in order to ensure its success in upcoming networks like B5G. In the article [29], the authors delved into the topic of load balancing in HetNets. They began by providing an overview of what load balancing is and its objectives. Next, they explored various options for managing the load balancing problem in HetNets such as data analysis-based solutions, Cell Range Expansion (CRE), fuzzy logic solutions, cell breathing, and channel borrowing. They also discussed various key performance indicators (KPIs) that can be used to evaluate performance in HetNets, such as SINR, resource utilization (Physical Resource Balancing-PRB), user satisfaction, throughput, CBR, CDR, outage ratio (OR) and PLR. The authors also highlighted the importance of coordination between MRO and MLB, as well as strategies for conflict resolution. Finally, they looked at how different researchers have used various ML methods to solve the load balancing problem in HetNets. They discussed the basic implementation details, technical challenges, performance analyses, and model inadequacies of these ML methods. In [30], the authors conducted a detailed examination of the handover process in 5G networks, highlighting key parameters such as MRO and LBO optimization and exploring different decision-making functions for initiating a handover. The study also included a thorough examination of the literature pertaining to different optimization methodologies such as velocity aware-based, RSRP-based, and FLC-based. The authors also discussed the various scenarios, methodologies, key performance indicators, and simulator types, highlighting both the successes and challenges of the handover process and potential solutions. Table 1 provides a comprehensive overview of various survey papers related to mobility and handover management. As far as our research is concerned, it is noteworthy that we only considered the most recent survey articles related to handover and mobility management. The analyzed works in the table focus on different aspects such as 4G and 5G network scenarios, handover and mobility, load balancing, machine learning applications to handover management, utilization of heuristic approaches, spatial information-based handover approaches, and conventional RSRP and UE velocity. These in-depth studies aim to provide a comprehensive understanding of the current state of the art in the field of mobility and handover management.