## 2 Related Works  
To tackle the problem of HO management and optimize adjustments to HCPs, researchers have taken various approaches. There are two main options for configuring the HCPs: fixed and adaptive. However, it is common practice to combine both fixed and adaptive settings into a single configuration for HCPs, harmonizing them together. In [11],the authors proposed an HO algorithm considering signal-to-interference-plus-noise ratio (SINR) and time-of-stay (ToS) parameters to balance the load between the cells and improve the throughput in HetNets. They assigned specific values to TTT and HOM based on the mobility of the UEs. The algorithm has a maximum speed limit of 70 km/h. The authors in [12] introduced an algorithm that adaptively adjusts the HOM based on the UE's position within the cell in an LTE environment. They clarified that the HOM decreases as UE moves toward the cell edge. The algorithm considers two UE speeds: 30 km/h and 120 km/h. In [13], an algorithm was developed based on enhanced mobility state estimation to optimize TTT and HOM. The algorithm takes into account the speed of the UE and different types of HOs (MC to MC, MC to SC, SC to MC, and SC to SC) in HetNets. In the study, the authors combined the adjusted TTT values with adaptive HOM adjustment in SON. The maximum speed of the UE considered is 120 km/h. In addition, it is recommended to set lower TTT values for UEs with higher mobility speeds. This will help prevent late HOs that could lead to RLF. In [14], the authors proposed an algorithm using a weight function tailored for carrier aggregation within the LTE-A environment to adaptively estimate HOM. The algorithm dynamically fine-tunes the HOM level by employing an adaptive function that relies on the weighting functions of SINR, cell load, and UE speed. The evaluation of the system's performance involved measuring the SINR received by the UE, the throughput at the cell edges, and the probability of experiencing an outage. The maximum UE speed was set to 150 km/h. In reference [15], the authors extended the approach introduced in [14] and proposed a method to estimate the TTT and HOM parameters for each UE in a 5G network. The estimation was performed dynamically based on the UE's experience. This method takes into account cell load, SINR, and UE speed as inputs to execute bounded functions. The outputs of these bounded functions are then used as inputs to another function that estimates the TTT and HOM parameters. The performance of the proposed method was evaluated using RSRP, HOR, HOPP, and RLF. The maximum UE speed considered in the study was 140 km/h. In a similar manner, the authors of [16] presented an algorithm that estimates both TTT and HOM in an LTE/5G HetNet. This algorithm adjusts the TTT and HOM by considering factors such as RSRP, cell load, and the speed of the UE. The performance of the network was evaluated using metrics such as HOF, HOPP, and InT. The maximum speed of the UE was set to 140 km/h. Furthermore, the use of FLC has been extensively documented in the literature as a means of dynamically adjusting TTT and HOM. Some studies have used FLC to estimate either TTT or HOM, whereas others have used FLC to estimate both. In [17], a method has been developed for LTE HetNet environment that uses FLC to dynamically set the HOM. This method considers parameters such as reference signal received quality (RSRQ), UE speed, and the current HOM threshold. The maximum speed for the UE was set to 90 km/h. The study focused on system performance based on HOR, RSRQ, and HOM levels but did not consider KPIs such as RLF and HOPP, which are important for measuring system quality. The authors of [18] proposed a method that uses FLC to dynamically adjust the HOM based on three different rules in an LTE network. They considered the HOR and call dropping rate (CDR) as inputs to the FLC. TTT was applied using a fixed set of values. The method was analyzed for various traffic loads and UE speeds.