## 3 Proposed System Architecture
### Proposed System and Algorithm
### Handover Ping-Pong (HOPP)

HOPP refers to a wireless network scenario where UEs experience frequent and repetitive HOs between two BSs. Instantaneous variations in signal level due to shadowing, especially in UEs located at the cell edge, cause HOPP. HOPP is one of the vital performance metrics that shows the UHO. HOPP may occur due to inappropriate settings of HCPs in automatic configuration and low value setting to HCPs in manual configuration. In both the automatic and manual configurations, the minimum value settings to HCPs cause early HO and, in turn, lead to an increase in HOPP probability. HOPP significantly degrades the QoS of the network, wastes network resources, and results in excessive power consumption. Figure 12a displays the HOPP probabilities as the average of all UEs with varying UE speed scenarios. It illustrates that HOPP probabilities vary across different methods and speed limits. For instance, A3 exhibits unpredictable HOPP probabilities for different UE speeds. On the other hand, FLD [25] shows an increasing HOPP probability from 5 to 80 km/h, but it decreases for speeds above 80 km/h. In contrast, AWF [15] has higher HOPP probabilities for lower UE speeds, but these decrease as UE speeds increase. In comparison, the proposed algorithm has lower HOPP probabilities for low UE speeds and higher HOPP probabilities for higher UE speeds. The occurrence of the HOPP probability is related to how HCPs limits are applied in algorithms. Some algorithms have more reactions with UE speeds, whereas others do not. Figure 12: Average HOPP probability for the methods under consideration across different scenarios: (**a**) for all UEs versus UE speed scenarios, (**b**) for the overall system, and (**c**) for all UEs versus simulation time. Figure 12b presents the HOPP probabilities for all the considered methods as the average of all UEs across the various UE speed scenarios. Similarly, Figure 12c shows the HOPP probability for all UEs versus time. In these illustrations, it is evident that the proposed algorithm offers a substantial reduction in the HOPP probability compared to the other methods considered. The proposed algorithm achieved reductions of 91.72%, 88.09%, and 91.85% in comparison with the A3, FLD [25], and AWF [15] methods, respectively. This reduction is attributed to the optimization of HCPs and the effectiveness of the algorithm presented in this work. ## 5 Conclusions

In this study, we proposed a system that uses FLC to estimate HCPs, such as TTT and HOM, and based on the proposed system, we developed an algorithm which optimizes the KPIs by making efficient HO decisions in a 5G ultra-densely deployed SCs HetNet environment. The FLC utilized in the system leverages prior expertise and considers factors such as RSRP, UE speed, and cell load as input metrics. It adapts to different UE speeds and adjusts the TTT and HOM in a separate manner inside the FLC framework. The rules for both parameters differ significantly, while TTT is directly ruled by the UE speeds, a concept is referred as'velocity-aware', HOM operates under distinct rules. Importantly, our proposed algorithm customizes the HCPs for each UE independently, eliminating the blanket impact on all UEs. We validated the HCP settings across a range of speed scenarios from 5 km/h to 220 km/h and assessed the algorithm's performance using various KPIs such as HOR, HOF, RLF, and HOPP. The results undeniably demonstrate that our proposed algorithm surpasses the performance of compared methods in the literature, ensuring a balance between RLF and UHO by minimizing the average RLF probability to 0.0069 and the average HOPP probability to 0.057 in an ultra-densely deployed HetNet environment, which serves as compelling evidence of its efficacy and superiority. This clear advantage positions our algorithm as a prime candidate for implementation in 5G networks, offering tangible benefits in terms of performance optimization and operational efficiency.