## 3 Proposed System Architecture
### Proposed System and Algorithm  
We proposed a system that uses FLC, as shown in Figure 4. The proposed system consists of two segments: one segment focuses on adjusting the TTT based on velocity awareness, while the other segment dynamically regulates the HOM. Afterwards, the system outputs are incorporated into the developed algorithm to improve the HO decision process, thereby enhancing the overall system performance. In the proposed system, FLC incorporates three inputs: RSRP and cell load, both with a trinary classification, and UE speed segmented into four distinct levels. This configuration creates 36 rules that govern the system. These rules partially follow the structure outlined in Table 1 and use triangular and trapezoidal functions, as described in Equations (1) and (2). The classification of inputs into different categories was carefully considered in terms of accuracy and processing time. It is crucial to note that the number of levels directly affects the processing time and complexity of the system. Figure 4: Proposed velocity-aware FLC-based system. RSRP is defined by 3 GPP in LTE/LTE-A to illustrate received signal strength (RSS). This parameter plays a crucial role in measuring the performance of a mobile wireless network during UE mobility due to the rapid fluctuation of the RSRP level. Furthermore, the system's performance is significantly influenced by RSRP, with a higher level helping to reduce the probability of system outage. The speed of the UE directly affects the adjustment of HCPs values. UEs with slower speeds tend to stay in a cell for longer periods, benefiting from better signal quality. As a result, they transition between cell boundaries more smoothly, leading to fewer handovers within a given time frame compared with UEs that move faster. Slower-moving UEs usually require higher HCP settings to account for their shorter travel distances unless they experience early HOs. On the other hand, UEs with higher speeds require lower HCPs settings to avoid late HOs. In our proposed system, we have included the cell's traffic load as an input parameter. The cell load has a significant impact on the HO process when a user moves from one cell to another. If the target cell is heavily loaded, the user may experience service failure, which can negatively affect the overall system QoS. In this study, we considered the loads of both static and moving users as factors that influence the cell load. UEs regularly monitor both their current cell and neighboring cells in the network by measuring the downlink RSRP. These measurement reports are then sent by the UEs to their serving cells. When certain predefined network configuration conditions are satisfied, the serving cell takes action on the basis of the corresponding report, which indicates the triggering event. In our proposed algorithm, we implement the A3 event condition as defined by 3 GPP [32]. The A3 event is triggered when a neighboring cell has a signal quality offset that is better than that of the serving cell. When this condition is met and persists for a specific duration known as TTT, HO is initiated. \[RSRP_{t}\geq RSRP_{s}+HOM, \tag{3}\]  
where \(RSRP_{s}\) and \(RSRP_{t}\) are the \(RSRP_{s}\) of the current serving cell and the target cell, respectively. Based on the proposed system, we developed an algorithm that optimizes the HO process by adjusting the TTT and HOM for each UE, as depicted in Figure 5. This algorithm mitigates HOPP, RLF, HOR, and HOF effects on the network, thereby improving the overall QoS. In Figure 5, a trigger timer is used to manage the HO process, preventing it from occurring within the same cell, whether in MC or SC. The calculation of the trigger timer is detailed in Algorithm 1.