## 1 Introduction  
The swift rise in the number of mobile devices is leading to an increased demand for data traffic. According to the Ericsson Mobility Report, mobile data traffic is forecast to reach 329 exabytes (EB) per month by 2028. This represents a growth factor of 3.5% compared to the data traffic demand in 2022 [1]. The escalating demand for data traffic requires expanding and extending the capacity and coverage of legacy networks. A potential solution is the use of higher frequency bands and the deployment of a significant quantity of small cells (SCs) within the coverage area of existing cells [2]. This creates a heterogeneous network (HetNet) environment. HetNets comprise both macro cells (MCs) and SCs, which may use different radio access networks [3; 4]. The deployment of dense SCs in a HetNet pose significant mobility management challenges, particularly for users moving at higher speeds. During movement, the user equipment (UE) passes through different cell-serving areas, requiring a smooth transition of the UE connection from one cell to another. This transition process is called a handover (HO), which differentiates cellular communication from Wi-Fi and is a sophisticated process in cellular networks. The goal of this process is to provide uninterrupted and reliable connectivity to the UE to facilitate service continuity [5]. The HO process becomes more complicated and challenging in densely deployed SC HetNets compared to conventional networks. HO triggering is based on the serving cell and neighboring cell's signal quality in conventional networks, where the UE reports the signal strength of the serving cell, and the serving cell decides to initiate the HO process. For better HO decision and proper triggering of HO, based on reference signal received power (RSRP) and reference signal received quality (RSRQ) measurements, different parameters, such as time-to-trigger (TTT), handover margin (HOM), cell individual offset (CIO), and layer-3 (L3) filtering coefficient, are used. The pertinent selection of parameters for HO triggering affects the optimization of key performance indicators (KPIs). Proper triggering of HO ensures a reduction in unnecessary handover (UHO), handover failure (HOF), and radio link failure (RLF) probabilities while optimizing cell blocking probability (CBP), cell dropping probability (CDP), handover latency (HOL), and interruption time (InT). To achieve this, an efficient and resilient HO algorithm must be developed [6; 7]. Mobility robustness optimization (MRO) is a significant function of self-organizing networks (SON) introduced by the third generation partnership project (3 GPP) [8; 9]. Its purpose is to find the appropriate HO trigger for the optimal target cell to automatically optimize the HO control parameters (HCPs), such as TTT and HOM. The aim of the MRO algorithm should aim to mitigate UHO, RLF, and handover rate (HOR), which are significantly affected by the adjustment of HCPs [10]. For example, setting a long TTT at high speeds decreases UHO but increases RLF and vice versa. In addition, setting a low value for the HOM at high speeds can result in an early HO which in turn leads to RLF. Therefore, determining the optimal TTT and HOM to optimize the KPIs is still a challenge. Numerous methods have been suggested in academic research to address the issue of estimating TTT and HOM in long-term evolution (LTE) and 5G HetNets. This study proposes a system for developing an algorithm that utilizes a fuzzy logic controller (FLC), and its notable contributions can be summarized as follows:  
* Development of an intelligent FLC framework that separately handles TTT and HOM. The proposed framework utilizes critical system parameters such as RSRP, UE speed, and cell load to optimize the tuning of the TTT and HOM settings. These settings vary based on different input conditions, with the TTT being specifically set to longer for lower UE speed scenarios and shorter for higher UE speeds to ensure optimal performance.