## 2 Background
### Related Works
The study in [28] focused on how different HCP configurations can impact the network performance in 5G networks. They explored various system scenarios with considerations of different user mobility speeds. The evaluation of system performance considers various key performance indicators (KPIs) such as Reference Signal Received Power (RSRP), HO probability, HOPP, RLF, HO interruption time (HIT), and HOF. The simulations showed that there is a trade-off in the results gained from different systems, where using lower HCP settings gives a significant disadvantage by raising the HOPP rate more as compared with the higher HCP settings. At the same time, using lesser HCP settings achieved notable improvement compared to higher HCP settings regarding RLF for all mobile speed scenarios. The study concluded with the need for careful adjustment of HCP system settings considering other factors such as the mobile environment and use case. The authors in [29] proposed a load-balancing algorithm designed for 5G and future 6G networks, particularly those utilizing high-frequency millimeter waves. This algorithm builds upon the traditional method of choosing the best RSRP to establish a connection during the initial access phase. The authors simulated their algorithm in an urban microcell (UMi) scenario with a multi-beam next-generation base station. The simulation results show that this approach has promise for improving load balancing in high-frequency cellular networks. A distributed load balancing algorithm for multi-radio access technology (multi-RAT) HetNets was proposed in [30]. It aims to address the unbalanced network load distribution caused by random small-cell deployment and user mobility. The algorithm considers the QoS requirements of users and the disparate capabilities of multiple RATs, such as different propagation delays, to balance the network load. The proposed algorithm achieves a balanced load distribution among cells in a finite number of iterations. It shows the network performance in terms of load distribution, throughput, and the number of users with satisfactory QoS, as shown through simulations. The authors in [31] proposed a multi-unmanned aerial vehicle (UAV)-aided mobile-edge computing (MEC) system to address the computational limitations of IoT devices. Their approach involves deploying multiple UAVs as MEC nodes to provide offloading services. To optimize UAV placement and load balancing, a differential evolution-based algorithm was employed, and the problem was modeled as a generalized assignment problem. Furthermore, a deep reinforcement learning (DRL)-based task scheduling algorithm was introduced to enhance task execution efficiency within each UAV. The work offers insights into UAV-based MEC system design, particularly in terms of load balancing and task optimization. A simulation was used by [32] to investigate load-balancing self-optimization (LBSO) in 5G and 6G networks, focusing on the impact of different mobility speeds on optimization algorithms. Their findings underscore the significance of user location in enhancing handover control parameters, with the distance-based algorithm demonstrating performance in reducing probabilities of HOPP and RLF, and improving spectral efficiency. While their work provides valuable insights, this study extends these findings by concentrating on extremely dense UE deployment, enabling a more granular analysis of distance-based load-balancing strategies in this specific context. Existing load-balancing techniques often have limitations in addressing highly dynamic user distributions, particularly in ultra-dense network environments like those encountered in urban centers or during large-scale events. These environments experience rapid changes in user density, leading to persistent congested cells in specific areas while others remain underutilized. Traditional methods designed for static or gradual user pattern changes might not effectively adapt to such dynamic conditions.