## 2 Background
### Related Works  
LB is significantly important in 5G and 6G networks due to the robustness and reliability requirements of various applications such as streaming video, cloud computing, and virtual reality. It can help to optimize network performance and ensure that resources are used more efficiently. However, researchers often overlook the potential benefits of improving MM strategies to mitigate the impact of OL scenarios immediately during their occurrence. The understanding of how MM techniques can be adapted or improved to reduce the impact of congested cells on the performance of the network is a challenging gap. Ensuring a seamless handover, efficient load balancing, and the continuity of service delivery are the critical factors that need to be taken into consideration. Addressing this gap is essential for guaranteeing user mobility and maintaining service quality for applications that rely heavily on real-time connectivity in future networks. To balance the loads between the cells more efficiently, several studies have been carried out in the literature. Simulations by [23] attempted to demonstrate the effectiveness of basic Load Balancing (LB) algorithms. LB mechanisms automatically tune configuration parameters, which increases cell edge throughput and reduces call blocking. The implementation of this technique in mobile networks optimizes a utility function that dynamically splits data across multiple access networks, regardless of application or QoS requirements. A study by [24] proposed a traceable analytical network model that reduces Inter-Cell Interference (ICI) by using a combination of a reverse frequency assignment scheme and user correlation based on cell expansion. The researchers analyzed how this combination, with or without selective small cell base stations (sBSs), impacts coverage probability and user data rates in 5G networks. Their results showed that using reverse frequency allocation (RFA) improves coverage and user rates by mitigating ICI for unloaded macro-cell base stations (mBSs). They also found that RFA can improve performance by enhancing Channel Reuse Efficiency (CRE). Moreover, deploying selective sBSs can significantly improve coverage and user rates without requiring extra resources. However, the authors in [25] proposed a cell selection algorithm for load balancing in HetNets. This method uses two SINR thresholds to ensure user throughput. The algorithm creates two candidate lists for femtocells if the received SINR value is below the first threshold. The first is based on measured Received Signal Strength (RSS) values, while the second uses predicted RSS values. The final target femtocell is chosen based on the number of available physical resource blocks (PRBs) and pilot signal strength. If no suitable femtocell is available, users are forced to connect to the macro cell. Simulations showed that this approach improves user throughput compared to the traditional Max-SINR method but may not perform well for users with high mobility. A zone-based load balancing approach in cellular networks was investigated in [26]. The authors divided the network into zones, with a congested macrocell and its neighbors in one tier, and picocells within the zone area in another. The proposed algorithm prioritizes users closer to the macrocell for association, reducing the potential for uneven load distribution across the macrocell. Simulation results showed that the proposed algorithm is effective in achieving balanced load distribution in heterogeneous networks. Furthermore, the authors in [27] proposed a load balancing technique called \"Swap-based Load Balancing\" (SLB) specifically designed for managing access points (APs) in 5G networks. The algorithm aims to outline the issue of disproportional user distribution association across small cells, which can create congested areas and overloaded APs. The results show that SLB, particularly with biasing, effectively reduces load imbalance between APs while simultaneously improving signal quality for users.