# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 4 Results
### Semi-Automatic  
Social-Aware Load Balancing System for Crowds in Cellular Networks [20]: In this paper, the implementation of a social-aware load balancing system for cellular networks using fuzzy logic controllers in the context of crowded events is discussed. Through integrating social event data with traditional network and user data, the system optimizes load sharing between cells, resulting in improved load balancing without compromising signal quality. Simulation results demonstrated the effectiveness of the system in maintaining acceptable Signal-to-Interference-plus-Noise Ratio (SINR) levels in a 5G network scenario. This study highlighted the significance of considering user behaviors and network traffic correlation in addressing load imbalance challenges during mass events. Mobility Context Awareness to Improve Quality of Experience in Traffic Dense Cellular Networks [21]: In this paper, a system model designed to predict traffic severity at specific locations based on vehicular user activity was presented. It included algorithms for detecting vehicular clusters, predicting traffic jams and implementing proactive load balancing and small cell activation. The proposed strategies leverage mobility context-awareness to improve the quality of experience in traffic-dense cellular networks, addressing challenges related to high mobility and data demand. The simulation results demonstrated improvements in key performance indicators, energy consumption and significant reductions in user dropping and blocking, enhancing the overall user experience in high-load scenarios. Context-Aware Handover Policies in HetNets [22]: In this paper, a Context-Aware Handover Policy (CAHP) to optimize handover decisions in Heterogeneous Networks (HetNets) was proposed and validated. The CAHP considers various context parameters, including the transmit power, path loss coefficients, UE speed and cell traffic load to improve user capacity and performance. The authors employed a theoretical model based on a Markov Chain to develop the optimal Time-to-Trigger (TTT) value and maximize the average performance of the user equipment (UE) in HetNet scenarios. This model considers  
Figure 3: Word cloudof the categories into which the analyzed papers were classified. transitions among neighboring cells and analyzes the impact of context parameters on handover decisions. Simulation results showed that the proposed CAHP outperforms standard handover processes with fixed TTT values in various scenarios. The CAHP enhances the average capacity of mobile users crossing different cells within the HetNet, demonstrating the effectiveness of context-awareness in enhancing handover performance. Traffic-Aware Carrier Allocation [23]: This study introduced a two-step resource allocation algorithm designed to enhance service differentiation in heterogeneous traffic scenarios within wireless networks. The main goal of the algorithm is to prioritize inelastic traffic for licensed bands while directing elastic traffic to unlicensed bands, particularly under high traffic loads. The algorithm involves resource grouping and allocation, with primary carriers assigned using the Hungarian algorithm and secondary carriers allocated based on traffic loads and types. Simulation results demonstrate improved QoS for inelastic traffic, increased connection robustness, and enhanced system utility and QoS satisfaction, even under high traffic conditions. However, it was noted that the proposed approach could lead to resource wastage during periods of low traffic. Based on the Use Case of Load Balancing in Small-Cell Networks [24]: This study discussed the integration of context-aware algorithms in load balancing for cellular networks. It introduced the power traffic sharing (PTS) algorithm and a modified version, power load sharing (PLS). These algorithms leverage context information to enhance network performance and reduce user dissatisfaction. The study demonstrated the advantages of context-aware LB algorithms in optimizing network performance in dynamic scenarios.