# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 4 Results
### Hybrid  
Proposed Algorithm for Transfer Decisions in LTE-A [58]: This study proposed a regression model for optimizing the handoff process in mobile networks, specifically focusing on LTE and LTE-A based HetNets, with the aim of improving load balancing and overall network performance through utilization of Regression Heuristics of Quality Metrics (RHQM) in the handover decision process. RHQM leverages regression heuristics to scale quality factors of the target cell during handover. Considering key parameters such as radio resources, signal quality metrics, and load balancing capacity, the algorithm seeks to enhance the call blocking ratio, call dropping ratio, signaling cost, handover failures and radio link failures. Experimental studies showed that the algorithm outperforms existing handover optimization models. The results highlighted significant improvements in the considered parameters, indicating the efficacy of the regression-based approach in optimizing handoff procedures in heterogeneous networks. Backhaul-Aware and Context-Aware User-Cell Association [59]: The main objective of this study was to address the user-BS association problem in 5G networks through introducing a User-Centric Backhaul (UCB) scheme that optimizes the usage of constrained backhaul networks while considering both backhaul and user context. The study developed and solved a multi-objective optimization problem for the UCB user-BS association. This problem was considered with the aim of finding the optimal association policy between users and candidate small cells to maximize throughput while adhering to capacity limits and user-defined QoS gaps. The implementation of UCB was discussed using Q-learning. The benefits of a context-aware and backhaul-aware user-cell association approach in enhancing network performance were demonstrated. By optimizing user-BS associations based on biased average received power and user priorities, the UCB scheme effectively enhances throughput while sustaining QoS requirements. Spectral Efficient and Energy Aware Clustering in Cellular Networks [60]: In this paper, clustering algorithms based on device-to-device communications were proposed to increase the capacity of cellular networks. These algorithms aim to reduce resource requirements, balance uplink and downlink traffic and address energy over-consumption using cluster heads. The algorithms--namely, eCORE, CaLB and CEEa--are designed for FDD LTE-A networks. eCORE focuses on improving spectral efficiency by creating conflict-free user clusters, while CaLB aims to balance the load between uplink and downlink traffic. CEEa limits energy over-consumption by setting thresholds for cluster head selection. The results showed that CaLB provides the highest capacity gain, while CEEa achieves better energy consumption performance. These algorithms aim to improve network capacity while considering energy efficiency and signaling overhead in D2D communications. Joint Uplink and Downlink Cell Selection in Cognitive Small-Cell Heterogeneous Networks [61]: In this paper, the optimization of energy efficiency in cognitive small cell heterogeneous networks through a joint uplink and downlink cell selection approach was discussed, and a heuristic algorithm that efficiently associates user equipment's by considering both access and backhaul energy demand was introduced. The algorithm aims to maximize network energy efficiency while maintaining user equipment QoS. It consists of two stages: initial candidate cell selection based on user equipment requirements and backhaul conditions, followed by the selection of the optimal cell based on backhaul load and available resources. The algorithm outperformed reference approaches in terms of energy efficiency, spectral efficiency and user equipment power consumption. Swarm Intelligence for Next-Generation Networks [62]: The main objective of this paper was to explore the application of Swarm Intelligence (SI) techniques in next-generation networks, especially in the context of wireless communications for 5G and 6G.