# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 4 Results
### Semi-Automatic
It dynamically adjusts base station operation modes based on load measurements and SINR values and outperformed benchmarks in terms of power consumption and performance. It aims to make consistent decisions without prior optimization, reacting to changing load conditions. The approach considers traffic demands, user SINR values and power consumption to optimize user association and base station operation modes, and its effectiveness was demonstrated through simulations. The proposed GSPA adapts to varying traffic demands and network conditions and showed promising results in terms of reducing power consumption, minimizing service denials and enhancing user satisfaction. Delay and Energy-Aware Load Balancing in Ultra-Dense Heterogeneous 5G Networks [30]: In this paper, an enhanced approach for load balancing in a cellular network was proposed, specifically focusing on performance, energy consumption and delay awareness in ultra-dense heterogeneous 5G networks with small cells and a macrocell. Both static and dynamic load balancing strategies were introduced to achieve this optimization. The static strategy involves a nonlinear convex optimization problem, while the dynamic strategy employs the Fixed-Point Iteration (FPI) approach. Additionally, a probabilistic strategy was proposed, where small cells independently serve flows with specific probabilities. Numerical results demonstrated that the dynamic strategy consistently surpasses the static strategy in terms of load balancing. Towards Energy Efficient Load Balancing for Sustainable Green Wireless Networks Under Optimal Power Supply [31]: This study focused on enhancing load balancing and energy efficiency in green cellular networks powered by grid-tied solar PV/battery systems. The study utilized load balancing techniques, centralized resource allocation for cell zooming and cost modeling to optimize energy efficiency and reduce the carbon footprint. The results demonstrated improvements in throughput, energy efficiency and spectral efficiency in the grid-tied solar PV/battery system for LTE base stations. Load balancing algorithms further enhance energy efficiency, while cell zooming techniques improve spectral efficiency despite certain implementation challenges. Overall, the research highlighted the potential for green cellular networks to achieve sustainability and cost savings through the integration of renewable energy. Mobility Management in HetNets [32]: In this paper, innovative context-aware mobility management procedures and scheduling methods for heterogeneous networks (HetNets) in cellular networks were proposed, aiming to enhance handover performance, throughput and fairness. The algorithms included reinforcement learning techniques for context-aware mobility management in small-cell networks, a multi-armed bandit (MAB)-based approach for system performance maximization in HetNets, a satisfaction-based learning approach to reduce handover failure probability and a context-aware scheduler for optimizing throughput in HetNets through dynamically adjusting resource allocation based on factors such as UE velocity and history. The results showed significant improvements in UE throughput, with gains of up to 80% when compared to classical methods, along with reduced handover failure probability and improved fairness in resource allocation among UE in a cell. Overall, the proposed approach outperformed classical mobility management approaches in terms of throughput, fairness and handover performance. Channel Access-Aware User Association [33]: This study explored the impact of bias values and interference coordination on the spectral efficiency of different user association schemes in a heterogeneous network. It introduced a channel access-aware user association scheme for two-tier downlink cellular networks, highlighting the importance of optimal bias values and interference coordination in enhancing network performance and achieving fairness between macro and small-cell users.