# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 4 Results
### Automatic
Results indicated significant resource savings of up to 90% at the base station, improved offloading performance and near-optimal configurations, outperforming fixed allocation strategies. Characterizing Throughput and Convergence Time in Dynamic Multi-Connectivity [50]: The main objective of this paper was to optimize throughput in dynamic multi-RAT 5G systems through UE-centric learning-based algorithms. The study focused on network-assisted and collaborative learning mechanisms to enhance system performance in high-density deployments. Regarding the algorithms used, user-centric RL algorithms and collaborative learning mechanisms were employed to optimize RAT selection and throughput, while transfer learning techniques were utilized to enhance learning performance across different network scenarios. The proposed user-centric reinforcement learning approach for network selection showed promising results in terms of optimizing network performance, as demonstrated by the simulation results. A Cost-Effective Trilateration-Based Radio Localization Algorithm [51]: The primary aim of this paper was to introduce a trilateration-based outdoor positioning technique for estimating the locations of mobile terminals within cellular networks, employing machine learning and optimization methodologies. The study employed the k-nearest neighbors (k-NN) algorithm alongside optimization techniques such as Nelder-Mead, genetic algorithms and Sequential Least-Squares Programming (SLSQP), optimizing the neighbor count and implementing a K-D Tree search strategy to enhance computational efficiency while preserving accuracy. The results indicated that the proposed method, integrating k-NN with SLSQP, achieved notable accuracy and computational speed in predicting distances between users and base stations across diverse network conditions and environmental settings, surpassing benchmarks established through Nelder-Mead and genetic algorithm-based optimization, thus underscoring its efficacy in outdoor terminal positioning. Revised Reinforcement Learning Based on Anchor Graph Hashing for Autonomous Cell Activation in Cloud-RANs [52]: This study proposed a framework for autonomous cell activation and customized physical resource allocation in cloud radio access networks (C-RANs) using a revised reinforcement learning model. The main goal was to balance energy consumption and QoS in wireless networks. The algorithm effectively balances both energy consumption and user satisfaction, demonstrating improved QoS and minimized energy consumption. Efficient and Reliable Hybrid Deep Learning-Enabled Model for Congestion Control [53]: This study focused on enhancing the efficiency of 5G network slicing through the implementation of hybrid AI-based solutions. The main objective was to optimize load balancing and resource allocation and prevent network slice failure. A hybrid model that combines long short-term memory (LSTM) and support vector machine (SVM) algorithms was introduced to achieve accurate predictions and efficient resource utilization. The model effectively manages network traffic to avoid overloading and slice failure conditions and outperformed comparative algorithms in other performance metrics. Joint User Association and Resource Allocation in HetNets Based on User Mobility Prediction [54]: This study focused on tackling the dual challenge of user association and resource allocation in wireless networks, with a specific focus on HetNets featuring virtual small-cell (VSC) deployment, aiming to optimize network efficiency, fairness, system capacity and spectral efficiency through the use of novel algorithms and solutions. Key algorithms employed include the Multi-Agent Q-Learning (MAQL)-based algorithm, which optimizes user association and resource allocation by considering user load ratios, base station candidates and QoS requirements while utilizing deep Q-networks to efficiently manage large state and action spaces.