# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 4 Results
### Automatic
It compared a distributed crowding game-based algorithm with a centralized Deep Q-Network (DQN) algorithm for the optimization of resource allocation while meeting QoS requirements. The algorithms aim to improve resource utilization efficiency without compromising user performance. Performance evaluations showed that both approaches achieved similar results, with the distributed approach converging faster and the centralized approach being more adaptable. The algorithms outperformed both legacy and state-of-the-art methods, showcasing improvements in resource utilization and user performance. Congestion Minimization of LTE Networks [45]: In this paper, the use of deep learning combined with optimization algorithms to minimize congestion in LTE and LTE-A networks was discussed. Two algorithms--Block Coordinated Descent Simulated Annealing (BCDSA) and the genetic algorithm (GA)--were proposed to address the optimization problem through the adjustment of cell power and handover thresholds. The results showed that BCDSA is faster but has lower success rates compared to the GA, with real-time comparisons demonstrating reduced overhead and increased throughput without significant changes in signal strength. The study highlighted the advantages of the DL-BCDSA (Downlink-Block Coordinated Descent Simulated Annealing) algorithm in the network optimization context. Mobility Load Management in Cellular Networks [46]: This study proposed a deep reinforcement learning (RL) framework for load management in cellular networks, focusing on the direction of Individual Cell Compensation (IOC) to improve downlink throughput and reduce the number of blocked users. The authors summarized their contributions in terms of reformulating the load balancing problem as a Markov Decision Process (MDP), defining a new state based on key performance indicators (KPIs), formulating a flexible optimization framework with different reward definitions, proposing RL agents for discrete and continuous action spaces and building a realistic system-level simulator for LTE cellular networks. The proposed framework seeks to address the challenges of balancing traffic in cellular networks and optimizing network performance. Deep Learning in Heterogeneous Wireless Networks [47]: In this paper, the application of deep learning for radio resource allocation in 5G networks was discussed, specifically focusing on user associations in heterogeneous wireless networks. The authors proposed a deep learning-based user association model that employs a modified U-Net architecture to determine the association matrix between users and base stations. The model was trained using labeled data generated using a heuristic algorithm, with the training phase including pre-training and constraints on the number of base stations to which a user can be associated. The objective was to maximize the minimum achievable rate of users at the cell edge. Efficient Resource Management in 6G [48]: In this paper, the development of a future hybrid quantum deep learning model for 6G communication systems was discussed, aiming to enhance network slicing, load balancing and system availability. Through combining Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures, the proposed model improves QoS performance, accurately allocates network slices, manages load balance in congested scenarios and provides backup solutions for slice failures. A D2D Learning-Based Approach to LTE Traffic Offloading [49]: This study focused on optimizing data dissemination in LTE networks through a joint multicast/D2D approach using reinforcement learning algorithms. The main objective was to minimize the number of resource blocks required for content delivery while ensuring reliable and efficient distribution. The algorithm, which is based on a multi-armed bandit framework, dynamically selects the optimal user allocation between multicast and D2D based on channel quality and mobility patterns.