# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 4 Results
### Semi-Automatic
Additionally, a framework for context-aware self-optimization in small-cell networks was outlined, highlighting improved convergence time and user experience. The research emphasized the benefits of incorporating user context awareness in load balancing algorithms to enhance network performance and user satisfaction. QoS Aware Predictive Radio Resource Management Approach Based on MIH Protocol [25]: In this paper, a QoS-aware predictive radio resource management approach based on the IEEE 802.21 Media Independent Handover (MIH) protocol for heterogeneous wireless networks was proposed. The approach seeks to enhance vertical handover processes and overall network efficiency through the integration of radio resource allocation in handover decisions and target network selection. The algorithm involves user classification, priority call indicators, resource allocation decisions and handover processes. It also features modules for monitoring handovers, estimating information, scheduling users and radio resource control. The approach estimates available resources in different networks, predicts handover events and manages interference to improve QoS. The scheduling process prioritizes users based on QoS requirements and channel conditions, with the goal of maximizing resource allocation while ensuring efficient bandwidth usage. Context-Aware Group Buying in Ultra-Dense Small Cell Networks [26]: This study discussed the concept of Context-Aware Group Buying in small-cell networks (SCNs) to optimize resource allocation. It explored the relationship between context-awareness and group buying, emphasizing their roles in enhancing resource efficiency. Technologies such as spectrum management, energy efficiency and caching were discussed in this context. Case studies of spectrum auction and cooperative caching using the Context-Aware Group Buying (CAGB) mechanism were presented. Additionally, challenges in theoretical game models versus practical communication systems were highlighted. Graph-Based Joint User-Centric Overlapped Clustering and Resource Allocation in Ultradense Networks [27]: In this paper, resource allocation in ultra-dense networks (UDNs) was discussed, focusing on user-centric overlapped clustering and limited orthogonal resource blocks (RBs). The proposed framework involves a novel distributed clustering solution and a graph-based resource allocation scheme to maximize spectral efficiency. The algorithm includes a two-stage process: initially, it sends requests to access points (APs) and compares throughput requests; subsequently, it yields a graph-based resource allocation solution using graph modeling and coloring. The algorithm efficiently allocates RBs to mitigate interference and maximize throughput and showed improved performance in terms of per area aggregated user rate and user rate when compared to traditional approaches. Energy Efficient Throughput Aware Traffic Load Balancing in Green Cellular Networks [28]: The main objective of this paper was to propose a dynamic point selection coordinated multipoint (DPS CoMP)-based load balancing paradigm for energy-efficient and throughput-aware traffic management in green cellular networks. The key focus was on maximizing renewable energy usage while minimizing grid energy consumption to improve network performance. Additionally, a SINR-based traffic steering algorithm was employed to optimize energy efficiency, throughput and radio efficiency. The use of actor-critic reinforcement learning approaches for resource allocation in HetNets with hybrid energy supply was also discussed. The results showed significant improvements in energy efficiency, throughput performance and spectral efficiency compared to conventional schemes. A Self-Organizing Base Station Sleeping and User Association Strategy for Dense Cellular Networks [29]: This study presented a self-organizing strategy, called the Green Shadow Price User Association algorithm (GSPA), for the optimization of dense cellular networks by balancing energy consumption and user-perceived performance.