# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 4 Results
### Automatic  
Context-Aware Proactive 5G Load Balancing and Optimization for Urban Areas [15]: This study discussed the application of data analytics, machine learning techniques and social media data for proactive load balancing and optimization of 5G networks in urban areas. Various frameworks and methods were proposed to detect network anomalies, forecast high-traffic areas during events and enhance network performance through the integration of context awareness and event detection. This study aimed to enhance network efficiency and capacity through the use of social network data analytics and proactive optimization strategies. Machine Learning Aided Context-Aware Self-Healing Management [39]: In this paper, a self-healing scheme designed to address cell outages in small-cell networks using machine learning algorithms was proposed. The scheme includes a Cell Outage Detection (SCOD) mechanism that efficiently detects outages, even with limited key performance indicator (KPI) information, through the utilization of support vector data description (SVDD). It also incorporates a Cell Outage Compensation (SCOC) mechanism that optimizes resource allocation to mitigate the impact of outages while considering load balancing and user QoS requirements. The framework aims to automatically manage cell outages in ultra-dense small-cell networks, improving network performance and user satisfaction. Context-Aware Mobility Management In HetNets [40]: This study evaluated different mobility management approaches in a HetNet scenario with picocell deployment. It proposed coordination-based and context-aware mobility management approaches for small-cell networks in HetNets using reinforcement learning. These methods utilize multi-armed bandit and satisfaction-based learning algorithms to optimize handover performance, UE throughput, load balancing, context-aware scheduling and fairness. Simulation results revealed significant improvements in UE throughput, handover probability, sum-rate and cell-edge UE throughput, when compared to classical mobility management methods. Mobility Management in HetNets Consider on QOS and Improve Throughput [41]: This study discussed mobility management in HetNets, focusing on QoS and throughput improvement. A context-aware mobility management approach was proposed for small cellular networks, using reinforcement learning for optimal cell range extension and UE scheduling. This method aims to minimize handover failures and ping-pong effects, improving overall system capacity and fairness conditions. The method involves optimizing load balancing and scheduling based on tissue-aware timing, including short-term scheduling based on user speed and history, as well as long-term learning-based solutions for optimal performance. The algorithm combines reinforcement learning with tissue-aware timing for load balancing and scheduling in HetNets. SON Conflict Resolution [42]: This study introduced SONCO, a solution for conflict resolution in Self-Organizing Networks (SONs) using reinforcement learning with state aggregation. SONCO optimizes parameter configurations in an SON through minimizing regret based on operator policies. The algorithm has a simplified state space and reduced complexity, compared to traditional methods, by employing a Markov Decision Process framework and state aggregation technique. Simulation results demonstrated the effectiveness of SONCO in resolving conflicts between SON functions, particularly in scenarios with conflicting update requests. Delay-Oriented Scheduling in 5G [43]: In this paper, a novel delay-oriented downlink scheduling framework based on deep reinforcement learning for 5G wireless networks was presented, which significantly outperformed existing approaches in terms of both tail delay and average delay. Crowding Game and Deep Q-Networks [44]: This study introduced dynamic resource allocation algorithms for Radio Access Network (RAN) slicing in 5G networks, focusing on Ultra-Reliable Low-Latency Communications (URLLC) and enhanced Mobile Broadband (eMBB) services.