# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 5 Discussion  
Most of the results are presented in international journals or have been presented at conferences and congresses. We can classify them into three main areas: (1) context awareness, (2) resource allocation, and (3) artificial intelligence. This is reasonable, since the study field considered in this research combines these three areas. This article presents a classification of the articles based on their content and study area. The categories are as follows: Load Balancing (LB), Mobility Prediction (MP), Mobility Load Balancing (MLB), Resource Allocation (RA), Self-Organizing Networks (SONs), Long-Term Evolution (LTE) and Long Term Evolution--Advanced (LTE-A), 5G, Machine Learning (ML), Deep Learning (DL), Context-Aware (CA), Load Management (LM), and Handover (HO). The categorization is detailed in Table 2 and in more detail and further visualized in Figure 4 using a pie chart for a clearer representation. Although there is a significant body of literature on the design of cellular networks, especially concerning load balancing in networks, the reality is that research on the application of artificial intelligence--specifically the use of both automatic and semi-automatic algorithms for the allocation of network resources--remains scarce. From the analyzed studies, the authors of this article clearly determined the type of algorithm (semi-automatic, automatic or hybrid) used in the papers. This information is summarized in Table 3 and, for better visualization of the data, we present an associated bar plot in Figure 5.  
Although it is crucial to recognize that load balancing in cellular networks involves multiple stages, identifying which type of algorithm is best suited for each stage remains essential. In addition, the evolution of load balancing algorithms reflects the shifting performance priorities and architectural demands of mobile networks during the 4G to 5G transition (2014-2023). Semi-automatic algorithms, which combine rule-based human intervention with threshold-driven automation, were instrumental in optimizing throughput and reliability for mature 4G deployments. For example, LTE-Advanced carrier aggregation strategies (2014-2018) achieved peak throughputs of 1 Gbps in urban macrocells by enabling operators to manually prioritize traffic sectors, although at the cost of limited adaptability to dynamic 5G requirements. In contrast, fully automatic algorithms, driven by AI/ML frameworks, emerged as 5G standardization accelerated (post 2018), prioritizing ultra-low latency (<10 ms for \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Relevance** & **Categories** & **Abbrev.** & **Papers** \\ \hline \multirow{4}{*}{First-order} & Load Balancing & LB & [15,20,22,23,24,28,29,30,31,38,39,48,53,58,59,61] \\  & Mobility Prediction & MP & [21,35,54] \\  & Mobility Load Balancing & MLB & [32,34,40,41,42,46] \\  & Resource Allocation & RA & [23,25,26,37,42,54] \\ \hline \multirow{4}{*}{Second-order} & Self-Organizing Networks & SON & [24,34,39,42,46] \\  & Long-Term Evolution and Long-Term & LTE and LTE-A & [45,49,58,61] \\  & Evolution—Advanced & & \\  & 5G & [15,28,35,36,39,42,47,50,62] \\  & Machine Learning & ML & [15,39,51,53] \\  & Deep Learning & DL & [45,55,56,57,53,58,52,53,55,54,55,56,57] \\  & Context-Aware & CA & [15,20,22,24,26,32,36,59,61] \\  & Load Management & LM & [28,31,33,40,59,60,63] \\  & Handover & HO & [22,25,40,41,45,46,58,59] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classification of the retrieved papers, according to their content and area of study. Figure 4: Pie chart of the categories into which the analyzed papers have been classified. URLLC) and energy efficiency. A case study [46] demonstrated that reinforcement learning-based load balancers reduced base station energy consumption by 30% in ultra-dense networks while simultaneously enhancing scalability for IoT deployments. Hybrid algorithms bridged these eras, harmonizing legacy 4G infrastructure with 5G innovations.