# Algorithms for Load Balancing in Next-Generation Mobile Networks: A Systematic Literature Review
## 6 Conclusions  
In recent years, there has been a significant wave of research on the design and dimensioning of mobile networks, especially with respect to load balancing techniques at the radio interface. However, there have been no comprehensive studies that address the application of artificial intelligence-based computational algorithms, whether automatic, semi-automatic or hybrid, in the load balancing of traffic and resources managed by mobile networks. This article conducted an SLR following PRISMA as an analytical methodology. Out of 1202 identified papers, 45 were selected for further analysis. As a result, we presented an analysis of the state of the art at the intersection of context awareness, resource allocation and artificial intelligence, aiming to delineate the scope of research and identify any existing gaps. By adhering to PRISMA's documentation standards, we systematically recorded exclusion criteria, resolved discrepancies through consensus and generated a flow diagram (see Figure 1) to visualize attrition rates. This approach not only strengthened the validity of findings but also enabled replicability in future studies on adaptive algorithms for socially influenced network environments. The results revealed that, despite the relative scarcity of literature on the application of artificial intelligence-based computational algorithms, many types of algorithms are being used in the load balancing context. Figure 4 shows the categorization of the studies based on their relation to the research topics, while Figure 5 displays a bar plot indicating the number of semi-automatic (19), automatic (20) and hybrid (6) algorithms utilized in the analyzed studies. In the course of carrying out this study, a series of more specific questions emerged, as follows: \begin{table}
\begin{tabular}{l l l} \hline \hline
**Factor** & **Adopting AI-Based** & **Not Adopting AI (Traditional** \\  & **Algorithms** & **Methods)** \\ \hline \multirow{3}{*}{Latency Reduction} & Significant improvement & \\  & through predictive resource & Limited by reactive tuning \\  & allocation & \\  & High initial CapEx (AI & ALow CapEx but increased \\  & infrastructure) + OpEx (data & OpEx due to manual \\  & pipelines) & optimization \\  & Handles 10,000 devices/cell via & Fails beyond 1000 devices; \\  & adaptive learning & requires hardware \\  & & overprovisioning \\ \multirow{2}{*}{Risk Profile} & Data privacy vulnerabilities; & Predictable but inflexible; \\  & model drift in non-IID data & frequent service degradation \\  & Achieves 6G KPIs (AI-native air & Meets only static 4G/5G \\ Standard & interface); enables ETSI ZSM & requirements; inhibits \\  & automation & zero-touch goals \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The advantages and disadvantages of AI adoption. What are the most suitable algorithms for radio resource re-allocation in next-generation mobile networks, based on machine learning models for load balancing? 2. What are the most suitable algorithms for radio resource re-allocation in next-generation mobile networks, based on machine learning models for localization? 3. What is the computational cost relating to the use of hybrid algorithms versus semi-automatic and/or automatic algorithms? 4. How can we classify semi-automatic, automatic and hybrid algorithms when applied to different context-aware approaches? In addition, this study highlights the strategic importance of load balancing algorithms to address 5G optimization challenges such as latency reduction, dynamic resource allocation, and scalability in dense user environments. Adaptive algorithmic frameworks, such as those that integrate network fragmentation and edge computing, are critical to enable mission-critical applications such as industrial automation and ultra-reliable communications. Our classification provides practical insights for balancing computational efficiency and quality of service in heterogeneous architectures. In closing, we highlight five key trajectories for the development of load balancing algorithms.