Traffic Prediction Improvement in 5G and beyond: AI and Self-Controlled Components
Résumé
The advent of 5G and Beyond 5G (B5G) networks requires novel network management strategies to mitigate potential congestion. Traditional reactive approaches are inadequate as they address issues only post-occurrence, whereas proactive Artificial Intelligence (AI) powered methods can predict and optimize resource allocation. This paper leverages AI on 5G emulated datasets to forecast network traffic, facilitating proactive resource allocation. The experimental results however indicate suboptimal model performance due to the high variability, irregular patterns, sudden traffic bursts, noise, and inconsistent data distributions in the datasets. Our analysis revealed that these issues arise from uncoordinated background traffic, system operations, and random traffic-consuming activities, leading to underperforming model outcomes. Given these challenges, we have proposed a Self-Controlled Component (SCC)-based approach to ensure that high-quality data is fed into the selected AI models, thereby improving prediction accuracy and enhancing performance.