Enhancing Internet Traffic Forecasting in MEC Environments With 5GT-Trans: Leveraging Synthetic Data and Transformer-Based Models

Recently, transformer-based learning models have shown promising results in network traffic prediction across various contexts. However, their potential in Mobile Edge Computing (MEC) environments remains largely unexplored, despite the unique requirements of MEC such as low latency and high-bandwidth communication between end-users and edge servers. This study evaluates the performance of four transformer-based learning models for Internet traffic prediction in MEC environments, comparing them against state-of-the-art models using a real-world dataset from publicly available mobile operator networks. Performance is assessed using mean squared error (MSE) and mean absolute error (MAE) as evaluation metrics. Our experiments demonstrate that the proposed GAN-based transformer model, referred to as 5GT-GAN-Trans, achieves an MSE of 0.428 and an MAE of 0.441, considerably outperforming baseline models such as FED-Former-F (MSE: 0.791, MAE: 0.606), FED-Former-W (MSE: 0.622, MAE: 0.508), Informer (MSE: 0.814, MAE: 0.649), and a standard Transformer model (MSE: 0.803, MAE: 0.640). These numerical improvements underscore the efficacy of transformer-based models for precise Internet traffic forecasting in MEC environments, offering valuable insights for improved network management and enhanced user experience.