5GT-GAN-NET: Internet Traffic Data Forecasting With Supervised Loss Based Synthetic Data Over 5G

In an era of 5G smart cities, precise traffic prediction remains elusive due to limited real-world data. Our paper introduces a novel approach using Generative Adversarial Networks (GANs) to create synthetic traffic data that closely mimics real-world statistics. This artificial dataset enhances our new 5GT-GAN-NET-based prediction model. The result is a significant boost in prediction accuracy, with Mean Square Error (MSE) reduced to 0.000346 and Mean Absolute Error (MAE) to 0.00685. Compared to benchmarks, our model improves MSE and MAE by up to 95.45% with respect to the ARIMA model and 87.31% with respect to the NARNN model respectively. User privacy remains a cornerstone of our approach, crucial for smart city applications. Our predictive capabilities enable more efficient resource allocation by service providers, increasing communication infrastructure reliability. Although tailored for smart cities, the approach is adaptable to other fields facing data scarcity and privacy concerns. Our research highlights the potential of GANs in generating large, accurate datasets for traffic prediction in 5G environments while prioritizing user privacy.