Generative-aided and Context-aware Forecasting of Mobile Network Traffic

• We use deep learning to forecast mobile traffic, with particular emphasis on traffic peaks • We integrated exogenous features with traffic input for closed-loop prediction • We generated synthetic data to address scarcity and enhance model generalization • We validated performance on real-world data, outperforming baseline methods Mobile cellular networks are experiencing rapid growth in data demand, largely driven by data-intensive applications such as video streaming. In particular, the popularity of live events can induce abrupt and localized traffic surges, often resulting in congestion and performance degradation. For these reasons, accurate traffic forecasting is expected to play an increasingly important role in future sixth-generation (6G) mobile networks, supporting both real-time operational responses and long-term capacity planning. In this work, we move beyond classical traffic forecasting approaches and propose an AI-based framework that explicitly conditions traffic predictions on contextual information available in advance, while also leveraging generative data augmentation to address data scarcity. Through a comprehensive analysis conducted on two major Italian cities and several real-world datasets spanning four years, we show that traffic dynamics exhibit strong correlations with the occurrence of football matches. Building on this observation, we design a forecasting methodology that combines historical traffic measurements with scheduled event information to forecast future traffic over the prediction horizon. To improve robustness under event-driven and high-load conditions, we further introduce a lightweight synthetic data generation strategy that mitigates the scarcity and imbalance of rare traffic patterns. Experimental results demonstrate that the proposed framework improves forecasting accuracy, particularly during peak and busy-hour regimes, compared to baseline traffic-only approaches