PROPHET: PRediction Of 5G bandwidtH using Event-driven causal Transformer

Accurately predicting 5G bandwidth in vehicular mobility scenarios is essential for optimizing real-time communication (RTC) systems in a range of emerging vehicular applications, such as autonomous driving, in-car video conferencing, vehicular augmented reality (AR), and remote driving in dynamic urban environments. In this paper, we propose Prophet, a causality-aware Transformer model designed to forecast 5G throughput by capturing the complex causal relationships between control-plane 5G network events (e.g., handovers) and environmental factors (e.g., signal strength). Our key contributions include: (1) a wavelet-based encoder that efficiently captures long-term trends through multi-scale signal decomposition; (2) a control-plane causality attention mechanism in the decoder that focuses on localized, short-term throughput variations triggered by network events; and (3) a Granger causality attention mechanism that enhances prediction accuracy by emphasizing historically significant data patterns. Prophet surpasses State-Of-The-Art (SOTA) models like random forest, Long Short-Term Memory (LSTM), and other SOTA transformers in both accuracy and scalability.