Z-PRISM: Intelligent and Proactive Slice Resizing for Fast Adaptation in 6G Zero-Touch Networks

Managing fronthaul slices in 6G networks requires not only proactive bandwidth provisioning but also rapid adaptation to sudden and unpredictable traffic fluctuations. Although machine learning (ML) models have shown promise in forecasting regular traffic patterns, their performance often degrades under sudden demand surges, such as those caused by large-scale events. To address this challenge, we propose Z-PRISM (Zero-touch Proactive Resizer for Intelligent Slice Management), a data-driven solution that combines the Time-Series-Transformer (TST) and the AutoRegressive (AR) algorithm to adapt to abrupt changes in traffic patterns. Z-PRISM can predict short-term traffic dynamics and proactively reconfigure fronthaul slices in near real time. Simulation results demonstrate that Z-PRISM maintains more than 90% synchronization between O-RUs and BBUs during sudden traffic changes, outperforming ML-based alternatives that drop below 50%. The proposed solution aligns with the zero-touch management paradigm, offering a fast, resilient, and computationally efficient approach to adaptive slice management in dynamic 6G fronthaul environments.