Abstract

NextG applications such as AR/VR, industrial automation, cloud gaming, and autonomous robots increasingly demand lower latencies. Current 5G networks, however, incur significant delays due to request-based scheduling, where users must signal demand before the base station can allocate resources for uplink transmissions. In this paper, we present SAGE, a real-time AI system that can predict per-user uplink demand at millisecond granularity and proactively allocate resources to reduce uplink latency. SAGE proposes traffic trains: a novel abstraction that mitigates distortions to the observed traffic arrivals at the base station and yields stable prediction targets. SAGE extracts statistical features from user traffic and retrieves appropriate models from a traffic-aware database of dedicated AI predictors. SAGE further executes low-latency inference, error tracking, and online continual learning to dynamically adapt prediction models. Extensive evaluation shows that SAGE achieves millisecond-level prediction accuracy with sub-millisecond inference overhead, reducing uplink latency by 2.53× on average across diverse applications while maintaining high resource efficiency.

Code will be available soon...