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 2.53X on average across diverse applications while maintaining high resource efficiency.

O-RAN (Open Radio Access Network) multivendor integration, despite its promise of a diverse 5G ecosystem, faces compatibility challenges such as vendor-specific implementations. We propose WA-RAN, a novel framework that leverages WebAssembly (Wasm) to enhance interoperability and flexibility within the O-RAN architecture. Using Wasm plugins, WA-RAN addresses the complexities of integrating multivendor equipment, enables components update on the fly, and facilitates the introduction of new features. Additionally, it offers platform and language agnosticism along with enhanced security via sandboxing. We demonstrate the design of WA-RAN through two use cases in 5G: a slice scheduler and a near-Real-Time RAN Intelligent Controller (near-RT RIC). Our implementation and evaluation show WA-RAN potential to safely overcome O-RAN integration challenges, providing a promising path to versatile 5G networks.

Ultra-Reliable Low-Latency Communication (URLLC) was introduced in 5G to meet the demanding requirements of latencies as low as 0.5 ms and reliability of 99.999 % for specific applications. Despite over a decade of discussions on URLLC, achieving these standards in real-world implementations remains challenging. We argue that it is unclear if and how URLLC can be attained and a holistic system-level perspective that addresses all the system's inherent bottlenecks is needed. Inspired by a real-world 5G testbed, we present this comprehensive vision and show how to achieve latency requirements by outlining the necessary design choices across all system layers, including the processing and radio units.

5G enables diverse services through network slicing, allowing multiple virtual networks to share physical infrastructure. However, efficiently managing resources across slices is challenging. This demo presents SliceGuard, a two-level scheduling system that leverages WebAssembly (Wasm) to allow slice owners to run customized schedulers in a secure, platform-independent environment, while the network operator manages inter-slice resource allocation. We demonstrate dynamic slicing for cloud gaming over 5G and show how Wasm enables real-time scheduler updates and fault isolation. This approach enhances flexibility, security, and customization for private 5G networks.