AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling
2026-06-02 • Networking and Internet Architecture
Networking and Internet ArchitectureArtificial Intelligence
AI summaryⓘ
The authors address the problem of slow uplink communication in 5G networks caused by the need for devices to request permission before sending data, which increases delay. They propose AUGUSTE, a smart system that uses machine learning to predict when devices will send data and grants permission in advance, reducing wait times. Tested on a real 5G setup, AUGUSTE cuts the usual delay in half while using far fewer network resources compared to always-on scheduling. This approach works for various types of ultra-reliable, low-latency communication traffic.
URLLC5GTime Division Duplexing (TDD)Scheduling Request (SR)Configured Grant (CG)Medium Access Control (MAC)Machine LearningRound Trip Time (RTT)OpenAirInterface
Authors
Maxime Elkael, Michele Polese, Yunseong Lee, Koichiro Furueda, Tommaso Melodia
Abstract
Ultra Reliable and Low Latency Communications (URLLC) was one of the main motivations behind 5G, with 3GPP advertising 1-10 ms latency targets for applications such as industrial automation, Vehicle-To-Everything (V2X), tactical edge networking, and unmanned-system control. Years on, real 5G Time Division Duplexing (TDD) networks still show median Uplink (UL) round-trip times in the 50-70 ms range, largely because of the Scheduling Request (SR) procedure that a User Equipment (UE) must complete before transmitting UL data. Existing remedies, primarily Configured Grant (CG) scheduling, only eliminate this overhead for strictly periodic traffic and require cross-layer synchronization, which has limited their adoption. We propose AUGUSTE (Anticipatory Uplink Grants for URLLC via Self-Adapting Temporal Estimation), a learning-based Medium Access Control (MAC) scheduling framework that embeds online Machine Learning (ML) models in the UL scheduler to predict packet arrivals and proactively allocate resources before an SR is issued. An adaptive state machine alternates between a learning phase that collects unbiased arrival statistics and a confident phase that exploits the learned predictions to schedule only when traffic is expected. We evaluate AUGUSTE on a real 5G testbed running OpenAirInterface across three URLLC traffic patterns (request-response, ML edge inference, and periodic autonomous reporting), and show that it operates at the best achievable point on the latency-overhead trade-off: it matches always-on scheduling's median Round Trip Time (RTT) (around 10 ms, halving the 20 ms SR-based baseline) at roughly one-tenth its resource cost (7-10 percent overhead).