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).

翻译：摘要：超可靠低时延通信（URLLC）是5G的主要驱动力之一，3GPP为工业自动化、车联网（V2X）、战术边缘组网及无人系统控制等应用设定了1-10毫秒的时延目标。多年后，实际5G时分双工（TDD）网络的上行链路（UL）往返时间中位数仍处于50-70毫秒区间，其主要原因在于用户设备（UE）在传输上行数据前必须完成调度请求（SR）过程。现有解决方案（主要是配置授权（CG）调度）仅能消除严格周期性业务的该开销，且需跨层同步，这限制了其部署。我们提出AUGUSTE（通过自适应时序估计实现URLLC的上行链路演进预测授权），这是一种基于学习的媒体接入控制（MAC）调度框架，通过在上行链路调度器中嵌入在线机器学习（ML）模型，在SR发起前预测数据包到达并主动分配资源。自适应状态机在收集无偏到达统计量的学习阶段与仅当流量预期时利用学习预测的置信阶段之间交替运行。我们在运行OpenAirInterface的真实5G测试平台上，针对三种URLLC业务模式（请求-响应、ML边缘推理和周期性自主上报）评估了AUGUSTE，结果表明它在时延-开销权衡曲线上达到最优可执行点：以约十分之一的资源开销（7-10%额外开销），实现与始终在线调度相当的往返时间（RTT）中位数（约10毫秒，较基于SR的20毫秒基线减半）。