用在线学习预测用户上行数据，提前分配资源，大幅降低5G低时延通信延迟。
AUGUSTE: Online-Learning dApp for Predictive URLLC Scheduling
- 通过在线机器学习预测数据到达时间，提前分配上行资源，避免等待调度请求。
- 实测中将平均往返时延降至约10毫秒，资源开销仅为传统方法的十分之一。
- 适合工业自动化、车联网等对时延敏感的实时应用，无需复杂跨层同步。
超可靠低时延通信（URLLC）是5G的重要目标，3GPP设定工业自动化、车联网（V2X）、战术边缘网络和无人系统控制等场景的时延目标为1-10毫秒。然而，实际5G时分双工（TDD）网络的上行（UL）往返时延中位数仍处于50-70毫秒范围，主要源于用户设备（UE）在发送上行数据前必须完成调度请求（SR）流程。现有方案如配置授权（CG）仅适用于严格周期性流量，且需跨层同步，限制了其应用。本文提出AUGUSTE（基于自适应时间估计的预测性上行资源分配），一个嵌入在线机器学习模型的媒体接入控制（MAC）调度框架，可预测包到达并提前分配资源，无需等待SR。其自适应状态机在学习阶段收集无偏到达统计，在自信阶段利用预测结果调度，仅在预计有流量时才分配资源。我们在基于OpenAirInterface的真实5G测试平台上，针对三种URLLC流量模式（请求-响应、边缘机器学习推理、周期性自主上报）评估AUGUSTE，结果表明其达到时延-开销权衡的最优点：平均往返时延（RTT）约10毫秒（相比20毫秒的基于SR基线减半），资源开销仅7-10%，约为全时隙占用的十分之一。
原文摘要 · Abstract (English)
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).
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