5G-TSN 中的过载鲁棒时延：面向 3GPP 室内工厂环境的 HoL 增强混合李雅普诺夫方法
Overload-Robust Latency in 5G-TSN: A HoL-Enhanced Hybrid Lyapunov Approach for 3GPP Indoor Factory Environments
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中文总结 AI 辅助
针对 5G-TSN 工业场景，提出 HoL 增强混合李雅普诺夫调度器，在过载时优先保障 URLLC 流量，显著降低时延和丢包率，提升系统鲁棒性。
中文摘要 AI 辅助
专用 5G 网络是实现灵活工业自动化的关键推动因素，尤其是与时间敏感网络（TSN）技术结合使用时。在此背景下，无线调度器必须在固定的频谱分配上，将安全关键的工业控制流量与高带宽需求的传感数据流进行复用。本文提出了一种用于 5G-TSN 网络的队头（HoL）增强混合李雅普诺夫调度器，该调度器在漂移加惩罚的队列稳定性核心基础上，增加了显式的队头时延项和类别隔离机制。该调度器在 3GPP 室内工厂场景中进行了评估，该场景采用标准化的 3GPP 衰落、空间一致性和杂波阻塞模型，并使用自动导引车（AGV）生成并发的 URLLC、eMBB 和 mMTC 流，这些流映射到专用的 QoS 流承载上。在固定的 20 MHz 载波上，对 5 至 30 辆 AGV 的车队规模扫描揭示了一个与调度器无关的容量阈值，大约在 12 辆车左右，该阈值通过资源块饱和得到了验证。在阈值以下，所提出的调度器与最强的时延感知基线方法相比具有竞争力，其队头项相对于普通李雅普诺夫公式，将 URLLC 截止时间错失率减半。在阈值以上，它选择性地退化，而基线方法则崩溃：在 2.5 倍过载下，它比比例公平和时延预算感知基线多传输 1.8 倍的 URLLC 流量，且第 99 百分位时延短约 4 至 7 倍，从而将容量不足问题解决为有利于关键类别，而不是将其分散到整个流量组合中，代价是聚合小区吞吐量的量化损失。结果表明，基于李雅普诺夫的调度是一种有吸引力的过载鲁棒性机制，适用于必须在意外负载条件下保持可靠性的工业 5G 部署。
英文摘要
Private 5G networks are a key enabler for flexible industrial automation, especially when used in conjunction with Time-Sensitive Networking (TSN) technology. In this context, radio schedulers must multiplex safety-critical control traffic with bandwidth-hungry sensing streams over a fixed spectrum allocation. This paper proposes a Head-of-Line (HoL) Enhanced Hybrid Lyapunov scheduler for 5G-TSN networks that augments a drift-plus-penalty queue-stability core with an explicit head-of-line delay term and a class-isolation mechanism. The scheduler is evaluated in a 3GPP Indoor Factory scenario with standardized 3GPP fading, spatial consistency, and clutter blockage, using Automated Guided Vehicles (AGVs) generating concurrent URLLC, eMBB, and mMTC flows mapped to dedicated QoS-flow bearers. A fleet-size sweep of 5--30 AGVs on a fixed 20\,MHz carrier reveals a scheduler-independent capacity threshold at approximately 12 vehicles, verified by resource-block saturation. Below the threshold, the proposed scheduler is competitive with the strongest delay-aware baselines and its head-of-line term halves the URLLC deadline-miss ratio relative to the plain Lyapunov formulation. Beyond the threshold, it degrades selectively where the baselines collapse: at $2.5\times$ overload it delivers $1.8\times$ more URLLC traffic than the proportional-fair and delay-budget-aware baselines with a $\approx 4$--$7\times$ shorter 99th-percentile latency, resolving the capacity shortfall in favour of the critical classes instead of spreading it across the traffic mix, at a quantified cost in aggregate cell throughput. The results position Lyapunov-based scheduling as an attractive overload-robustness mechanism for industrial 5G deployments that must remain dependable under unexpected load conditions.
发表机构
- School of Computer Science and Information Technology, University College Cork（科克大学计算机科学与信息技术学院）
机构由 AI 辅助整理，请以论文原文为准。
