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Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks

机译:URLLC和5G无线网络中的URLLC和EMBB流量的联合调度

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Emerging 5G systems will need to efficiently support both enhanced mobile broadband traffic (eMBB) and ultra-low-latency communications (URLLC) traffic. In these systems, time is divided into slots which are further sub-divided into minislots. From a scheduling perspective, eMBB resource allocations occur at slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively overlapped at the minislot timescale, resulting in selective superposition/puncturing of eMBB allocations. This approach enables minimal URLLC latency at a potential rate loss to eMBB traffic.We study joint eMBB and URLLC schedulers for such systems, with the dual objectives of maximizing utility for eMBB traffic while immediately satisfying URLLC demands. For a linear rate loss model (loss to eMBB is linear in the amount of URLLC superposition/puncturing), we derive an optimal joint scheduler. Somewhat counter-intuitively, our results show that our dual objectives can be met by an iterative gradient scheduler for eMBB traffic that anticipates the expected loss from URLLC traffic, along with an URLLC demand scheduler that is oblivious to eMBB channel states, utility functions and allocation decisions of the eMBB scheduler. Next we consider a more general class of (convex/threshold) loss models and study optimal online joint eMBB/URLLC schedulers within the broad class of channel state dependent but minislot-homogeneous policies. A key observation is that unlike the linear rate loss model, for the convex and threshold rate loss models, optimal eMBB and URLLC scheduling decisions do not de-couple and joint optimization is necessary to satisfy the dual objectives. We validate the characteristics and benefits of our schedulers via simulation.
机译:新兴的5G系统需要有效地支持增强的移动宽带流量(EMBB)和超低延迟通信(URLLC)流量。在这些系统中,时间被分成槽,其进一步分为小型线。从调度角度来看,突发资源分配在槽边界面发生,而减少延迟URLLC流量在Minislot Timescale之前先前重叠,导致embb分配的选择性叠加/打孔。这种方法可以实现最小的URLLC延迟,以潜在的速率损失突破流量。我们研究了这种系统的联合eMB和URLLC调度程序,具有最大化embb流量的双重目标,同时立即满足URLLC需求。对于线性速率损失模型(损耗嵌入式在Urllc叠加/打孔量的线性中),我们获得了最佳的联合调度程序。我们的结果表明,我们的双重目标可以通过迭代梯度调度程序来满足,用于突破性流量,这些时间表预计URLLC流量的预期损失,以及忘记委员会频道状态,公用事业功能和分配的URLLC需求调度程序embb计划程序的决定。接下来,我们考虑更一般的(凸阈值)丢失模型,并在广泛的频道状态依赖但巨大的均匀政策中研究最佳的在线联合伯氏/ URIFS调度员。一个关键观察是,与线性速率损失模型不同,对于凸起和阈值速率损失模型,最佳embb和Urllc调度决策不会脱耦和联合优化,以满足双重目标。我们通过模拟验证了我们的调度员的特征和优势。

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