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.

中文翻译：

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5G无线网络中URLLC和eMBB流量的联合调度

新兴的5G系统将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，时间被划分为多个时隙，这些时隙又被细分为多个小时隙。从调度的角度来看，eMBB资源分配发生在时隙边界，而为减少延迟，URLLC流量在小时隙时间尺度上被抢先地重叠，从而导致eMBB分配的选择性叠加/打孔。这种方法实现了最小的URLLC延迟，从而潜在地降低了eMBB流量。我们研究了此类系统的eMBB和URLLC联合调度程序，其双重目标是最大化eMBB流量的实用性，同时立即满足URLLC的需求。对于线性速率损失模型（eMBB的损失在URLLC叠加/穿孔数量上是线性的），我们得出一个最优的联合调度器。有点违反直觉的是，我们的结果表明，可以通过eMBB通信量的迭代梯度调度程序来实现双重目标，该调度程序可以预期URLLC通信量的预期损失，而URLLC需求调度程序可以忽略eMBB通道状态，实用程序功能和分配eMBB调度程序的决定。接下来，我们考虑一类更为通用的（凸/阈值）损耗模型，并在与信道状态相关但与微槽均匀相关的广泛策略中研究最佳的在线联合eMBB / URLLC调度程序。一个关键的观察结果是，与线性速率损失模型不同，对于凸和阈值速率损失模型，最佳eMBB和URLLC调度决策不会解耦，联合优化对于满足双重目标是必要的。