Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other services, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system in the presence of eMBB traffic. Based on the proposed superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the rate loss of the eMBB service and URLLC packet segmentation loss while satisfying the eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed-integer non-linear program (MINLP) which is generally very hard to solve in polynomial time. Hence, we reformulate the problem as a one-to-one pairing problem and we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to support the many-to-many pairing. Simulation results show that the proposed algorithm achieves higher URLLC packet admission rate and lower rate loss for eMBB. For instance, the URLLC packet admission rate, unlike baseline methods, is shown to be preserved under the proposed method even at higher URLLC load. It is shown that at least 30% more URLLC users can be served without degrading their QoS, while keeping the impact on eMBB rate minimal. Detailed numerical evaluation is presented to quantify the benefits of the proposed method.

中文翻译：

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5G 及以后无线网络中基于叠加的 URLLC 流量调度


超可靠和低延迟通信 (URLLC) 是 5G 网络及其他网络中的基本服务之一。 URLLC 与其他服务（即增强型移动宽带 (eMBB) 和大规模机器类型通信 (mMTC)）的共存需要开发频谱高效的复用技术。在这项工作中，我们研究了存在 eMBB 流量的下行链路系统中调度 URLLC 流量的问题。基于所提出的叠加/打孔方案，制定了资源分配问题，其目标是在满足 eMBB 和 URLLC 服务质量（QoS）约束的同时最小化 eMBB 服务的速率损失和 URLLC 数据包分段损失。由此产生的问题被表述为混合整数非线性程序（MINLP），通常很难在多项式时间内解决。因此，我们将问题重新表述为一对一配对问题，并得出其可行性区域以及功率和频谱资源分配的最优解决方案。随后，我们提出了一种低复杂度算法来支持多对多配对。仿真结果表明，该算法实现了较高的URLLC数据包准入率和较低的eMBB速率损失。例如，与基线方法不同，即使在较高的 URLLC 负载下，URLLC 数据包接纳率也能在所提出的方法下得到保留。结果表明，在不降低 QoS 的情况下，可以为 URLLC 用户提供至少 30% 的服务，同时将对 eMBB 速率的影响降至最低。详细的数值评估被提出来量化所提出的方法的好处。