For ultra-reliable, low-latency communications (URLLC) applications such as mission-critical industrial control and extended reality (XR), it is important to ensure the communication quality of individual packets. Prior studies have considered Probabilistic Per-packet Real-time Communications (PPRC) guarantees for single-cell, single-channel networks, but they have not considered real-world complexities such as inter-cell interference in large-scale networks with multiple communication channels and heterogeneous real-time requirements. To fill the gap, we propose a real-time scheduling algorithm based on \emph{local-deadline-partition (LDP)}, and the LDP algorithm ensures PPRC guarantee for large-scale, multi-channel networks with heterogeneous real-time constraints. We also address the associated challenge of schedulability test. In particular, we propose the concept of \emph{feasible set}, identify a closed-form sufficient condition for the schedulability of PPRC traffic, and then propose an efficient distributed algorithm for the schedulability test. We numerically study the properties of the LDP algorithm and observe that it significantly improves the network capacity of URLLC, for instance, by a factor of 5-20 as compared with a typical method. Furthermore, the PPRC traffic supportable by the LDP algorithm is significantly higher than that of state-of-the-art comparison schemes. This demonstrates the potential of fine-grained scheduling algorithms for URLLC wireless systems regarding interference scenarios.

翻译：针对超可靠低延迟通信（URLLC）应用（如关键任务工业控制和扩展现实（XR）），确保单个数据包的通信质量至关重要。已有研究考虑了单小区、单信道网络下的概率性逐数据包实时通信（PPRC）保障，但尚未解决大规模多信道异构实时需求网络中的小区间干扰等实际复杂性。为填补这一空白，我们提出一种基于局部截止时间划分（LDP）的实时调度算法，该算法可为具有异构实时约束的大规模多信道网络提供PPRC保障。同时，我们解决了相关的可调度性检验难题。具体而言，我们提出可行集的概念，推导了PPRC流量可调度性的闭式充分条件，并进一步提出一种高效的可调度性检验分布式算法。通过数值仿真研究LDP算法的性质，我们观察到该算法显著提升了URLLC网络容量——与典型方法相比可实现5-20倍的性能增益。此外，LDP算法可支持的PPRC流量显著优于现有对比方案。这证明了细粒度调度算法在干扰场景下对URLLC无线系统的巨大潜力。