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与扩展现实（XR）），确保单个数据包的通信质量至关重要。现有研究已考虑单小区单信道网络的概率逐包实时通信（PPRC）保障，但未涉及多信道多通信信道及异构实时需求的大规模网络中的复杂场景（如小区间干扰）。为填补这一空白，我们提出了一种基于局部截止时间划分（LDP）的实时调度算法。该LDP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，并解决了相关的可调度性测试难题。具体而言，我们提出了可行集的概念，推导出PPRC流量可调度性的闭式充分条件，并设计了一种高效的可调度性测试分布式算法。通过数值研究LDP算法的特性，我们发现其显著提升了URLLC网络容量——例如，与典型方法相比，提升幅度可达5-20倍。此外，LDP算法可支持的PPRC流量显著高于当前最先进的对比方案。这展示了细粒度调度算法在应对干扰场景下URLLC无线系统中的潜力。