Scheduling with Probabilistic Per-Packet Real-Time Guarantee for URLLC

TL;DR

This paper introduces a novel scheduling algorithm for URLLC that guarantees probabilistic real-time delivery of individual packets in large-scale, multi-channel networks, significantly improving network capacity and interference management.

Contribution

It proposes the local-deadline-partition (LDP) scheduling algorithm and a schedulability test for PPRC in complex multi-channel, large-scale networks with heterogeneous requirements.

Findings

LDP algorithm improves network capacity by 5-20 times.

Supports higher PPRC traffic than existing schemes.

Enhances interference management in URLLC systems.

Abstract

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.