DTMC-Based Analysis and Scheduling for Periodic Flows with Proactive HARQ

2026-08-06

AI Summary

This work addresses the challenge of schedulability analysis for heterogeneous periodic traffic in ultra-reliable low-latency communication (URLLC) systems, where proactive HARQ introduces slot-level timing effects—such as feedback delay—that complicate resource allocation. To tackle this, the paper proposes a discrete-time Markov chain (DTMC)-based modeling framework that accurately captures cross-slot dynamics, including HARQ round-trip latency, through an expanded state space. Coupled with a two-stage genetic algorithm, the approach optimizes offset scheduling to meet diverse reliability and latency requirements. This study is the first to apply DTMCs to timing modeling of periodic flows under proactive HARQ, enabling precise schedulability analysis that explicitly accounts for feedback delay. Simulations demonstrate that the proposed method significantly improves schedulability compared to reactive HARQ, K-repetition schemes, and non-guaranteed proactive HARQ, while maintaining manageable computational overhead.

Abstract

Ultra-Reliable Low-Latency Communication (URLLC) requires strict reliability and latency guarantees for heterogeneous periodic traffic. Proactive HARQ improves resource efficiency through early termination, but slot-level timing effects, particularly delayed feedback, complicate schedulability analysis. This paper presents a discrete-time Markov chain (DTMC)-based framework for periodic flows with proactive HARQ. By expanding the state space, the model captures HARQ round-trip time and other cross-slot timing effects. The framework determines the transmission opportunities required to satisfy heterogeneous reliability and latency constraints and supports offset-based scheduling through a two-stage genetic algorithm. Simulations with industrial URLLC traffic show that the proposed method achieves higher schedulability than reactive HARQ, K-Repetition, and non-guaranteed proactive HARQ, with acceptable computational overhead.

Problem

• URLLC
• proactive HARQ
• schedulability analysis
• periodic flows
• timing effects

Innovation

• DTMC
• Proactive HARQ
• URLLC
• Schedulability analysis
• Offset-based scheduling