基于DTMC的主动HARQ周期流分析与调度
DTMC-Based Analysis and Scheduling for Periodic Flows with Proactive HARQ
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中文总结 AI 辅助
针对URLLC中异构周期流量的可调度性分析问题，提出基于DTMC的主动HARQ周期流框架，结合两阶段遗传算法实现调度，仿真显示其可调度性优于反应式HARQ等方法且开销可接受。
中文摘要 AI 辅助
超可靠低延迟通信（URLLC）需要为异构周期流量提供严格的可靠性和延迟保障。主动HARQ通过早终止提高资源效率，但时隙级时序效应，尤其是延迟反馈，使可调度性分析复杂化。本文提出一种基于离散时间马尔可夫链（DTMC）的主动HARQ周期流框架，通过扩展状态空间，模型捕捉HARQ往返时间和其他跨时隙时序效应。该框架确定满足异构可靠性和延迟约束所需的传输机会，并通过两阶段遗传算法支持基于偏移的调度。针对工业URLLC流量的仿真表明，所提方法比反应式HARQ、K-重复和无保障主动HARQ实现更高的可调度性，且计算开销可接受。
英文摘要
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.
