基于李雅普诺夫的、面向协议数据单元（PDU）集的实时扩展现实（XR）流量的完成感知调度
Lyapunov-Based Completion-Aware Scheduling for PDU Set-Based Real-Time XR Traffic
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中文总结 AI 辅助
针对实时XR流量的PDU集调度问题，提出一种基于李雅普诺夫的完成感知MAC调度器，经仿真验证可提升资源竞争下的PDU集交付可靠性与低尾性能，适配异构场景。
中文摘要 AI 辅助
实时扩展现实（XR）服务对吞吐量、时延和可靠性有严格要求。XR媒体单元通常被分割为协议数据单元（PDU）集，仅当所有所需PDU都在其时延预算内交付时，该PDU集才有用。现有的感知PDU集的介质访问控制（MAC）调度器通常根据紧迫性或进度进行传输优先级排序，未明确考虑PDU集是否仍能按时完成。本文提出一种基于李雅普诺夫的、完成感知的MAC调度器，该调度器联合捕获每个队首PDU集的集级完成依赖关系、截止日期紧迫性和完成可行性。长期服务质量（QoS）要求通过虚拟债务队列表示，资源根据其对每个队首PDU集按时完成概率的边际贡献进行分配。这些组件被整合到漂移加惩罚调度指标中，该指标平衡长期可靠性要求与当前完成机会。系统级仿真表明，所提调度器在资源竞争下可提高PDU集交付可靠性和低尾性能，同时在异构流量需求和网络条件下提供稳健性能。
英文摘要
Real-time extended reality (XR) services impose stringent throughput, latency, and reliability requirements. An XR media unit is commonly segmented into a protocol data unit (PDU) set that is useful only when all required PDUs are delivered within its delay budget. Existing PDU set-aware medium access control (MAC) schedulers generally prioritize transmissions according to urgency or progress, without explicitly accounting for whether a PDU set can still be completed on time. In this paper, we propose a Lyapunov-based completion-aware MAC scheduler that jointly captures the set-level completion dependency, deadline urgency, and completion feasibility of each head-of-line PDU set. Long-term quality-of-service (QoS) requirements are represented through virtual debt queues, and resources are allocated according to their marginal contribution to the timely-completion probability of each head-of-line PDU set. These components are integrated into a drift-plus-penalty scheduling metric that balances long-term reliability requirements against current completion opportunities. System-level simulations demonstrate that the proposed scheduler improves PDU set delivery reliability and lower-tail performance under resource contention, while providing robust performance across heterogeneous traffic demands and network conditions.
