从度量到机制：通过有限块长动态设计无线弹性
From Metric to Mechanism: Designing Wireless Resilience through Finite Blocklength Dynamics
AI总结：
研究下一代无线网络在干扰下的弹性，提出统一跨层框架，联合优化队列动态、速率适配和块长，开发三阶段交替优化方法，引入可解释弹性度量，数值结果验证性能，该度量可比较不同方法及干扰类型的弹性。
AI中文摘要：
下一代无线网络必须在突然和严重的干扰下保持可靠运行，特别是在超可靠低延迟通信（URLLC）场景中，严格的时间限制主导系统设计。这项工作从以时间为中心的角度解决网络弹性问题，通过明确整合有限块长（FBL）通信，将传输持续时间作为系统恢复的可控资源。为此，我们提出一个统一的跨层框架，联合耦合队列动态、速率适配和块长优化，使系统能积极吸收、适应并从各种弹性事件中恢复。为系统评估这些机制，我们引入一个可解释的弹性度量，将干扰影响分解为吸收损失、适配效率和恢复行为，能直接直观地评估系统弹性。在此框架基础上，我们开发一种三阶段交替优化方法，联合优化包括波束成形、可重构智能表面（RIS）相移和块长等物理层参数，揭示了在FBL机制中时间感知资源分配的重要性。数值结果表明在重复信道干扰和人工智能驱动的流量激增下具有强大的弹性能，突出了跨层资源适配的有效性。最后，所提出的弹性度量能直观一致地比较不同方法和干扰类型的弹性能，同时揭示它们各自的优势和局限性。
英文摘要：
Next-generation wireless networks must maintain reliable operation under abrupt and severe disruptions, particularly in ultra-reliable low-latency communication (URLLC) scenarios where strict time constraints dominate system design. This work addresses network resilience from a time-centric perspective by explicitly integrating finite blocklength (FBL) communication, thereby exposing transmission duration as a controllable resource for system recovery. To this end, we propose a unified cross-layer framework that jointly couples queue dynamics, rate adaptation, and blocklength optimization, enabling the system to actively absorb, adapt to, and recover from diverse resilience events. To systematically evaluate these mechanisms, we introduce an interpretable resilience metric that decomposes disruption impact into absorption loss, adaptation efficiency, and recovery behavior, enabling a direct and intuitive assessment of system resilience. Building on this framework, we develop a three-stage alternating optimization approach that jointly optimizes PHY-layer parameters, including beamforming, reconfigurable intelligent surface (RIS) phase shifts, and blocklength, revealing the importance of time-aware resource allocation in the FBL regime. Numerical results demonstrate strong resilience performance under repeated channel disruptions and AI-driven traffic surges, highlighting the effectiveness of cross-layer resource adaptation. Finally, the proposed resilience metric enables an intuitive and consistent comparison of resilience performance across different approaches and disruption types, while revealing their respective strengths and limitations.
