分布式量子辅助鲁棒AoII最小化在星地融合边缘网络中的应用
Distributed Quantum-Assisted Robust AoII Minimization in Satellite-Ground Integrated Edge Networks
- Middle Tennessee State University (MTSU)（中田纳西州立大学）
- Auburn University at Montgomery（阿拉巴马大学蒙哥马利分校）
- New Jersey Institute of Technology（新泽西理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对星地融合边缘网络中链路中断导致估计错误的问题，提出分布式量子-经典框架SENTINEL，联合调度更新、关联与带宽，最小化最坏情况AoII，并给出认证保证。
AI中文摘要：
6G及未来网络中的关键任务边缘应用，如自主系统、灾难响应和基础设施监控，要求边缘决策者对受监控过程的估计保持正确，而不仅仅是保持最新。星地融合网络（SAGIN）通常在基础设施受限或受灾地区提供唯一的连接，然而卫星切换和阴影效应会中断链路，在此期间过程可能多次改变状态，导致边缘节点的估计严重错误。信息年龄（AoI）仅跟踪经过的时间，无法区分无害延迟与危险错误。我们转而采用错误信息年龄（AoII），它同时惩罚估计误差的持续时间和幅度，并据我们所知，首次提出了在随机切换和阴影效应下，SAGIN上网络级、多节点AoII最小化问题。我们提出SENTINEL，一种分布式混合量子-经典框架，联合调度更新速率、卫星到基站关联和带宽分配，以最小化最坏情况下的时间平均AoII。由于AoII具有历史依赖性，它抵抗逐时隙优化；一种将源动态与信道中断分离的更新间隔分解方法，为每个传输间隔产生闭式AoII成本。由此产生的鲁棒调度问题VANGUARD被表述为QUBO，映射到Ising哈密顿量，并通过分布式QAOA结合基于ADMM的星地域协调来求解。每个返回的调度都带有对所有中断场景的认证最坏情况AoII。仿真表明，SENTINEL优于基于学习和随机的基线，在小网络中与状态感知阈值策略相匹配，同时额外提供最坏情况保证，并接近精确的极小极大参考。
英文摘要：
Mission-critical edge applications in 6G-and-beyond networks, such as autonomous systems, disaster response, and infrastructure monitoring, require that the edge decision-maker's estimate of a monitored process remain correct, not merely up to date. Satellite-ground integrated networks (SAGIN) often provide the only connectivity in infrastructure-limited or disaster-affected regions, yet satellite handover and shadowing interrupt links, during which the process may change state several times, leaving the edge node's estimate substantially wrong. Age of information (AoI) tracks only elapsed time and cannot distinguish a harmless delay from a dangerous error. We instead adopt the age of incorrect information (AoII), which penalizes both the duration and magnitude of estimation error, and formulate, to our knowledge, the first network-level, multi-node AoII minimization problem over SAGIN under stochastic handover and shadowing. We propose SENTINEL, a distributed hybrid quantum-classical framework that jointly schedules update rates, satellite-to-base-station associations, and bandwidth allocation to minimize the worst-case time-average AoII. Because AoII is history dependent, it resists per-slot optimization; a renewal-interval decomposition that separates source dynamics from channel disruption yields a closed-form AoII cost per inter-delivery interval. The resulting robust scheduling problem, VANGUARD, is formulated as a QUBO, mapped to an Ising Hamiltonian, and solved via distributed QAOA with ADMM-based coordination across satellite and ground domains. Every returned schedule carries a certified worst-case AoII over all disruption scenarios. Simulations show that SENTINEL outperforms learning-based and random baselines, matches a state-aware threshold policy in small networks while additionally providing a worst-case guarantee, and remains close to an exact minimax reference.
