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首页> 外文期刊>IEEE Transactions on Communications >Statistical Guarantee Optimization for AoI in Single-Hop and Two-Hop FCFS Systems With Periodic Arrivals
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Statistical Guarantee Optimization for AoI in Single-Hop and Two-Hop FCFS Systems With Periodic Arrivals

机译：使用周期性抵达的单跳和两跳FCFS系统的AOI统计保障优化

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摘要

Age of Information (AoI) has proven to be a useful metric in networked systems where timely information updates are of importance. In the literature, minimizing "average age" has received considerable attention. However, various applications pose stricter age requirements on the updates which demand knowledge of the AoI distribution. Furthermore, the analysis of AoI distribution in a multi-hop setting, which is important for the study of Wireless Networked Control Systems (WNCS), has not been addressed before. Toward this end, we study the distribution of AoI in a WNCS with two hops and devise a problem of minimizing the tail of the AoI distribution with respect to the frequency of generating information updates, i.e., the sampling rate of monitoring a process, under first-come-first-serve (FCFS) queuing discipline. We argue that computing an exact expression for the AoI distribution may not always be feasible; therefore, we opt for computing upper bounds on the tail of the AoI distribution. Using these upper bounds, we formulate Upper Bound Minimization Problems (UBMP), namely, Chernoff-UBMP and alpha-relaxed Upper Bound Minimization Problem (alpha-UBMP), where alpha > 1 is an approximation factor, and solve them to obtain "good" heuristic rate solutions for minimizing the tail. We demonstrate the efficacy of our approach by solving the proposed UBMPs for three service distributions: geometric, exponential, and Erlang. Simulation results show that the rate solutions obtained are near optimal for minimizing the tail of the AoI distribution for the considered distributions.
机译：信息年龄（AOI）已被证明是网络系统中的有用度量标准，其中及时信息更新具有重要性。在文献中，最大限度地减少“平均年龄”得到了相当大的关注。但是，各种应用程序对更新的更严格的年龄要求，要求对AOI分布的了解。此外，在多跳设置中的AOI分布分析，这对无线网络控制系统（WNCS）的研究很重要，尚未解决。朝向此结束，我们研究了两个跳跃的WNC中AOI的分布，并设计了在第一个发布信息更新的频率下最小化AOI分布尾部的问题，即监视过程的采样率 - 首先服务（FCFS）排队纪律。我们认为计算AOI分布的确切表达可能并不总是可行的;因此，我们选择在AOI分布的尾部计算上限。使用这些上限，我们制定了上限最小化问题（UBMP），即Chernoff-UBMP和α-轻松的上限最小化问题（alpha-UBMP），其中alpha> 1是近似因子，并解决它们获得“良好” “启发式速率解决方案，即最小化尾部。我们通过解决三个服务分布所提出的UBMP来证明我们方法的功效：几何，指数和erlang。仿真结果表明，所获得的速率溶液在最大限度地降低所考虑的分布的AOI分布的尾部附近。

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