论文标题
多源网络中及时状态更新的最佳采样和调度
Optimal Sampling and Scheduling for Timely Status Updates in Multi-source Networks
论文作者
论文摘要
我们考虑一个联合采样和调度问题，以优化多源系统中的数据新鲜度。数据新鲜度是通过\ emph {信息时代}的非罚款惩罚函数来衡量的，其中所有来源都具有相同的年龄范围函数。来源轮流生成更新数据包，并通过随机延迟的共享频道一一将其转发到目的地。有一个调度程序，它选择了源的更新顺序和一个采样器，该调度程序确定源何时应在其依次生成新数据包。我们的目标是找到最佳的调度程序对采样器对，以最大程度地减少输送时间（TA-APD）和总平均年龄 - 元素（TA-AP）的总平均年龄量。我们证明，最大年龄（MAF）调度程序和零等待采样器是共同最佳选择TA-APD的最佳选择。同时，具有降低的复杂性（RVI-RC）采样器的MAF调度程序和相对值迭代对于最小化TA-AP的共同最佳。 RVI-RC采样器基于一种相对值迭代算法，其复杂性通过利用最佳采样器中的阈值属性而降低。最后，通过对Bellman方程的近似分析来设计低复杂性阈值式采样器。该阈值型采样器将其减少到简单的填充抽样器中，以实现线性年龄函数。
We consider a joint sampling and scheduling problem for optimizing data freshness in multi-source systems. Data freshness is measured by a non-decreasing penalty function of \emph{age of information}, where all sources have the same age-penalty function. Sources take turns to generate update packets, and forward them to their destinations one-by-one through a shared channel with random delay. There is a scheduler, that chooses the update order of the sources, and a sampler, that determines when a source should generate a new packet in its turn. We aim to find the optimal scheduler-sampler pairs that minimize the total-average age-penalty at delivery times (Ta-APD) and the total-average age-penalty (Ta-AP). We prove that the Maximum Age First (MAF) scheduler and the zero-wait sampler are jointly optimal for minimizing the Ta-APD. Meanwhile, the MAF scheduler and a relative value iteration with reduced complexity (RVI-RC) sampler are jointly optimal for minimizing the Ta-AP. The RVI-RC sampler is based on a relative value iteration algorithm whose complexity is reduced by exploiting a threshold property in the optimal sampler. Finally, a low-complexity threshold-type sampler is devised via an approximate analysis of Bellman's equation. This threshold-type sampler reduces to a simple water-filling sampler for a linear age-penalty function.
