论文标题
及时的多进程估计与擦除
Timely Multi-Process Estimation with Erasures
论文作者
论文摘要
我们考虑一个多过程远程估计系统，观察$ K $独立的Ornstein-Uhlenbeck流程。在此系统中，共享传感器以$ k $流程的方式采样了长期均值均值误差（MSE）的方式。传感器在总采样频率约束$ f _ {\ max} $下运行，并根据最大年龄（MAF）时间表对过程进行采样。来自所有过程的样本都消耗随机处理延迟，然后通过概率$ε$在擦除通道上传输。在最佳结构结果的帮助下，我们表明，在某些情况下，最佳抽样策略是\ emph {阈值策略}。我们表征了最佳阈值和相应的最佳长期平均总和MSE作为$ k $，$ f _ {\ max} $，$ε$的函数，以及观察到的过程的统计属性。
We consider a multi-process remote estimation system observing $K$ independent Ornstein-Uhlenbeck processes. In this system, a shared sensor samples the $K$ processes in such a way that the long-term average sum mean square error (MSE) is minimized. The sensor operates under a total sampling frequency constraint $f_{\max}$ and samples the processes according to a Maximum-Age-First (MAF) schedule. The samples from all processes consume random processing delays, and then are transmitted over an erasure channel with probability $ε$. Aided by optimal structural results, we show that the optimal sampling policy, under some conditions, is a \emph{threshold policy}. We characterize the optimal threshold and the corresponding optimal long-term average sum MSE as a function of $K$, $f_{\max}$, $ε$, and the statistical properties of the observed processes.
