Timely Multi-Process Estimation with Erasures
Timely Multi-Process Estimation with Erasures
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DOI：
10.1109/ieeeconf56349.2022.10051950
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Karim Banawan;A. Arafa;Karim G. Seddik
中科院分区：
文献类型：
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作者：
Karim Banawan;A. Arafa;Karim G. Seddik
我们考虑一个多过程远程估计系统，观察 $K$ 独立的 Ornstein-Uhlenbeck 过程。在此系统中，共享传感器以长期平均均方误差和 (MSE) 最小化的方式对 $K$ 过程进行采样。传感器在总采样频率约束 $f_{\max}$ 下运行，并根据最大年龄优先 (MAF) 时间表对过程进行采样。所有进程的样本都会消耗随机处理延迟，然后以 $\epsilon$ 的概率通过擦除通道进行传输。在最优结构结果的帮助下，我们表明最优抽样策略在某些条件下是阈值策略。我们将最佳阈值和相应的最佳长期平均和 MSE 描述为 $K、f_{\max}、\epsilon$ 和观察过程的统计特性的函数。
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 $\epsilon$. Aided by optimal structural results, we show that the optimal sampling policy, under some conditions, is a threshold policy. We characterize the optimal threshold and the corresponding optimal long-term average sum MSE as a function of $K, f_{\max},\epsilon$, and the statistical properties of the observed processes.