Timely Multi-Process Estimation with Erasures

Autor:	Banawan, Karim, Arafa, Ahmed, Seddik, Karim G.
Rok vydání:	2022
Předmět:	Computer Science - Information Theory Computer Science - Networking and Internet Architecture Electrical Engineering and Systems Science - Signal Processing Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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 \emph{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. Comment: Accepted for publication in the Asilomar Conference on Signals, Systems, and Computers, October 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.11213 Zobrazit plný text záznamu View this record from Arxiv