Sampling for Remote Estimation of an Ornstein-Uhlenbeck Process through Channel with Unknown Delay Statistics

TL;DR

This paper develops an online sampling algorithm for remote estimation of an Ornstein-Uhlenbeck process over channels with unknown delay statistics, ensuring minimal mean square error under sampling constraints.

Contribution

It introduces a novel online learning approach using stochastic approximation and virtual queues to optimize sampling thresholds without known delay statistics.

Findings

The proposed algorithm converges to the minimum MSE with probability 1.

The MSE gap grows at most logarithmically with the number of samples.

Simulation results confirm the effectiveness of the online learning method.

Abstract

In this paper, we consider sampling an Ornstein-Uhlenbeck (OU) process through a channel for remote estimation. The goal is to minimize the mean square error (MSE) at the estimator under a sampling frequency constraint when the channel delay statistics is unknown. Sampling for MSE minimization is reformulated into an optimal stopping problem. By revisiting the threshold structure of the optimal stopping policy when the delay statistics is known, we propose an online sampling algorithm to learn the optimum threshold using stochastic approximation algorithm and the virtual queue method. We prove that with probability 1, the MSE of the proposed online algorithm converges to the minimum MSE that is achieved when the channel delay statistics is known. The cumulative MSE gap of our proposed algorithm compared with the minimum MSE up to the $(k+1)$-th sample grows with rate at most $\mathcal{O}(\ln k)$. Our proposed online algorithm can satisfy the sampling frequency constraint theoretically. Finally, simulation results are provided to demonstrate the performance of the proposed algorithm.