Sampling of the Wiener Process for Remote Estimation over a Channel with Random Delay

TL;DR

This paper develops an optimal threshold-based sampling strategy for remote Wiener process estimation over a queue with random delay, significantly reducing estimation error compared to other sampling methods.

Contribution

It proves the optimality of a threshold policy for sampling, linking estimation error minimization to age of information, and derives the optimal threshold based on process variation and sampling constraints.

Findings

Optimal threshold policy minimizes mean square error

Threshold depends on process variation and sampling rate

Optimal policy outperforms age-optimal, zero-wait, and periodic sampling

Abstract

In this paper, we consider a problem of sampling a Wiener process, with samples forwarded to a remote estimator over a channel that is modeled as a queue. The estimator reconstructs an estimate of the real-time signal value from causally received samples. We study the optimal online sampling strategy that minimizes the mean square estimation error subject to a sampling rate constraint. We prove that the optimal sampling strategy is a threshold policy, and find the optimal threshold. This threshold is determined by how much the Wiener process varies during the random service time and the maximum allowed sampling rate. Further, if the sampling times are independent of the observed Wiener process, the above sampling problem for minimizing the estimation error is equivalent to a sampling problem for minimizing the age of information. This reveals an interesting connection between the age of information and remote estimation error. Our comparisons show that the estimation error achieved by the optimal sampling policy can be much smaller than those of age-optimal sampling, zero-wait sampling, and periodic sampling.