Structured Estimators: A New Perspective on Information Freshness

TL;DR

This paper introduces structured estimators that bridge the gap between simple martingale estimators and optimal MAP estimators, improving information freshness analysis in remote estimation systems.

Contribution

It proposes a new class of structured estimators that combine the advantages of martingale and MAP estimators, enhancing analytical tractability and optimality.

Findings

Structured estimators interpolate between martingale and MAP estimators.

They retain analytical tractability while improving estimation accuracy.

Applicable to pull-based update systems for better information freshness.

Abstract

In recent literature, when modeling for information freshness in remote estimation settings, estimators have been mainly restricted to the class of martingale estimators, meaning the remote estimate at any time is equal to the most recently received update. This is mainly due to its simplicity and ease of analysis. However, these martingale estimators are far from optimal in some cases, especially in pull-based update systems. For such systems, maximum aposteriori probability (MAP) estimators are optimum, but can be challenging to analyze. Here, we introduce a new class of estimators, called structured estimators, which retain useful characteristics from a MAP estimate while still being analytically tractable. Our proposed estimators move seamlessly from a martingale estimator to a MAP estimator.