Remote State Estimation over a Wearing Channel: Information Freshness vs. Channel Aging

TL;DR

This paper investigates optimal strategies for remote state estimation over a channel that deteriorates with use, balancing information freshness and channel wear through a semi-Markov decision process.

Contribution

It formulates the problem as an SMDP, analyzes the optimal policy structure, and proposes solution methods considering channel wear and estimation accuracy trade-offs.

Findings

Monotonicity properties of the optimal policy are established.

Structure-aware solution methods are developed.

The optimal transmission and channel restoration policies balance estimation accuracy and channel wear.

Abstract

We study the remote estimation of a linear Gaussian system over a channel that wears out over time and with every use. The sensor can either transmit a fresh measurement in the current time slot, restore the channel quality at the cost of downtime, or remain silent. Frequent transmissions yield accurate estimates but incur significant wear on the channel. Renewing the channel too often improves channel conditions but results in poor estimation quality. What is the optimal timing to transmit measurements and restore the channel? This problem is formulated as a semi-Markov decision process (SMDP). We establish monotonicity properties of the optimal policy and propose structure-aware solution methods.