Remote State Estimation over a Wearing Channel: Information Freshness vs. Channel Aging
This work addresses the problem of optimal transmission and channel restoration scheduling for remote state estimation, which is relevant for wireless sensor networks and IoT systems.
The paper studies remote estimation of a linear Gaussian system over a wearing channel, formulating the trade-off between information freshness and channel aging as a semi-Markov decision process. It establishes monotonicity properties of the optimal policy and proposes structure-aware solution methods.
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.