Abstract
In this paper, we address the problem of designing an optimal transmission policy for remote state estimation over packet-dropping wireless channels with imperfect channel state information. We consider a setup where a smart sensor performs state estimation of a linear time-invariant (LTI) dynamical system using a Kalman filter. The resulting state estimate obtained by the smart sensor at each discrete-time step, is transmitted over the wireless channel to a remote estimator. To balance the trade-off between information freshness and reliability, we employ a Hybrid Automatic Repeat reQuest (HARQ) protocol at the smart sensor which has imperfect channel state information in the form of acknowledgment feedback signal received by the remote estimator after its attempt to decode the information packets. We formulate this problem as a finite horizon Partially Observable Markov Decision Process (POMDP) with an augmented state-space that incorporates both the Age of Information (AoI) and the unknown channel state. By defining an information state that serves as a sufficient statistic for optimal decision-making by the smart sensor, we derive the dynamic programming equations for evaluating the optimal policy. This policy is computed numerically using the point-based value iteration algorithm.
Evagoras Makridis
PhD Student | Distributed Decision and Control of Networked Systems
My research interests include autonomous systems in networks, distributed optimization, and data-driven sequential decision-making (Reinforcement Learning), with applications in quadrotor navigation, and resource management.