Remote Estimation over Packet-Dropping Wireless Channels with Partial State Information

TL;DR

This paper develops an optimal transmission policy for remote state estimation over unreliable wireless channels, balancing estimation accuracy and energy use with partial channel information.

Contribution

It introduces a novel POMDP framework incorporating age of information and channel uncertainty, solved via point-based value iteration.

Findings

Optimal policies improve estimation accuracy and energy efficiency.

The approach effectively manages packet retransmissions under channel uncertainty.

Numerical results demonstrate the policy's practical benefits.

Abstract

In this paper, we study the design of an optimal transmission policy for remote state estimation over packet-dropping wireless channels with imperfect channel state information. A smart sensor uses a Kalman filter to estimate the system state and transmits its information to a remote estimator. Our objective is to minimize the state estimation error and energy consumption by deciding whether to transmit new information or retransmit previously failed packets. To balance the trade-off between information freshness and reliability, the sensor applies a hybrid automatic repeat request protocol. We formulate this problem as a finite horizon partially observable Markov decision process with an augmented state-space that incorporates both the age of information and the unknown channel state. By defining an information state, we derive the dynamic programming equations for evaluating the optimal policy. This transmission policy is computed numerically using the point-based value iteration algorithm.