Minimizing Age of Incorrect Information over a Channel with Random Delay

TL;DR

This paper studies how to minimize the Age of Incorrect Information (AoII) in a system with random delays by optimizing transmission policies, including proving the optimality of simple threshold policies under certain conditions.

Contribution

It characterizes the AoII minimization problem as a Markov decision process, analyzes threshold policies, and proves the optimality of a simple threshold policy with =1 under specific conditions.

Findings

Optimal threshold policy exists for AoII minimization.

Expected AoII can be precisely computed using Markov chain analysis.

Under certain conditions, the optimal policy is a threshold policy with =1.

Abstract

We consider a transmitter-receiver pair in a slotted-time system. The transmitter observes a dynamic source and sends updates to a remote receiver through an error-free communication channel that suffers a random delay. We consider two cases. In the first case, the update is guaranteed to be delivered within a certain number of time slots. In the second case, the update is immediately discarded once the transmission time exceeds a predetermined value. The receiver estimates the state of the dynamic source using the received updates. In this paper, we adopt the Age of Incorrect Information (AoII) as the performance metric and investigate the problem of optimizing the transmitter's action in each time slot to minimize AoII. We first characterize the optimization problem using the Markov decision process and investigate the performance of the threshold policy, under which the transmitter transmits updates only when the transmission is allowed and the AoII exceeds the threshold $\tau$. By delving into the characteristics of the system evolution, we precisely compute the expected AoII achieved by the threshold policy using the Markov chain. Then, we prove that the optimal policy exists and provide a computable relative value iteration algorithm to estimate the optimal policy. Furthermore, by leveraging the policy improvement theorem, we theoretically prove that, under an easily verifiable condition, the optimal policy is the threshold policy with $\tau=1$. Finally, numerical results are presented to highlight the performance of the optimal policy.