Age-Optimal Low-Power Status Update over Time-Correlated Fading Channel

TL;DR

This paper develops optimal scheduling policies for minimizing the average age of information in a status update system over a Gilbert-Elliott fading channel, considering energy constraints and different levels of channel state information.

Contribution

It formulates the problem as a constrained POMDP and constrained MDP, deriving threshold-based optimal policies for both no sensing and delayed sensing scenarios.

Findings

Optimal policies are threshold-based in channel belief and AoI.

Proposed algorithms outperform greedy policies in simulations.

Structured policies effectively balance energy and information freshness.

Abstract

In this paper, we consider transmission scheduling in a status update system, where updates are generated periodically and transmitted over a Gilbert-Elliott fading channel. The goal is to minimize the long-run average age of information (AoI) at the destination under an average energy constraint. We consider two practical cases to obtain channel state information (CSI): (i) \emph{without channel sensing} and (ii) \emph{with delayed channel sensing}. For case (i), the channel state is revealed when an ACK/NACK is received at the transmitter following a transmission, but when no transmission occurs, the channel state is not revealed. Thus, we have to design schemes that balance tradeoffs across energy, AoI, channel exploration, and channel exploitation. The problem is formulated as a constrained partially observable Markov decision process problem (POMDP). To reduce algorithm complexity, we show that the optimal policy is a randomized mixture of no more than two stationary deterministic policies each of which is of a threshold-type in the belief on the channel. For case (ii), (delayed) CSI is available at the transmitter via channel sensing. In this case, the tradeoff is only between the AoI and energy consumption and the problem is formulated as a constrained MDP. The optimal policy is shown to have a similar structure as in case (i) but with an AoI associated threshold. Finally, the performance of the proposed structure-aware algorithms is evaluated numerically and compared with a Greedy policy.