Average Age of Information with Hybrid ARQ under a Resource Constraint

TL;DR

This paper investigates optimal scheduling policies for minimizing the average age of information in error-prone channels using ARQ and HARQ protocols, considering resource constraints and unknown error probabilities, with reinforcement learning solutions.

Contribution

It introduces optimal scheduling strategies for AoI with HARQ under resource constraints and develops reinforcement learning algorithms for unknown error probabilities.

Findings

Optimal policies differ for ARQ and HARQ protocols.

Reinforcement learning effectively estimates error probabilities.

Proposed algorithms improve AoI performance under constraints.

Abstract

Scheduling of the transmission of status updates over an error-prone communication channel is studied in order to minimize the long-term average age of information (AoI) at the destination, under an average resource constraint at the source node, which limits the average number of transmissions. After each transmission, the source receives an instantaneous ACK/NACK feedback, and decides on the next update, without a priori knowledge on the success of the future transmissions. The optimal scheduling policy is studied under different feedback mechanisms; in particular, standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols are considered. Average-cost reinforcement learning algorithms are proposed when the error probabilities for the HARQ system are unknown.