Query-Age-Optimal Scheduling under Sampling and Transmission Constraints

TL;DR

This paper develops optimal joint sampling and transmission scheduling policies to minimize the average query age of information in a heterogeneous system with constraints, using linear programming and Markov decision processes.

Contribution

It introduces a novel approach to optimize query-age in complex systems with sampling and transmission constraints, including a low-complexity near-optimal policy.

Findings

Up to 32% performance improvement over benchmark policies.

Formulation of the problem as a linear program.

Development of a low-complexity near-optimal policy.

Abstract

This letter provides query-age-optimal joint sampling and transmission scheduling policies for a heterogeneous status update system, consisting of a stochastic arrival and a generate-at-will source, with an unreliable channel. Our main goal is to minimize the average query age of information (QAoI) subject to average sampling, average transmission, and per-slot transmission constraints. To this end, an optimization problem is formulated and solved by casting it into a linear program. We also provide a low-complexity near-optimal policy using the notion of weakly-coupled constrained Markov decision processes. The numerical results show up to 32% performance improvement by the proposed policies compared with a benchmark policy.