Optimizing Age-of-Information in Adversarial and Stochastic Environments

TL;DR

This paper develops and analyzes online scheduling policies to optimize the freshness of information, measured by Age-of-Information, in cellular networks under adversarial and stochastic conditions, providing theoretical bounds and practical algorithms.

Contribution

It introduces competitive online policies for AoI minimization in adversarial environments and near-optimal index policies for stochastic mobility scenarios.

Findings

Greedy policy is competitive in adversarial settings.

Universal lower bounds on competitive ratio are established.

Index policy achieves near-optimal average AoI in stochastic scenarios.

Abstract

We design efficient online scheduling policies to maximize the freshness of information delivered to the users in a cellular network under both adversarial and stochastic channel and mobility assumptions. The information freshness achieved by a policy is investigated through the lens of a recently proposed metric - Age-of-Information (AoI). We show that a natural greedy scheduling policy is competitive against any optimal offline policy in minimizing the AoI in the adversarial setting. We also derive universal lower bounds to the competitive ratio achievable by any online policy in the adversarial framework. In the stochastic setting, we show that a simple index policy is near-optimal for minimizing the average AoI in two different mobility scenarios. Further, we prove that the greedy scheduling policy minimizes the peak AoI for static users in the stochastic setting. Simulation results show that the proposed policies perform well under realistic conditions.