Optimizing Age-of-Information in Adversarial Environments with Channel State Information

TL;DR

This paper introduces a new greedy scheduling policy, MA-CSIT, that leverages channel state information to optimize Age-of-Information in adversarial multi-user environments, providing improved performance guarantees over existing methods.

Contribution

The paper proposes the MA-CSIT policy that uses channel state information to improve AoI minimization and establishes its superior competitive ratio bounds in adversarial settings.

Findings

MA-CSIT achieves a competitive ratio of 2 for two users, improving over previous bounds.

For three users, the competitive ratio improves from 18 to 8/3 with MA-CSIT.

Numerical simulations confirm the advantage of using CSIT in AoI optimization.

Abstract

This paper considers a multi-user downlink scheduling problem with access to the channel state information at the transmitter (CSIT) to minimize the Age-of-Information (AoI) in a non-stationary environment. The non-stationary environment is modelled using a novel adversarial framework. In this setting, we propose a greedy scheduling policy, called MA-CSIT, that takes into account the current channel state information. We establish a finite upper bound on the competitive ratio achieved by the MA-CSIT policy for a small number of users and show that the proposed policy has a better performance guarantee than a recently proposed greedy scheduler that operates without CSIT. In particular, we show that access to the additional channel state information improves the competitive ratio from 8 to 2 in the two-user case and from 18 to 8/3 in the three-user case. Finally, we carry out extensive numerical simulations to quantify the advantage of knowing CSIT in order to minimize the Age-of-Information for an arbitrary number of users.