Optimizing Age of Information without Knowing the Age of Information

TL;DR

This paper addresses the challenge of maintaining fresh information in wireless networks by developing policies that optimize Age of Information without requiring prior AoI knowledge, using estimators and Max-Weight scheduling.

Contribution

It introduces a novel Max-Weight policy that estimates AoI and system times, outperforming traditional policies even without direct AoI knowledge.

Findings

Max-Weight policy with estimation outperforms optimal randomized policy

Derived lower bounds on achievable AoI

Validated policies through analytical and simulation results

Abstract

Consider a network where a wireless base station (BS) connects multiple source-destination pairs. Packets from each source are generated according to a renewal process and are enqueued in a single-packet queue that stores only the freshest packet. The BS decides, at each time slot, which sources to schedule. Selected sources transmit their packet to the BS via unreliable links. Successfully received packets are forwarded to corresponding destinations. The connection between the BS and destinations is assumed unreliable and delayed. Information freshness is captured by the Age of Information (AoI) metric. The objective of the scheduling decisions is leveraging the delayed and unreliable AoI knowledge to keep the information fresh. In this paper, we derive a lower bound on the achievable AoI by any scheduling policy. Then, we develop an optimal randomized policy for any packet generation processes. Next, we develop minimum mean square error estimators of the AoI and system times, and a Max-Weight Policy that leverages these estimators. We evaluate the AoI of the Optimal Randomized Policy and the Max-Weight Policy both analytically and through simulations. The numerical results suggest that the Max-Weight Policy with estimation outperforms the Optimal Randomized Policy even when the BS has no AoI knowledge.