Optimizing Age of Information without Knowing the Age of Information

2025-01-12

AI Summary

This paper addresses the challenge of minimizing long-term average Age of Information (AoI) in multi-source–destination wireless networks where base stations lack real-time AoI feedback and suffer from unreliable links. To tackle this, we first derive a fundamental lower bound on achievable AoI performance. We then propose an optimal randomized scheduling policy that operates without any AoI observations. Furthermore, we design a joint MMSE-based estimator for AoI and transmission delay, and construct the first Max-Weight freshness-aware scheduling framework leveraging estimated AoI values. We rigorously prove its asymptotic optimality. Simulations demonstrate that, even with zero AoI knowledge, our framework significantly outperforms existing state-of-the-art randomized policies—reducing average AoI by up to 32%. The methodology integrates stochastic optimization, renewal process modeling, minimum mean-square-error (MMSE) estimation, and AoI-theoretic analysis.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization
• Multiagent Systems: Other Foundations of Multi Agent Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Content-based information diffusion
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Information Freshness
• Wireless Communications
• Age of Information

Innovation

• Information Freshness
• Predictive Strategy
• Optimal Random Listening