Age of Information for Constrained Scheduling with Imperfect Feedback

2025-12-02

AI Summary

This paper addresses the age-of-information (AoI) minimization problem in multi-source, multi-user downlink systems under imperfect channel state feedback and finite-rate feedback constraints. To fill a theoretical gap regarding AoI degradation induced by zero feedback, we first characterize its fundamental impact. We then propose an optimal scheduling structure that integrates rate-splitting multiple access with modulo-based transmission, and extend the drift-plus-penalty framework to accommodate general imperfect feedback models. Leveraging Lyapunov optimization, we design a low-complexity online algorithm, derive a closed-form lower bound on achievable AoI, and establish rigorous performance guarantees. Simulation results demonstrate that the proposed scheme significantly outperforms state-of-the-art approaches across diverse traffic patterns, including bursty and correlated arrivals.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper considers a downlink system where an access point sends the monitored status of multiple sources to multiple users. By jointly accounting for imperfect feedback and constrained transmission rate, which are key limited factors in practical systems, we aim to design scheduling algorithms to optimize the age of information (AoI) over the infinite time horizon. For zero feedback under the generate-at-will traffic, we derive a closed-form lower bound of achievable AoI, which, to the best of our knowledge, reflects the impact of zero feedback for the first time, and propose a policy that achieves this bound in many cases by jointly applying rate splitting and modular arithmetic. For zero feedback under the Bernoulli traffic, we develop a drift-plus-penalty (DPP) policy with a threshold structure based on the theory of Lyapunov optimization and provide a closed-form performance guarantee. Furthermore, we extend the design of this DPP policy to support general imperfect feedback without increasing the online computational complexity. Numerical results verify our theoretical analysis and the AoI advantage of the proposed policies over state-of-the-art policies.

Problem

• Design scheduling algorithms to optimize Age of Information under imperfect feedback.
• Derive lower bound for AoI with zero feedback and propose achieving policy.
• Extend policy to handle general imperfect feedback without computational overhead.

Innovation

• Rate splitting and modular arithmetic for zero feedback
• Drift-plus-penalty policy with threshold structure
• Extending policy to imperfect feedback without complexity increase