AoI-Constrained Bandit: Information Gathering over Unreliable Channels with Age Guarantees
Ziyao Huang, Weiwei Wu, Chenchen Fu, Vincent Chau, Xiang Liu, Jianping, Wang, Junzhou Luo

TL;DR
This paper addresses the challenge of scheduling information transmissions over unreliable wireless channels to meet age-of-information constraints, proposing optimal policies and a learning algorithm with bounded regret for unknown channel reliabilities.
Contribution
It introduces a novel AoI-constrained multi-armed bandit model and develops a learning algorithm that guarantees AoI requirements with bounded regret.
Findings
Optimal scheduling policy when channel reliability is known.
A learning algorithm with regret bounded by O(K√T log T).
Numerical results demonstrate the algorithm's effectiveness and robustness.
Abstract
Age-of-Information (AoI) is an application layer metric that has been widely adopted to quantify the information freshness of each information source. However, few works address the impact of probabilistic transmission failures on satisfying the harsh AoI requirement of each source, which is of critical importance in a great number of wireless-powered real-time applications. In this paper, we investigate the transmission scheduling problem of maximizing throughput over wireless channels under different time-average AoI requirements for heterogeneous information sources. When the channel reliability for each source is known as prior, the global optimal transmission scheduling policy is proposed. Moreover, when channel reliabilities are unknown, it is modeled as an AoI-constrained Multi-Armed Bandit (MAB) problem. Then a learning algorithm that meets the AoI requirement with probability 1…
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Taxonomy
TopicsAge of Information Optimization · Congenital Heart Disease Studies · Cognitive Functions and Memory
