在未知信道状态时，用在线学习优化信息新鲜度与能耗的权衡。
Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics
- 基于年龄阈值策略设计在线学习算法，适应未知信道条件。
- 实现与时间长度无关的最优遗憾（O(1)），显著优于传统方法。
- 适合资源受限的实时监控系统，尤其信道状态难预测的场景。
我们研究一个实时监控系统，其中能量受限的源节点需通过一组信道调度状态更新传输，以保持目的节点的信息尽可能新鲜，信息新鲜度用年龄信息（Age of Information, AoI）衡量。在此设定下，源节点的传输成本（即能耗）与目的节点可达到的AoI性能之间存在自然权衡。现有文献在已知信道统计的前提下解决了该权衡问题。本文提出具有有限时间保证的在线学习算法，在信道统计未知的实际场景中优化此权衡。特别地，当信道统计已知时，证明最优调度策略关于AoI值具有阈值结构（即当AoI低于某阈值时应丢弃更新）。这一关键洞察被用于设计所提出的算法，意外实现了关于时间长度的阶最优遗憾（O(1)）。
原文摘要 · Abstract (English)
We consider a real-time monitoring system where a source node (with energy limitations) aims to keep the information status at a destination node as fresh as possible by scheduling status update transmissions over a set of channels. The freshness of information at the destination node is measured in terms of the Age of Information (AoI) metric. In this setting, a natural tradeoff exists between the transmission cost (or equivalently, energy consumption) of the source and the achievable AoI performance at the destination. This tradeoff has been optimized in the existing literature under the assumption of having a complete knowledge of the channel statistics. In this work, we develop online learning-based algorithms with finite-time guarantees that optimize this tradeoff in the practical scenario where the channel statistics are unknown to the scheduler. In particular, when the channel statistics are known, the optimal scheduling policy is first proven to have a threshold-based structure with respect to the value of AoI (i.e., it is optimal to drop updates when the AoI value is below some threshold). This key insight was then utilized to develop the proposed learning algorithms that surprisingly achieve an order-optimal regret (i.e., $O(1)$) with respect to the time horizon length.
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