论文标题
分散的信息匪徒
Decentralized Age-of-Information Bandits
论文作者
论文摘要
信息年龄（AOI）是用于调度系统的性能指标，可测量预期目的地可用的数据的新鲜度。 AOI正式定义为由于目的地从源头收到最新更新以来的时间以来的时间。我们考虑计划在多源多渠道设置中最大程度地减少累积AOI的问题。我们的重点是频道统计数据未知的设置，我们将问题建模为分布式的多武器匪徒问题。对于适当定义的AOI遗憾度量标准，我们为分布式多臂强盗问题的现有基于UCB的策略提供了分析性能保证。此外，我们提出了一项基于汤姆森抽样的新政策和一项混合政策，试图平衡上述政策之间的权衡。此外，我们开发了这些政策的AOI意识变体，在这些策略中，每个来源在做出决策时都考虑了当前的AOI。我们通过模拟比较各种政策的性能。
Age-of-Information (AoI) is a performance metric for scheduling systems that measures the freshness of the data available at the intended destination. AoI is formally defined as the time elapsed since the destination received the recent most update from the source. We consider the problem of scheduling to minimize the cumulative AoI in a multi-source multi-channel setting. Our focus is on the setting where channel statistics are unknown and we model the problem as a distributed multi-armed bandit problem. For an appropriately defined AoI regret metric, we provide analytical performance guarantees of an existing UCB-based policy for the distributed multi-armed bandit problem. In addition, we propose a novel policy based on Thomson Sampling and a hybrid policy that tries to balance the trade-off between the aforementioned policies. Further, we develop AoI-aware variants of these policies in which each source takes its current AoI into account while making decisions. We compare the performance of various policies via simulations.
