论文标题
在未知延迟统计下的年龄最佳抽样
Age Optimal Sampling Under Unknown Delay Statistics
论文作者
论文摘要
本文通过在采样频率约束\ cite {sun_17_tit}下通过通道进行采样和传输状态更新的问题。我们使用信息时代（AOI）来表征接收者的状态信息新鲜度。目的是设计一种采样策略，该策略可以在未知的延迟统计数据时最小化平均AOI。我们将问题重新制定为优化续签过程，并提出基于Robbins-Monro算法的在线抽样策略。我们证明所提出的算法满足采样频率约束。 Moreover, when the transmission delay is bounded and its distribution is absolutely continuous, the average AoI obtained by the proposed algorithm converges to the minimum AoI when the number of samples $K$ goes to infinity with probability 1. We show that the optimality gap decays with rate $\mathcal{O}\left(\ln K/K\right)$, and the proposed algorithm is minimax rate optimal.仿真结果验证了我们提出的算法的性能。
This paper revisits the problem of sampling and transmitting status updates through a channel with random delay under a sampling frequency constraint \cite{sun_17_tit}. We use the Age of Information (AoI) to characterize the status information freshness at the receiver. The goal is to design a sampling policy that can minimize the average AoI when the statistics of delay is unknown. We reformulate the problem as the optimization of a renewal-reward process, and propose an online sampling strategy based on the Robbins-Monro algorithm. We prove that the proposed algorithm satisfies the sampling frequency constraint. Moreover, when the transmission delay is bounded and its distribution is absolutely continuous, the average AoI obtained by the proposed algorithm converges to the minimum AoI when the number of samples $K$ goes to infinity with probability 1. We show that the optimality gap decays with rate $\mathcal{O}\left(\ln K/K\right)$, and the proposed algorithm is minimax rate optimal. Simulation results validate the performance of our proposed algorithm.
