要跳过还是要切换？链路容量约束下的最小信息年龄问题
To Skip or to Switch? Minimizing Age of Information under Link Capacity Constraint
摘要：
考虑这样一种场景：源不断地监视对象，并通过速率受限的链路向目的地发送带时间戳的状态更新。为了衡量在目的地可用的状态信息的“新鲜度”，我们采用了称为信息年龄（AoI）的度量。我们假设所有的更新都是相同大小的，并且根据伯努利过程随机到达源。由于链路容量约束，源需要$d$（$d\geq 2$）时隙来完成更新的传输。因此，当新的更新到达源并且它正在发送另一个更新时，源需要决定是跳过新的到达还是切换到它，以便最小化目的地处的预期平均AoI。我们证明，在一个广泛定义的在线政策类，最优的政策应该是一个更新的政策，并有一个顺序切换属性。然后，我们表明，最佳的决定，源应采取在任何时隙具有多阈值结构，并只取决于年龄的更新被发送和AOI在系统中。然后，通过将问题公式化为马尔可夫决策过程（MDP）来在数字上识别阈值。
英文摘要：
Consider a scenario where a source continuously monitors an object and sends time-stamped status updates to a destination through a rate-limited link. In order to measure the "freshness" of the status information available at the destination, we adopt the metric called Age of Information (AoI). We assume all updates are of the same size, and arrive randomly at the source according to a Bernoulli process. Due to the link capacity constraint, it takes $d$ ($d\geq 2$) time slots for the source to complete the transmission of an update. Therefore, when a new update when arrives at the source and it is transmitting another update, the source needs to decide whether to skip the new arrival or to switch to it, in order to minimize the expected average AoI at the destination. We prove that within a broadly defined class of online policies, the optimal policy should be a renewal policy, and has a sequential switching property. We then show that the optimal decision the source should take in any time slot has a multiple-threshold structure, and only depends on the age of the update being transmitted and the AoI in the system. The thresholds are then numerically identified by formulating the problem as a Markov Decision Process (MDP).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
