We study the problem of optimizing the decisions of a preemptively capable transmitter to minimize the Age of Incorrect Information (AoII) when the communication channel has a random delay. We consider a slotted-time system where a transmitter observes a Markovian source and makes decisions based on the system status. In each time slot, the transmitter decides whether to preempt or skip when the channel is busy. When the channel is idle, the transmitter decides whether to send a new update. A remote receiver estimates the state of the Markovian source based on the update it receives. We consider a generic transmission delay and assume that the transmission delay is independent and identically distributed for each update. This paper aims to optimize the transmitter's decision in each time slot to minimize the AoII with generic time penalty functions. To this end, we first use the Markov decision process to formulate the optimization problem and derive the analytical expressions of the expected AoIIs achieved by two canonical preemptive policies. Then, we prove the existence of the optimal policy and provide a feasible value iteration algorithm to approximate the optimal policy. However, the value iteration algorithm will be computationally expensive if we want considerable confidence in the approximation. Therefore, we analyze the system characteristics under two canonical delay distributions and theoretically obtain the corresponding optimal policies using the policy improvement theorem. Finally, numerical results are presented to illustrate the performance improvements brought about by the preemption capability.
翻译:我们研究在通信频道出现随机延迟时,优化先发制人决定将错误信息时代(AoII)最小化的问题,以尽可能减少错误信息时代(AoII)在通信频道出现随机延迟时优化信息时代(AoII)的决定。我们考虑一个时间档时间档系统,让发件人观察Markovian源,并根据系统状况做出决定。在每一个时间档中,发件人决定是在频道繁忙时先发制人还是跳过。当频道闲置时,发件人决定是否发送新的更新信息。一个远程接收人根据收到的更新情况估算马尔科维亚源的状况。我们考虑通用传输延迟,并假设每次更新的传输延迟是独立和同样分布的。本文旨在优化发件人在每个时间档中的决定时间档的间隔时间档,以便尽可能减少Aoolov源源源源的源头,并根据系统的一般时间间隔功能做出决定。为此,我们首先使用Markov决定程序来制定最佳化问题,然后通过两种先发制人先发制人先发制人的政策来分析预期AoII的更新。然后提出最佳政策并提供一种可行的增值算值来比较接近最佳政策。然而,如果我们想要相当地分析,那么,那么,那么,那么,那么它算算法的改进,那么,那么,那么,在两个最佳分配方法的改进的改进后算法将变得更精确的改进,那么,我们最后的改进政策,我们最后的改进后算法将会的改进。