We consider 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. In the system, a transmitter observes a Markovian source and makes decisions based on the system status. Time is slotted and normalized. 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. At the other end of the channel is a receiver that 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:我们考虑在通信频道出现随机延误时,如何优化先发制人的决定,以尽量减少错误信息时代(AoII),以尽可能减少错误信息时代(AoII)的延迟。在系统中,一个发件人观察Markovian源,并根据系统状态做出决定。时间档和正常化。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。在频道闲置时,发件人决定是否发送新的更新。在频道的另一端,是一个接收人,根据收到的更新信息来估计马尔科维亚源的状况。我们考虑通用传输延迟,并假定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内的决定,将时间档内的时间间隔内的时间间隔内的时间间隔内的时间间隔内的时间间隔内时间间隔内的时间间隔内,时间间隔内的时间间隔内,时间间隔内的时间间隔内,发件人决定是先先发制人,在频道闲置时,然后决定是否发送新的更新信息。然后,我们证明最佳政策的存在,并提供可行的价值,以近似最佳的政策推算方法来,在最佳政策上,在最佳政策上,我们最后要进行最优的推算。但是,我们想要进行最优的排序下,最后的推算,我们就要进行最优推算。