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arXiv 1907.06604cs.ITmath.IT
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错误信息的年龄:状态更新的新性能指标

The Age of Incorrect Information: A New Performance Metric for Status Updates

Ali Maatouk, Saad Kriouile, Mohamad Assaad, Anthony Ephremides

摘要:

在本文中,我们引入了一个新的性能指标的框架中的状态更新,我们将被称为年龄的不正确的信息(AoII)。这种新的度量方法处理了信息时代(AoI)和传统错误惩罚函数的缺点,因为它巧妙地将新鲜更新的概念扩展到新鲜的“信息”更新。在此上下文中,信息性一词指的是将新的和正确的信息带到监视器端的更新。在适当地激励新的度量,并以最小化其平均值的目的,我们制定了一个马尔可夫决策过程(MDP)的发送器-接收器对的情况下,数据包发送在一个不可靠的信道。我们表明,一个简单的“总是更新”的政策,最大限度地减少上述平均罚款以及平均年龄和预测误差。然后,我们解决一般的,更现实的情况下,发射机不能超过一个特定的功率预算。该问题被制定为一个约束马尔可夫决策过程(CMDP),我们提供了一个拉格朗日方法来解决。在描述了拉格朗日问题的最优传输策略之后,我们提供了一个严格的数学证明来展示两个拉格朗日策略的混合物对于所讨论的CMDP是最优的。配备了这一点,我们提供了一个低复杂度的算法,发现AoII的最佳操作点的系统在受约束的情况下。最后,仿真结果展示了所提出的政策的性能,并突出了与AoI框架的差异。

英文摘要:

In this paper, we introduce a new performance metric in the framework of status updates that we will refer to as the Age of Incorrect Information (AoII). This new metric deals with the shortcomings of both the Age of Information (AoI) and the conventional error penalty functions as it neatly extends the notion of fresh updates to that of fresh "informative" updates. The word informative in this context refers to updates that bring new and correct information to the monitor side. After properly motivating the new metric, and with the aim of minimizing its average, we formulate a Markov Decision Process (MDP) in a transmitter-receiver pair scenario where packets are sent over an unreliable channel. We show that a simple "always update" policy minimizes the aforementioned average penalty along with the average age and prediction error. We then tackle the general, and more realistic case, where the transmitter cannot surpass a specific power budget. The problem is formulated as a Constrained Markov Decision Process (CMDP) for which we provide a Lagrangian approach to solve. After characterizing the optimal transmission policy of the Lagrangian problem, we provide a rigorous mathematical proof to showcase that a mixture of two Lagrange policies is optimal for the CMDP in question. Equipped with this, we provide a low complexity algorithm that finds the AoII-optimal operating point of the system in the constrained scenario. Lastly, simulation results are laid out to showcase the performance of the proposed policy and highlight the differences with the AoI framework.

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。

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