Optimal Information Updating based on Value of Information
Optimal Information Updating based on Value of Information
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DOI：
10.1109/allerton.2019.8919810
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Rahul Singh;G. Kamath;P. Kumar
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文献类型：
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作者：
Rahul Singh;G. Kamath;P. Kumar
我们解决的问题，如何最佳地调度数据包在一个不可靠的信道，以尽量减少估计误差的一个简单的实现远程线性估计器使用恒定的“卡尔曼”增益跟踪状态的高斯马尔可夫过程。远程估计器接收包含过程的噪声观测的时间戳数据包。此外，它们还包含关于传感器/源的“质量”的信息，即，用于生成数据包的观测噪声的方差。为了使估计误差最小化，调度器需要在对分组传输进行优先级排序的同时使用两者。它示出了一个简单的索引规则，计算每个数据包的信息（VoI）的值，然后调度具有最大当前值的VoI的数据包，是最佳的。数据包的VoI随其年龄而减小，并随源的精度而增加。因此，我们的结论是，对于恒定的滤波器增益，最小化信息年龄的政策不一定最大化估计器的性能。
We address the problem of how to optimally schedule data packets over an unreliable channel in order to minimize the estimation error of a simple-to-implement remote linear estimator using a constant “Kalman” gain to track the state of a Gauss Markov process. The remote estimator receives time-stamped data packets which contain noisy observations of the process. Additionally, they also contain the information about the “quality” of the sensor/source, i.e., the variance of the observation noise that was used to generate the packet. In order to minimize the estimation error, the scheduler needs to use both while prioritizing packet transmissions. It is shown that a simple index rule that calculates the value of information (VoI) of each packet, and then schedules the packet with the largest current value of VoI, is optimal. The VoI of a packet decreases with its age, and increases with the precision of the source. Thus, we conclude that, for constant filter gains, a policy which minimizes the age of information does not necessarily maximize the estimator performance.