arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.05292cs.ITcs.NIeess.SPmath.IT
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远程短块长度过程监控:分辨率和数据新鲜度之间的权衡

Remote Short Blocklength Process Monitoring: Trade-off Between Resolution and Data Freshness

Stefan Roth, Ahmed Arafa, H. Vincent Poor, Aydin Sezgin

摘要:

在网络物理系统中,如在5G及以后,多个物理过程需要在远程设备上进行及时的在线监控。在那里,接收到的信息用于估计当前和未来的过程值。当通过通信信道传输过程数据时,使用源-信道编码以减少数据错误。在传输过程中,高数据分辨率有助于精确捕获过程变量的值。然而,这通常伴随着长的传输延迟,降低了数据的利用率,因为估计质量随着时间的推移而降低。在本文中,最近的数据和精确的测量之间的权衡捕获的高斯马尔可夫过程。信息量(AoI)度量用于评估数据的及时性,而均方误差(MSE)用于评估预测过程值的精度。AoI固有地出现在MSE表达式中,但它可以相对更容易优化。我们的目标是最小化这两个指标的时间平均版本。我们遵循短块长度源信道编码方法,并优化所使用的代码的参数,以便描述MSE和AoI之间的可扩展性区域。

英文摘要:

In cyber-physical systems, as in 5G and beyond, multiple physical processes require timely online monitoring at a remote device. There, the received information is used to estimate current and future process values. When transmitting the process data over a communication channel, source-channel coding is used in order to reduce data errors. During transmission, a high data resolution is helpful to capture the value of the process variables precisely. However, this typically comes with long transmission delays reducing the utilizability of the data, since the estimation quality gets reduced over time. In this paper, the trade-off between having recent data and precise measurements is captured for a Gauss-Markov process. An Age-of-Information (AoI) metric is used to assess data timeliness, while mean square error (MSE) is used to assess the precision of the predicted process values. AoI appears inherently within the MSE expressions, yet it can be relatively easier to optimize. Our goal is to minimize a time-averaged version of both metrics. We follow a short blocklength source-channel coding approach, and optimize the parameters of the codes being used in order to describe an achievability region between MSE and AoI.

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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