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

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