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

Abstract

In cyber-physical systems, as in 5G and beyond,multiple physical processes require timely online monitoring at aremote device. There, the received information is used to estimatecurrent and future process values. When transmitting the processdata over a communication channel, source-channel coding isused in order to reduce data errors. During transmission, ahigh data resolution is helpful to capture the value of theprocess variables precisely. However, this typically comes withlong transmission delays reducing the utilizability of thedata,since the estimation quality gets reduced over time. In thispaper, the trade-off between having recent data and precisemeasurements 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 precisionof the predicted process values. AoI appears inherently withinthe MSE expressions, yet it can be relatively easier to optimize.Our goal is to minimize a time-averaged version of both metrics.We follow ashort blocklengthsource-channel coding approach,and optimize the parameters of the codes being used in order todescribe anachievability regionbetween MSE and AoI.