Empirical IIoT Data Traffic Analysis and Comparison to 3GPP 5G Models

5G New Radio (NR) technology is expected to enable the wireless transition of manufacturing processes in modern factories. In order to proper dimension the 5G system, accurate models of the data and control traffic in industrial use cases are needed. In this paper, we analyze the industrial traffic empirically measured in different Danish factories and compare its characteristics to the modeling assumptions used in the 3GPP for 5G NR performance analyses. In particular, three relevant use cases are studied: a unit test cell for actuator calibration, a visual inspection cell, and the control of an autonomous mobile robot (AMR). Our results indicate that some of the relevant 3GPP assumptions for industrial traffic such as exponential packet-interarrival time (PIAT) for aperiodic traffic and fixed packet sizes are only partially corroborated by experimental evidence. Moreover, industrial traffic can exhibit a large burstiness that may lead to conservative admission control and radio resource allocation.