  6th Generation (6G) industrial wireless subnetworks are expected to replace
wired connectivity for control operation in robots and production modules.
Interference management techniques such as centralized power control can
improve spectral efficiency in dense deployments of such subnetworks. However,
existing solutions for centralized power control may require full channel state
information (CSI) of all the desired and interfering links, which may be
cumbersome and time-consuming to obtain in dense deployments. This paper
presents a novel solution for centralized power control for industrial
subnetworks based on Graph Neural Networks (GNNs). The proposed method only
requires the subnetwork positioning information, usually known at the central
controller, and the knowledge of the desired link channel gain during the
execution phase. Simulation results show that our solution achieves similar
spectral efficiency as the benchmark schemes requiring full CSI in runtime
operations. Also, robustness to changes in the deployment density and
environment characteristics with respect to the training phase is verified.
