Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach
This addresses interference management for improved spectral efficiency in 6G industrial deployments, offering a more practical solution by reducing information requirements, though it is incremental as it builds on existing power control methods with a novel application of GNNs.
The paper tackles the problem of centralized power control in dense 6G industrial wireless subnetworks, where existing methods require cumbersome full channel state information. It proposes a Graph Neural Network approach that uses only positioning information and desired link channel gain, achieving similar spectral efficiency as benchmark schemes requiring full CSI in simulations.
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.