Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach

TL;DR

This paper introduces a GNN-based centralized power control method for 6G industrial wireless subnetworks that achieves high spectral efficiency with reduced CSI requirements, enhancing practicality in dense deployments.

Contribution

It proposes a novel GNN approach that only needs subnetwork positioning and desired link channel gain, reducing CSI dependency compared to existing methods.

Findings

Achieves spectral efficiency comparable to full-CSI schemes

Demonstrates robustness to deployment density changes

Requires less channel information during operation

Abstract

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.