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

被引：10
作者：
Abode, Daniel [1 ]
Adeogun, Ramoni [1 ]
Berardinelli, Gilberto [1 ]
机构：
[1] Aalborg Univ, Dept Elect Syst, Aalborg, Denmark
来源：
2023 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC | 2023年
关键词：
Interference management; power control; graph neural networks; channel state information; subnetworks;
D O I：
10.1109/WCNC55385.2023.10118984
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
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页数：6
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