TY - GEN
T1 - Power Control for 6G Industrial Wireless Subnetworks
T2 - 2023 IEEE Wireless Communications and Networking Conference (WCNC)
AU - Abode, Daniel Ohizimede
AU - Adeogun, Ramoni Ojekunle
AU - Berardinelli, Gilberto
PY - 2023/5/12
Y1 - 2023/5/12
N2 - 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.
AB - 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.
KW - 6G
KW - Artificial Intelligence (AI)
KW - Interference Management
KW - Power Control
KW - Wireless Communication
KW - graph neural network
UR - http://www.scopus.com/inward/record.url?scp=85159789381&partnerID=8YFLogxK
U2 - 10.1109/WCNC55385.2023.10118984
DO - 10.1109/WCNC55385.2023.10118984
M3 - Article in proceeding
SN - 978-1-6654-9123-5
T3 - I E E E Wireless Communications and Networking Conference. Proceedings
BT - 2023 IEEE Wireless Communications and Networking Conference (WCNC)
PB - IEEE
Y2 - 26 March 2023 through 29 March 2023
ER -
