论文标题
6G工业无线子网的电源控制：图形神经网络方法
Power Control for 6G Industrial Wireless Subnetworks: A Graph Neural Network Approach
论文作者
论文摘要
预计第六代（6G）工业无线子网将取代有线连接性，用于机器人和生产模块中的控制操作。诸如集中电力控制之类的干扰管理技术可以提高此类子网密集部署的光谱效率。但是，现有的集中电力控制解决方案可能需要所有所需和干扰链接的完整渠道状态信息（CSI），这可能很麻烦且耗时，以便在密集的部署中获得。本文为基于图神经网络（GNNS）的工业子网集中电力控制提供了一种新颖的解决方案。所提出的方法仅需要通常在中央控制器上已知的子网定位信息，以及在执行阶段中所需的链路通道增益的知识。仿真结果表明，我们的解决方案达到的光谱效率与需要在运行时运行中完整的CSI的基准方案相似。同样，还验证了部署密度和环境特征相对于训练阶段的鲁棒性。
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.
