Graph neural networks (GNNs) are information processing architectures that model representations from networked data and allow for decentralized implementation through localized communications. Existing GNN architectures often assume ideal communication links, and ignore channel effects, such as fading and noise, leading to performance degradation in real-world implementation. This paper proposes graph neural networks over the air (AirGNNs), a novel GNN architecture that incorporates the communication model into the architecture. The AirGNN modifies the graph convolutional operation that shifts graph signals over random communication graphs to take into account channel fading and noise when aggregating features from neighbors, thus, improving the architecture robustness to channel impairments during testing. We propose a stochastic gradient descent based method to train the AirGNN, and show that the training procedure converges to a stationary solution. Numerical simulations on decentralized source localization and multi-robot flocking corroborate theoretical findings and show superior performance of the AirGNN over wireless communication channels.
翻译:图像神经网络(GNNs)是信息处理结构,建模来自网络数据,并允许通过本地通信进行分散实施。现有的GNN结构往往承担理想的通信连接,忽视频道效应,如消退和噪音,导致现实世界实施中的性能退化。本文提议了将通信模型纳入架构的新型GNN结构,即空中的图形神经网络(AirGNNs),这是将通信模型纳入该架构的新型GNN结构。AirGNN改变图形组合操作,将图形信号移动到随机通信图上,以便在集成邻居的功能时,考虑到通道消逝和噪音,从而改进结构坚固性,在测试期间引导障碍。我们提出了一种基于随机梯度梯度的梯度下移方法,用于培训AirGNNS,并显示培训程序与固定式解决方案相融合。关于分散源本地化和多机器人群传的数值模拟,证实了理论结论,并显示AGNNN在无线通信频道上的高级性能表现。