论文标题
用于大规模网络本地化的图形神经网络
Graph Neural Network for Large-Scale Network Localization
论文作者
论文摘要
图形神经网络（GNN）很受欢迎，可用于在机器学习的背景下用于对结构化数据进行分类。但令人惊讶的是，它们很少应用于回归问题。在这项工作中，我们采用GNN来解决一个经典但具有挑战性的非线性回归问题，即网络本地化。我们的主要发现是井井有条。首先，就准确性，鲁棒性和计算时间而言，GNN可能是大规模网络本地化的最佳解决方案。其次，通信范围的适当阈值对于其出色的性能至关重要。仿真结果证实了所提出的基于GNN的方法在迄今为止优于所有最新基准测试。从理论上讲，这种鼓舞人心的结果在数据聚合，非视线（NLOS）降噪和低通滤波效果方面是合理的，所有这些结果都受到邻居选择阈值的影响。代码可从https://github.com/yanzongzi/gnn-for-localization获得。
Graph neural networks (GNNs) are popular to use for classifying structured data in the context of machine learning. But surprisingly, they are rarely applied to regression problems. In this work, we adopt GNN for a classic but challenging nonlinear regression problem, namely the network localization. Our main findings are in order. First, GNN is potentially the best solution to large-scale network localization in terms of accuracy, robustness and computational time. Second, proper thresholding of the communication range is essential to its superior performance. Simulation results corroborate that the proposed GNN based method outperforms all state-of-the-art benchmarks by far. Such inspiring results are theoretically justified in terms of data aggregation, non-line-of-sight (NLOS) noise removal and low-pass filtering effect, all affected by the threshold for neighbor selection. Code is available at https://github.com/Yanzongzi/GNN-For-localization.
