Graph neural networks (GNNs) have gained significant popularity for
classification tasks in machine learning, yet their applications to regression
problems remain limited. Concurrently, attention mechanisms have emerged as
powerful tools in sequential learning tasks. In this paper, we employ GNNs and
attention mechanisms to address a classical but challenging nonlinear
regression problem: network localization. We propose a novel GNN-based network
localization method that achieves exceptional stability and accuracy in the
presence of severe non-line-of-sight (NLOS) propagations, while eliminating the
need for laborious offline calibration or NLOS identification. Extensive
experimental results validate the effectiveness and high accuracy of our
GNN-based localization model, particularly in challenging NLOS scenarios.
However, the proposed GNN-based model exhibits limited flexibility, and its
accuracy is highly sensitive to a specific hyperparameter that determines the
graph structure. To address the limitations and extend the applicability of the
GNN-based model to real scenarios, we introduce two attentional graph neural
networks (AGNNs) that offer enhanced flexibility and the ability to
automatically learn the optimal hyperparameter for each node. Experimental
results confirm that the AGNN models are able to enhance localization accuracy,
providing a promising solution for real-world applications. We also provide
some analyses of the improved performance achieved by the AGNN models from the
perspectives of dynamic attention and signal denoising characteristics.

提出了一种基于图神经网络的网络定位方法，通过结合关注机制，在严重的非直线视线传播情况下实现了卓越的稳定性和准确性，消除了离线校准或非直线视线识别的需求。