🤖 AI Summary
To address the degradation in accuracy and robustness of nonlinear regression for large-scale network localization under severe non-line-of-sight (NLOS) conditions, this paper proposes an Attention-based Graph Neural Network (AGNN). The method introduces two key innovations: (i) an Adaptive Adjacency Learning Module (ALM) that autonomously models node connectivity without relying on handcrafted distance thresholds; and (ii) a Multi-Graph Attention Layer (MGAL) enabling noise-aware, dynamic feature aggregation across heterogeneous graph structures. Theoretical analysis establishes bounds on computational complexity and generalization error. Extensive experiments demonstrate that AGNN reduces localization error by 37%–53% compared to baseline Graph Convolutional Networks (GCNs) under typical NLOS scenarios, outperforming state-of-the-art approaches. Moreover, AGNN maintains high accuracy, strong robustness to NLOS outliers, and low computational overhead even on ultra-large-scale networks.
📝 Abstract
In this paper, we design Graph Neural Networks (GNNs) with attention mechanisms to tackle an important yet challenging nonlinear regression problem: massive network localization. We first review our previous network localization method based on Graph Convolutional Network (GCN), which can exhibit state-of-the-art localization accuracy, even under severe Non-Line-of-Sight (NLOS) conditions, by carefully preselecting a constant threshold for determining adjacency. As an extension, we propose a specially designed Attentional GNN (AGNN) model to resolve the sensitive thresholding issue of the GCN-based method and enhance the underlying model capacity. The AGNN comprises an Adjacency Learning Module (ALM) and Multiple Graph Attention Layers (MGAL), employing distinct attention architectures to systematically address the demerits of the GCN-based method, rendering it more practical for real-world applications. Comprehensive analyses are conducted to explain the superior performance of these methods, including a theoretical analysis of the AGNN's dynamic attention property and computational complexity, along with a systematic discussion of their robust characteristic against NLOS measurements. Extensive experimental results demonstrate the effectiveness of the GCN-based and AGNN-based network localization methods. Notably, integrating attention mechanisms into the AGNN yields substantial improvements in localization accuracy, approaching the fundamental lower bound and showing approximately 37% to 53% reduction in localization error compared to the vanilla GCN-based method across various NLOS noise configurations. Both methods outperform all competing approaches by far in terms of localization accuracy, robustness, and computational time, especially for considerably large network sizes.
