Graph Neural Network for Large-Scale Network Localization

TL;DR

This paper demonstrates that graph neural networks can effectively solve large-scale network localization problems, outperforming existing methods in accuracy, robustness, and speed, with theoretical justification for their success.

Contribution

It introduces a GNN-based approach for nonlinear network localization, a problem rarely tackled with GNNs, and shows its superiority over state-of-the-art benchmarks.

Findings

GNN achieves top accuracy, robustness, and efficiency in large-scale localization.

Proper thresholding of communication range is crucial for optimal GNN performance.

Simulation confirms GNN outperforms existing methods significantly.

Abstract

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.