Graph Neural Network for Large-Scale Network Localization
This work addresses network localization, a classic but challenging problem in fields like wireless sensor networks, by showing GNNs can be effectively adapted for regression tasks, though it appears incremental as it applies an existing method to a new problem domain.
The authors tackled the challenging nonlinear regression problem of network localization by applying graph neural networks (GNNs), demonstrating that their GNN-based method outperforms all state-of-the-art benchmarks in accuracy, robustness, and computational time for large-scale scenarios.
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.