| Paper ID | SPTM-11.2 |
| Paper Title |
GRAPH NEURAL NETWORK FOR LARGE-SCALE NETWORK LOCALIZATION |
| Authors |
Wenzhong Yan, Chinese University of Hong Kong, Shenzhen, China; Di Jin, Technische Universität Darmstadt, Germany; Zhidi Lin, Feng Yin, Chinese University of Hong Kong, Shenzhen, China |
| Session | SPTM-11: Graphs Neural Networks |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation Time: | Wednesday, 09 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: Signal Processing over Networks |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
