Multi-Scenario mmWave Localization Using KNN Graphs and Graph Neural Networks
Year: 2026
Type: conference-paper
Abstract: Graph-based wireless localization framework for multi-scenario mmWave transmitter localization by KNN graphs and GraphSAGE style Graph Neural Network. The model achieves scalable and memory-efficient training on the synthetic DeepMIMO Campus3p5 dataset (MAE 0.73, RMSE 1.18), and high level of generalisation for both synthetic and measured mmWave environments on a real-world Effective Channel dataset (R² 0.9917).
Authors: Ali, Asad, Saddiqa, Aysha
Institution: University of Greenwich
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7161572256
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