Multi-Scenario mmWave Localization Using KNN Graphs and Graph Neural Networks

Asad Ali; Aysha Saddiqa

doi:10.5281/zenodo.20264594

May 18, 2026Open Access

Multi-Scenario mmWave Localization Using KNN Graphs and Graph Neural Networks

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Key Points

To develop a graph-based framework for accurate mmWave transmitter localization across multiple scenarios.
Utilized KNN graphs and GraphSAGE style Graph Neural Networks for localization.
Trained on the synthetic DeepMIMO Campus3p5 dataset and validated using a real-world Effective Channel dataset.
Achieved scalable and memory-efficient training procedures.
Achieved Mean Absolute Error (MAE) of 0.73 and Root Mean Square Error (RMSE) of 1.18 on the synthetic dataset.
Demonstrated high generalization with R² of 0.9917 on real-world measured environments.

Implication

Demonstrates accurate mmWave localization in diverse environments, indicating robust model performance.

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).

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Cite This Study

Ali et al. (2026) studied this question.

synapsesocial.com/papers/6a0d5132f03e14405aa9da24 https://doi.org/10.5281/zenodo.20264594

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Multi-Scenario mmWave Localization Using KNN Graphs and Graph Neural Networks

Key Points

Implication

Abstract

Discussion

Cite This Study

Multi-Scenario mmWave Localization Using KNN Graphs and Graph Neural Networks

Key Points

Implication

Abstract

Discussion

Cite This Study

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