What question did this study set out to answer?

This research aims to improve power control in D2D communication systems using graph neural networks (GNNs).

June 3, 2026

Graph embedding-enhanced GNN for scalable D2D power control

Puntos clave

This research aims to improve power control in D2D communication systems using graph neural networks (GNNs).
Utilized graph embedding techniques within GNNs for enhanced modeling.
Addressed non-convex scenarios in power control systems.
Evaluated performance in diverse environments.
Enhanced power control yielded robust results even in unseen environments.
Increased computational time was modest compared to traditional algorithms.

Resumen

As mobile communication and device numbers surge, wireless resources face increasing demand. D2D communication conserves base station resources but creates interference, making power control a crucial aspect of the system. Traditional algorithms struggle to strike a balance between performance and computational cost in non-convex scenarios. Data-driven methods, particularly GNNs, now excel at modelling complex relationships between devices. This study utilizes GNNs and graph embedding to enhance power control, yielding robust results across diverse and even unseen environments, with only a modest increase in computational time.

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