Título:
User association in wireless networks with distributed GNN-based reinforcement learning
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Autor:
Randall, Martín
Paternain, Santiago
Casas, Pedro
Larroca, Federico
Belzarena, Pablo
Paternain, Santiago
Casas, Pedro
Larroca, Federico
Belzarena, Pablo
Tutor:
Tipo de documento:
Ponencia
Editor:
Palabras clave:
User association
Mobile networks
Reinforcement learning
Graph neural networks
Wireless networks
Telecommunication traffic
Throughput
Performance metrics
Load management
Energy efficiency
Security
Resource management
Mobile networks
Reinforcement learning
Graph neural networks
Wireless networks
Telecommunication traffic
Throughput
Performance metrics
Load management
Energy efficiency
Security
Resource management
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Año de publicación:
2025
Contenido:
Resumen:
User association is crucial for optimizing the performance and utility of wireless networks, enhancing key aspects such as load balancing, spectrum efficiency, energy efficiency, and overall network performance. In this paper we tackle the user association challenge in wireless networks, particularly in resource-constrained connectivity scenarios. Our proposed approach, GROWTh (Graph Representation of Wireless systems Throughput fair), introduces a graph-based reinforcement learning framework that optimizes resource utilization through a fully decentralized algorithm. We validate GROWTh across diverse scenarios, including a 5 G deployment in densely populated areas characterized by high user density and traffic load, where it demonstrates significant improvements in various performance metrics. Notably, GROWTh achieves a substantial increase in system utility compared to traditional methods while simultaneously reducing user rejection rates. These findings highlight the effectiveness of GROWTh in managing user association in high-density environments and underscore its potential for real-world deployment.
Descripción:
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EN:
2025 12th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Paris, France, 18-20 jun. 2025, pp. 352-360.
Financiadores:
Austrian FFG AI4SIMPROD Project-AI-Assited Simulation y Digital Twining for Efficient Industrial Production- Project 909824.
Citación:
Randall, M., Paternain, S., Casas, P. y otros. User association in wireless networks with distributed GNN-based reinforcement learning [en línea]. EN: 2025 12th IFIP International Conference on New Technologies, Mobility and Security (NTMS), Paris, France, 18-20 jun. 2025, pp. 352-360. DOI: 10.1109/NTMS65597.2025.11076766.
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Departamento académico:
Telecomunicaciones
Grupo de investigación:
Análisis de Redes, Tráficos y Estadísticas de Servicios (ARTES)
Licencia:
Licencia Creative Commons Atribución (CC - By 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| RPCLB25.pdf | Camera-Ready | 1.32 MB | Adobe PDF |