Título:
Predicting wireless RSSI using machine learning on graphs.
Otros títulos:
Coordinador:
Director:
Compilador:
Autor:
Rattaro, Claudina
Larroca, Federico
Capdehourat, Germán
Larroca, Federico
Capdehourat, Germán
Tutor:
Tipo de documento:
Ponencia
Editor:
Palabras clave:
Wireless communication
Knowledge engineering
Costs
Training data
Machine learning
Particle measurements
Graph neural networks
Embeddings
Graph representation learning
Link-prediction
Knowledge engineering
Costs
Training data
Machine learning
Particle measurements
Graph neural networks
Embeddings
Graph representation learning
Link-prediction
Descriptores:
Año de publicación:
2021
Contenido:
Resumen:
In wireless communications, optimizing the resource allocation requires the knowledge of the state of the channel. This is even more important in device-to-device communications, one typical use case in 5G/6G networks, where such knowledge is hard to obtain at reasonable signaling costs. In this paper, we study the use of graph-based machine learning methods to address this problem. That is to say, we learn to predict the channel state on a given link through measurements on other links, thus decreasing signaling overhead. In particular, we model the problem as a link-prediction one and we consider two representative approaches: Random Dot Product Graphs and Graph Neural Networks. The key point is that these methods consider the geometric structure underlying the data. They thus enable better generalization and require less training data than classic methods, as we show on our evaluation using a dataset of RSSI measurements of real-world Wi-Fi operating networks.
Descripción:
metadata.articulos.dc.description.uri:
Editorial:
IEEE
EN:
IEEE URUCON 2021 , Montevideo, Uruguay, 24-26 nov. 2021, pp. 372-376.
Financiadores:
Este trabajo ha sido apoyado por la Agencia Nacional de Investigación e Innovación (ANII), Uruguay, subvenciones Fondo Maria Viñas 3 2018 1 148149 y Fondo María Viñas 1 2019 1 155700.
Citación:
Rattaro, C., Larroca, F. y Capdehourat, G. Predicting wireless RSSI using machine learning on graphs [en línea]. EN: IEEE URUCON 2021, Montevideo, Uruguay, 24-26 nov. 2021, 5 p. DOI 10.1109/URUCON53396.2021.9647374
Citación:
metadata.articulos.cc.license.name:
ISBN:
e-ISBN:
ISSN:
ISMN:
Otros identificadores:
Cobertura geográfica:
Cobertura temporal:
Departamento académico:
Grupo de investigación:
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| RLC21.pdf | Camera-Ready | 192.83 KB | Adobe PDF |