Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/49277
Título: Upper Confidence Bound learning approach for real HF measurements
Autores/as: Melian Gutierrez,Laura Beatriz 
Modi, Navikkumar
Moy, Christophe
Pérez-Álvarez, Iván 
Bader, Faouzi
Zazo, Santiago
Clasificación UNESCO: 3325 Tecnología de las telecomunicaciones
Palabras clave: Cognitive radio
Transceivers
Heuristic algorithms
Learning (artificial intelligence)
Databases
Fecha de publicación: 2015
Publicación seriada: 2015 IEEE International Conference on Communication Workshop, ICCW 2015
Conferencia: IEEE International Conference on Communication Workshop, ICCW 2015 
Resumen: New strategies based on cognitive radio are being discussed to make a more efficient use of the HF band. Multiple users transmit in this band with a worldwide coverage but having multiple collisions with other HF stations. The use of the Upper Confidence Bound (UCB) algorithm is proposed in this work to provide them with a dynamic spectrum access mitigating mutual interference. Based on reinforcement learning, it is used to select the best channel of a wideband HF transceiver in terms of availability. The feasibility of this proposal is demonstrated with real measurements of amateur contests in the HF band. To the best of the authors' knowledge, this is one of the few works on learning with real HF measurements.
URI: http://hdl.handle.net/10553/49277
ISBN: 9781467363051
ISSN: 2164-7038
DOI: 10.1109/ICCW.2015.7247209
Fuente: 2015 IEEE International Conference on Communication Workshop, ICCW 2015 (7247209), p. 381-386
Colección:Actas de congresos
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