HF spectrum activity prediction model based on HMM for cognitive radio applications

Melián Gutiérrez, Laura; Zazo Bello, Santiago; Blanco Murillo, José Luis; Pérez Álvarez, Iván; García Rodríguez, Adrián José y Pérez Díaz, Baltasar (2012). HF spectrum activity prediction model based on HMM for cognitive radio applications. "Physical Communication" ; pp. 1-13. ISSN 1874-4907. https://doi.org/10.1016/j.phycom.2012.09.004.

Descripción

Título: HF spectrum activity prediction model based on HMM for cognitive radio applications
Autor/es:
  • Melián Gutiérrez, Laura
  • Zazo Bello, Santiago
  • Blanco Murillo, José Luis
  • Pérez Álvarez, Iván
  • García Rodríguez, Adrián José
  • Pérez Díaz, Baltasar
Tipo de Documento: Artículo
Título de Revista/Publicación: Physical Communication
Fecha: Noviembre 2012
Materias:
Palabras Clave Informales: Cognitive radio; HF; Hidden Markov model; Prediction
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Although most of the research on Cognitive Radio is focused on communication bands above the HF upper limit (30 MHz), Cognitive Radio principles can also be applied to HF communications to make use of the extremely scarce spectrum more efficiently. In this work we consider legacy users as primary users since these users transmit without resorting to any smart procedure, and our stations using the HFDVL (HF Data+Voice Link) architecture as secondary users. Our goal is to enhance an efficient use of the HF band by detecting the presence of uncoordinated primary users and avoiding collisions with them while transmitting in different HF channels using our broad-band HF transceiver. A model of the primary user activity dynamics in the HF band is developed in this work to make short-term predictions of the sojourn time of a primary user in the band and avoid collisions. It is based on Hidden Markov Models (HMM) which are a powerful tool for modelling stochastic random processes and are trained with real measurements of the 14 MHz band. By using the proposed HMM based model, the prediction model achieves an average 10.3% prediction error rate with one minute-long channel knowledge but it can be reduced when this knowledge is extended: with the previous 8 min knowledge, an average 5.8% prediction error rate is achieved. These results suggest that the resulting activity model for the HF band could actually be used to predict primary users activity and included in a future HF cognitive radio based station.

Más información

ID de Registro: 16774
Identificador DC: http://oa.upm.es/16774/
Identificador OAI: oai:oa.upm.es:16774
Identificador DOI: 10.1016/j.phycom.2012.09.004
URL Oficial: http://www.sciencedirect.com/science/article/pii/S1874490712000742#
Depositado por: Memoria Investigacion
Depositado el: 10 Ago 2013 08:10
Ultima Modificación: 01 Dic 2014 23:56
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