Bayesian online learning-based spectrum occupancy prediction in cognitive radio networks /
Autor(es):
Mikaeil, Ahmed Mohammed
Editor: Geneva : International Telecommunication Union, 2018Descripción: 6 pTipo de contenido: texto (visual) Tipo de medio: electrónico
Tipo de soporte: recurso en líneaTema(s): Tecnologías habilitadoras digitales
| Science and TechnologyRecursos en línea: 
Acceso al documento
En: ITU Journal: ICT Discoveries Vol. 2018, no. 1, p. 95-100Resumen: Predicting the near future of primary user (PU) channel state availability (i.e. spectrum occupancy) is quite important in cognitive radio networks in order to avoid interfering its transmission by a cognitive spectrum user (i.e. secondary user (SU)). This paper introduces a new simple method for predicting PU channel state based on energy detection. In this method, we model the PU channel state detection sequence (i.e. "PU channel idle" and "PU channel occupied") as a time series represented by two different random variable distributions. We then introduce Bayesian online learning (BOL) to predict in advance the changes in time series (i.e. PU channel state.), so that the secondary user can adjust its transmission strategies accordingly. A simulation result proves the efficiency of the new approach in predicting PU channel state availability.
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El Centro de Documentación del Observatorio Nacional de las Telecomunicaciones y de la Sociedad de la Información (CDO) os da la bienvenida al catálogo bibliográfico sobre recursos digitales en las materias de Tecnologías de la Información y telecomunicaciones, Servicios públicos digitales, Administración Electrónica y Economía digital.
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Colección digital | Acceso libre online | 1000020175565 |
Predicting the near future of primary user (PU) channel state availability (i.e. spectrum occupancy) is quite important in cognitive radio networks in order to avoid interfering its transmission by a cognitive spectrum user (i.e. secondary user (SU)). This paper introduces a new simple method for predicting PU channel state based on energy detection. In this method, we model the PU channel state detection sequence (i.e. "PU channel idle" and "PU channel occupied") as a time series represented by two different random variable distributions. We then introduce Bayesian online learning (BOL) to predict in advance the changes in time series (i.e. PU channel state.), so that the secondary user can adjust its transmission strategies accordingly. A simulation result proves the efficiency of the new approach in predicting PU channel state availability.
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