01957naa a22003018i 4500 5316 5316 00005316 FR-PaOEC 20211006062616.0 a o d i cr || |||m|n|| 190601s2018 ||| o i|0| 0 eng d (FR-PaOEC) FR-PaOEC ES-MaONT Mikaeil, Ahmed Mohammed 3331 Bayesian online learning-based spectrum occupancy prediction in cognitive radio networks / Ahmed Mohammed Mikaeil Geneva : International Telecommunication Union, 2018. 6 p. texto (visual) isbdcontent electrónico isbdmedia recurso en línea rdacarrier 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. Tecnologías habilitadoras digitales 18 Science and Technology 4843 ITU Journal: ICT Discoveries Vol. 2018, no. 1, p. 95-100 2018:1<95 2616-8375 itu-ilibrary.org https://www.itu.int/dms_pub/itu-s/opb/journal/S-JOURNAL-ICTS.V1I1-2018-11-PDF-E.pdf Acceso al documento 0 pdf ART udc 0 0 0 3 1 6266 01 01 online 2019-09-12 0 1000020175565 2019-09-12 00:00:00 2019-09-12 ART pdf