000			01957naa a22003018i 4500
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003			FR-PaOEC
005			20211006062616.0
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007			cr || |||m|n||
008			190601s2018 ||| o i|0| 0 eng d
035			_a(FR-PaOEC)
040			_aFR-PaOEC _cES-MaONT
100	1		_aMikaeil, Ahmed Mohammed _93331
245	1	0	_aBayesian online learning-based spectrum occupancy prediction in cognitive radio networks / _cAhmed Mohammed Mikaeil
260			_aGeneva : _bInternational Telecommunication Union, _c2018.
300			_a6 p.
336			_atexto (visual) _2isbdcontent
337			_aelectrónico _2isbdmedia
338			_arecurso en línea _2rdacarrier
520	3		_aPredicting 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.
650		0	_aTecnologías habilitadoras digitales _918
653		4	_aScience and Technology
773	0		_04843 _tITU Journal: ICT Discoveries _gVol. 2018, no. 1, p. 95-100 _q2018:1<95 _x2616-8375
856	4	0	_aitu-ilibrary.org _uhttps://www.itu.int/dms_pub/itu-s/opb/journal/S-JOURNAL-ICTS.V1I1-2018-11-PDF-E.pdf _yAcceso al documento _x0 _qpdf
942			_cART _2udc