Bayesian online learning-based spectrum occupancy prediction in cognitive radio networks / (Record no. 5316)

MARC details
000 -LEADER
fixed length control field 01957naa a22003018i 4500
001 - CONTROL NUMBER
control field 00005316
003 - CONTROL NUMBER IDENTIFIER
control field FR-PaOEC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20211006062616.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field a o d i
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr || |||m|n||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190601s2018 ||| o i|0| 0 eng d
035 ## - SYSTEM CONTROL NUMBER
System control number (FR-PaOEC)
040 ## - CATALOGING SOURCE
Original cataloging agency FR-PaOEC
Transcribing agency ES-MaONT
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Mikaeil, Ahmed Mohammed
9 (RLIN) 3331
245 10 - TITLE STATEMENT
Title Bayesian online learning-based spectrum occupancy prediction in cognitive radio networks /
Statement of responsibility, etc. Ahmed Mohammed Mikaeil
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Geneva :
Name of publisher, distributor, etc. International Telecommunication Union,
Date of publication, distribution, etc. 2018.
300 ## - PHYSICAL DESCRIPTION
Extent 6 p.
336 ## - CONTENT TYPE
Content type term texto (visual)
Source isbdcontent
337 ## - MEDIA TYPE
Media type term electrónico
Source isbdmedia
338 ## - CARRIER TYPE
Carrier type term recurso en línea
Source rdacarrier
520 3# - SUMMARY, ETC.
Summary, etc. 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.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Tecnologías habilitadoras digitales
9 (RLIN) 18
653 #4 - INDEX TERM--UNCONTROLLED
Uncontrolled term Science and Technology
773 0# - HOST ITEM ENTRY
Host Biblionumber 4843
Title ITU Journal: ICT Discoveries
Related parts Vol. 2018, no. 1, p. 95-100
Enumeration and first page 2018:1<95
International Standard Serial Number 2616-8375
856 40 - ELECTRONIC LOCATION AND ACCESS
Host name itu-ilibrary.org
Uniform Resource Identifier https://www.itu.int/dms_pub/itu-s/opb/journal/S-JOURNAL-ICTS.V1I1-2018-11-PDF-E.pdf
Link text Acceso al documento
Nonpublic note Abierto
Electronic format type pdf
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Artículos
Source of classification or shelving scheme Universal Decimal Classification
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Koha itemnumber Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type Public note
      Acceso libre online Colección digital 6266 CDO CDO   12/09/2019   1000020175565 12/09/2019 12/09/2019 Artículos pdf
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