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Description
  • Deploying new wireless systems is very difficult in the current system of frequency spectrum assignments. The most prospective spectrum bands are fixed allocated for specific services and these bands are controlled by national telecommunication (governmental) organizations. Measurements of the frequency spectrum background show that a huge underutilization of the frequency spectrum exists. Overall effectiveness of spectrum utilization is widely discussed in the context of cognitive radio and dynamic spectrum allocation. An autonomous and intelligent system should improve spectrum sharing capabilities by detecting current, licensed users – primary users with established connections in unused spectrum bands for cognitive radio – and secondary users. In this paper, a machine learning algorithm is used and channels in particular bands are scored according to a weight function. Real measured data are used as frequency spectrum background. This system should decrease interference in communication channe
  • Deploying new wireless systems is very difficult in the current system of frequency spectrum assignments. The most prospective spectrum bands are fixed allocated for specific services and these bands are controlled by national telecommunication (governmental) organizations. Measurements of the frequency spectrum background show that a huge underutilization of the frequency spectrum exists. Overall effectiveness of spectrum utilization is widely discussed in the context of cognitive radio and dynamic spectrum allocation. An autonomous and intelligent system should improve spectrum sharing capabilities by detecting current, licensed users – primary users with established connections in unused spectrum bands for cognitive radio – and secondary users. In this paper, a machine learning algorithm is used and channels in particular bands are scored according to a weight function. Real measured data are used as frequency spectrum background. This system should decrease interference in communication channe (en)
Title
  • Rapid Channel Assignment for Intelligent Indoor Scenarios
  • Rapid Channel Assignment for Intelligent Indoor Scenarios (en)
skos:prefLabel
  • Rapid Channel Assignment for Intelligent Indoor Scenarios
  • Rapid Channel Assignment for Intelligent Indoor Scenarios (en)
skos:notation
  • RIV/00216305:26220/14:PU110395!RIV15-MSM-26220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED2.1.00/03.0072)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 41349
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/14:PU110395
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • cognitive radio, spectrum measurement, spectrum allocation, dynamic spectrum access (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [F2A33814FEF0]
http://linked.open...v/mistoKonaniAkce
  • The Hague
http://linked.open...i/riv/mistoVydani
  • Neuveden
http://linked.open...i/riv/nazevZdroje
  • The 8th European Conference on Antennas and Propagation (EuCAP 2014)
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Urban, Robert
  • Vlachová Hutová, Eliška
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Neuveden
https://schema.org/isbn
  • 978-88-907018-4-9
http://localhost/t...ganizacniJednotka
  • 26220
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