About: Optimization of Machine Learning Parameters for Spectrum Survey Analysis

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Description	This paper shows preliminary results of the optimization of machine learning parameters for cognitive radio application by brutal force calculations. We were analyzing frequency occupancy data of the huge measurement campaign of the spectrum background. For these date there are two possible states. Firstly, limited frequency band is occupied (detected signal level is above the threshold) by the other frequency signal \| there will be an interference for our system for this frequency band. Secondly, the frequency band is free of any other wireless radiation. These true/false data are analyzed in a context of the cognitive radio by the reinforcement learning and simple learning. Each channel received a score from the learning algorithm given by weighting function. The quality of the output scores is discussed in this paper according to the learning algorithm parameters and optional learning time. This paper shows preliminary results of the optimization of machine learning parameters for cognitive radio application by brutal force calculations. We were analyzing frequency occupancy data of the huge measurement campaign of the spectrum background. For these date there are two possible states. Firstly, limited frequency band is occupied (detected signal level is above the threshold) by the other frequency signal \| there will be an interference for our system for this frequency band. Secondly, the frequency band is free of any other wireless radiation. These true/false data are analyzed in a context of the cognitive radio by the reinforcement learning and simple learning. Each channel received a score from the learning algorithm given by weighting function. The quality of the output scores is discussed in this paper according to the learning algorithm parameters and optional learning time. (en)
Title	Optimization of Machine Learning Parameters for Spectrum Survey Analysis Optimization of Machine Learning Parameters for Spectrum Survey Analysis (en)
skos:prefLabel	Optimization of Machine Learning Parameters for Spectrum Survey Analysis Optimization of Machine Learning Parameters for Spectrum Survey Analysis (en)
skos:notation	RIV/00216305:26220/14:PU110396!RIV15-MSM-26220___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(ED2.1.00/03.0072)
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Steinbauer, Miloslav Urban, Robert
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoké učení technické v Brně / Fakulta elektrotechniky a komunikačních technologií
http://linked.open...dnocenehoVysledku	34963
http://linked.open...ai/riv/idVysledku	RIV/00216305:26220/14:PU110396
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	machine learning, cognitive radio, spectrum analysis (en)
http://linked.open.../riv/klicoveSlovo	cognitive radio spectrum analysis machine learning
http://linked.open...ontrolniKodProRIV	[29CDFE7750DA]
http://linked.open...v/mistoKonaniAkce	Guangzhou
http://linked.open...i/riv/mistoVydani	Neuveden
http://linked.open...i/riv/nazevZdroje	Proceedings of PIERS 2014 in Guangzhou
http://linked.open...in/vavai/riv/obor	JA
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Centre of sensor, information and communication systems
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Steinbauer, Miloslav Urban, Robert
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open.../riv/zahajeniAkce	2014-08-25 (xsd:date)
number of pages	4 (xsd:int)
http://purl.org/ne...btex#hasPublisher	Neuveden
https://schema.org/isbn	978-1-934142-28-8
http://localhost/t...ganizacniJednotka	26220

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