Optimization of Machine Learning Parameters for Spectrum Survey Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F14%3APU110396" target="_blank" >RIV/00216305:26220/14:PU110396 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Optimization of Machine Learning Parameters for Spectrum Survey Analysis

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Optimization of Machine Learning Parameters for Spectrum Survey Analysis

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/ED2.1.00%2F03.0072" target="_blank" >ED2.1.00/03.0072: Centrum senzorických, informačních a komunikačních systémů (SIX)</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of PIERS 2014 in Guangzhou

ISBN

978-1-934142-28-8

ISSN

1559-9450

e-ISSN

—

Počet stran výsledku

4

Strana od-do

612-615

Název nakladatele

Neuveden

Místo vydání

Neuveden

Místo konání akce

Guangzhou

Datum konání akce

25. 8. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000393225900133