Detail publikace

Optimization of Machine Learning Parameters for Spectrum Survey Analysis

URBAN, R. STEINBAUER, M.

Originální název

Optimization of Machine Learning Parameters for Spectrum Survey Analysis

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

machine learning, cognitive radio, spectrum analysis

Autoři

URBAN, R.; STEINBAUER, M.

Rok RIV

2014

Vydáno

30. 9. 2014

ISBN

978-1-934142-28-8

Kniha

Proceedings of PIERS 2014 in Guangzhou

ISSN

1559-9450

Periodikum

Progress In Electromagnetics

Stát

Spojené státy americké

Strany od

612

Strany do

615

Strany počet

4

BibTex

@inproceedings{BUT109329,
  author="Robert {Urban} and Miloslav {Steinbauer}",
  title="Optimization of Machine Learning Parameters for Spectrum Survey Analysis",
  booktitle="Proceedings of PIERS 2014 in Guangzhou",
  year="2014",
  journal="Progress In Electromagnetics",
  pages="612--615",
  isbn="978-1-934142-28-8",
  issn="1559-9450"
}