Publication detail

Optimization of Machine Learning Parameters for Spectrum Survey Analysis

URBAN, R. STEINBAUER, M.

Original Title

Optimization of Machine Learning Parameters for Spectrum Survey Analysis

Type

conference paper

Language

English

Original Abstract

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.

Keywords

machine learning, cognitive radio, spectrum analysis

Authors

URBAN, R.; STEINBAUER, M.

RIV year

2014

Released

30. 9. 2014

ISBN

978-1-934142-28-8

Book

Proceedings of PIERS 2014 in Guangzhou

ISBN

1559-9450

Periodical

Progress In Electromagnetics

State

United States of America

Pages from

612

Pages to

615

Pages count

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"
}