Detail publikace

NEURAL MODEL OF TRANSMISSION CHANNEL IN 60 GHZ ISM BAND

Originální název

NEURAL MODEL OF TRANSMISSION CHANNEL IN 60 GHZ ISM BAND

Anglický název

NEURAL MODEL OF TRANSMISSION CHANNEL IN 60 GHZ ISM BAND

Jazyk

en

Originální abstrakt

In this paper, methodology of estimating parameters of a wireless transmission channel inside a car is presented. The work is focused on the utilization of artificial neural networks for channel modelling in the frequency range from 55 GHz to 65 GHz. Promising results have been reached by a feed-forward neural network and a radial basis function neural network. In order to train the networks, a wireless transmission was carefully measured in a testing car. Measured data were properly processed to be used both for training neural networks and validating neural models.

Anglický abstrakt

In this paper, methodology of estimating parameters of a wireless transmission channel inside a car is presented. The work is focused on the utilization of artificial neural networks for channel modelling in the frequency range from 55 GHz to 65 GHz. Promising results have been reached by a feed-forward neural network and a radial basis function neural network. In order to train the networks, a wireless transmission was carefully measured in a testing car. Measured data were properly processed to be used both for training neural networks and validating neural models.

Dokumenty

BibTex


@inproceedings{BUT125220,
  author="Martin {Kotol} and Zbyněk {Raida}",
  title="NEURAL MODEL OF TRANSMISSION CHANNEL
IN 60 GHZ ISM BAND
",
  annote="In this paper, methodology of estimating parameters of a wireless transmission channel inside a car is presented. The work is focused on the utilization of artificial neural networks for channel modelling in the frequency range from 55 GHz to 65 GHz. Promising results have been reached by a feed-forward neural network and a radial basis function neural network. In order to train the networks, a wireless transmission was carefully measured in a testing car. Measured data were properly processed to be used both for training neural networks and validating neural models.",
  booktitle="Student EEICT",
  chapter="125220",
  howpublished="online",
  year="2016",
  month="april",
  pages="328--332",
  type="conference paper"
}