Detail výsledku
Comparison of Neural Models of UWB and 60GHz In-car Transmission Channels
Raida Zbyněk, prof. Dr. Ing., UREL (FEKT)
Knowledge of characteristics of the transmission channel is advantageous for the selection of a suitable location of transmitting and receiving antennas, choice of the carrier frequency and the transmission parameters such as bit rate, modulation type, coding, etc. However, the description of properties of the transmission channel can be computationally time consuming, and the computational complexity increases with the increasing frequency. The transmission channel can be modeled by an artificial neural network to reduce the computational complexity compared to the analysis using full-wave simulation programs (CST, HFSS, etc.). Two neural network architectures were selected (a feed-forward one and a radial basis function one) to model an in-car transmission channel. For each neural model, a study of the model error, the speed of training and the network complexity is given.
Artificial neural network; feed-forward network; radial basis function network; transmission channel measurement; estimation of in-car channel parameters; transfer function
@inproceedings{BUT129209,
author="Martin {Kotol} and Zbyněk {Raida}",
title="Comparison of Neural Models of UWB and 60GHz
In-car Transmission Channels
",
booktitle="CoBCom 2016",
year="2016",
pages="64--68",
publisher="IEEE Xplore",
doi="10.1109/COBCOM.2016.7593493",
isbn="978-1-5090-2269-4",
url="http://ieeexplore.ieee.org.ezproxy.lib.vutbr.cz/document/7593493/"
}