Research Article
Neural Networks Mode Classification based on Frequency Distribution Features
@INPROCEEDINGS{10.1109/CROWNCOM.2007.4549806, author={Andrea F.Cattoni and Marina Ottonello and Mirco Raffetto and Carlo S. Regazzoni}, title={Neural Networks Mode Classification based on Frequency Distribution Features}, proceedings={2nd International ICST Conference on Cognitive Radio Oriented Wireless Networks and Communications}, publisher={IEEE}, proceedings_a={CROWNCOM}, year={2008}, month={6}, keywords={Chromium Cognitive radio FCC Frequency Multiaccess communication Neural networks Radio spectrum management Radiofrequency identification Transceivers Vehicle dynamics}, doi={10.1109/CROWNCOM.2007.4549806} }- Andrea F.Cattoni
Marina Ottonello
Mirco Raffetto
Carlo S. Regazzoni
Year: 2008
Neural Networks Mode Classification based on Frequency Distribution Features
CROWNCOM
IEEE
DOI: 10.1109/CROWNCOM.2007.4549806
Abstract
The growing number of new emerging wireless standards is creating regulatory problems in allocating the unlicensed frequencies. A possible solution for increasing the frequency re-usage within the framework of info-mobility cellular systems is the joint exploitation of Smart Antennas and Cognitive Radio. In the paper a Mode Identification algorithm, based on frequency distribution features and multiple neural network classifiers, for a Cognitive Base Transceiver Station is presented. Simulated results, obtained in a simplified framework, will prove the effectiveness of the proposed approach.
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