Evaluation Of Cnn And Cldnn Architectures On Radio Modulation Datasets

Pijáčková, Kristýna

Evaluation Of Cnn And Cldnn Architectures On Radio Modulation Datasets

but.event.date	27.04.2021	cs
but.event.title	STUDENT EEICT 2021	cs
dc.contributor.author	Pijáčková, Kristýna
dc.date.accessioned	2021-07-21T07:06:53Z
dc.date.available	2021-07-21T07:06:53Z
dc.date.issued	2021	cs
dc.description.abstract	This paper presents an evaluation of deep learning architectures designed for modulationrecognition. The evaluation inspects, whether the architectures behave in the same way as they didon the dataset they were designed on. The architectures are trained and tested on two different radiomodulation datasets. This results in proposing additional binary classification as a method to reducemisclassification of QAM modulation types in one of the datasets.	en
dc.format	text	cs
dc.format.extent	42-45	cs
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Proceedings I of the 27st Conference STUDENT EEICT 2021: General papers. s. 42-45. ISBN 978-80-214-5942-7	cs
dc.identifier.isbn	978-80-214-5942-7
dc.identifier.uri	http://hdl.handle.net/11012/200673
dc.language.iso	cs	cs
dc.publisher	Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií	cs
dc.relation.ispartof	Proceedings I of the 27st Conference STUDENT EEICT 2021: General papers	en
dc.relation.uri	https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni	cs
dc.rights	© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií	cs
dc.rights.access	openAccess	en
dc.subject	Radio modulation	en
dc.subject	classification	en
dc.subject	neural network	en
dc.subject	deep learning	en
dc.subject	CNN	en
dc.subject	CLDNN	en
dc.title	Evaluation Of Cnn And Cldnn Architectures On Radio Modulation Datasets	en
dc.type.driver	conferenceObject	en
dc.type.status	Peer-reviewed	en
dc.type.version	publishedVersion	en
eprints.affiliatedInstitution.department	Fakulta elektrotechniky a komunikačních technologií	cs

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