Deep learning approach to signal processing in infocommunications

Kozlenko, Mykola; Lazarovych, Ihor; Kuz, Mykola; Козленко, Микола Іванович; Лазарович, Ігор Миколайович; Кузь, Микола Васильович

Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/9078

Full metadata record

DC Field	Value	Language
dc.contributor.author	Kozlenko, Mykola	-
dc.contributor.author	Lazarovych, Ihor	-
dc.contributor.author	Kuz, Mykola	-
dc.contributor.author	Козленко, Микола Іванович	-
dc.contributor.author	Лазарович, Ігор Миколайович	-
dc.contributor.author	Кузь, Микола Васильович	-
dc.date.accessioned	2021-02-01T12:50:31Z	-
dc.date.available	2021-02-01T12:50:31Z	-
dc.date.issued	2020-09-30	-
dc.identifier.citation	M. Kozlenko, I. Lazarovych, and M. Kuz, "Deep learning approach to signal processing in infocommunications," in Proc. 4th International Scientific and Practical Conference on Applied Systems and Technologies in the Information Society (AISTIS), V. Pleskach and V. Mironova, Eds. Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Sept. 30, 2020, pp. 81-82, doi: 10.5281/zenodo.4482757.	uk_UA
dc.identifier.uri	http://hdl.handle.net/123456789/9078	-
dc.description.abstract	Digital communications techniques based on random, chaotic, or noisy carriers are well known and successfully used in a number of applications. Simple on-off or amplitude shift noise keying modulation schemes are among the most popular. In this paper, we propose to use a classification model based on an artificial dense neural network and a deep learning approach for software-defined demodulation of spread spectrum signals.	uk_UA
dc.language.iso	en_US	uk_UA
dc.publisher	Taras Shevchenko National University of Kyiv	uk_UA
dc.subject	spread spectrum	uk_UA
dc.subject	communication system	uk_UA
dc.subject	ampitude noise shift keying	uk_UA
dc.subject	digital communications	uk_UA
dc.subject	demodulation	uk_UA
dc.subject	software defined radio	uk_UA
dc.subject	machine learning	uk_UA
dc.subject	deep learning	uk_UA
dc.subject	artificial neural network	uk_UA
dc.subject	deep neural network	uk_UA
dc.subject	interference immunity	uk_UA
dc.subject	bit error rate	uk_UA
dc.subject	symbol error rate	uk_UA
dc.title	Deep learning approach to signal processing in infocommunications	uk_UA
dc.type	Article	uk_UA
Appears in Collections:	Статті та тези (ФМІ)

Files in This Item:

File	Description	Size	Format
2020_AISTIS_kozlenko.pdf		492.61 kB	Adobe PDF	View/Open

Show simple item record

DSpace JSPUI

DSpace preserves and enables easy and open access to all types of digital content including text, images, moving images, mpegs and data sets