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Title: Deep learning approach to signal processing in infocommunications
Authors: Kozlenko, Mykola
Lazarovych, Ihor
Kuz, Mykola
Козленко, Микола Іванович
Лазарович, Ігор Миколайович
Кузь, Микола Васильович
Keywords: spread spectrum
communication system
ampitude noise shift keying
digital communications
software defined radio
machine learning
deep learning
artificial neural network
deep neural network
interference immunity
bit error rate
symbol error rate
Issue Date: 30-Sep-2020
Publisher: Taras Shevchenko National University of Kyiv
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.
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.
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