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http://hdl.handle.net/123456789/9078| 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 demodulation 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. |
| URI: | http://hdl.handle.net/123456789/9078 |
| 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 |
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