Software Defined Demodulation of Multiple Frequency Shift Keying with Dense Neural Network for Weak Signal Communications

被引：2

作者：

Kozlenko, Mykola ^{[1
]}

Vialkova, Vira ^{[2
]}

机构：

[1] Vasyl Stefanyk Precarpathian Natl Univ, Dept Informat Technol, Ivano Frankivsk, Ukraine

[2] Taras Shevchenko Natl Univ Kyiv, Dept Cyber Secur & Informat Protect, Kiev, Ukraine

来源：

15TH INTERNATIONAL CONFERENCE ON ADVANCED TRENDS IN RADIOELECTRONICS, TELECOMMUNICATIONS AND COMPUTER ENGINEERING (TCSET - 2020) | 2020年

关键词：

Weak Signal Communications; Earth-Moon-Earth; Moon Bounce; Digital Communication; Demodulation; Frequency Shift Keying; Machine Learning; Deep Learning; Artificial Neural Network; Dense Neural Network; Interference Immunity; Symbol Error Rate; and Bit Error Rate; !text type='PYTHON']PYTHON[!/text;

D O I：

10.1109/TCSET49122.2020.235501

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In this paper we present the symbol and bit error rate performance of the weak signal digital communications system. We investigate orthogonal multiple frequency shift keying modulation scheme with supervised machine learning demodulation approach using simple dense end-to-end artificial neural network. We focus on the interference immunity over an additive white Gaussian noise with average signal-to-noise ratios from -20 dB to 0 dB.

引用

页码：590 / 595

页数：6

共 22 条

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