Software Demodulation of Weak Radio Signals using Convolutional Neural Network

被引：0
|
作者：
Kozlenko, Mykola [1 ]
Lazarovych, Ihor [1 ]
Tkachuk, Valerii [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：
2020 IEEE 7TH INTERNATIONAL CONFERENCE ON ENERGY SMART SYSTEMS (2020 IEEE ESS) | 2020年
关键词：
Wide Area Monitoring; Electric Power System; Smart Grid; Weak Signal Communications; JT65A; Digital Communications; Demodulation; Frequency Shift Keying; Software Defined Radio; Machine Learning; Deep Learning; Artificial Neural Network; Deep Neural Network; Convolutional Neural Network; Interference Immunity; Symbol Error Rate; Bit Error Rate;
D O I：
10.1109/ess50319.2020.9160035
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper we proposed the use of JT65A radio communication protocol for data exchange in wide-area monitoring systems in electric power systems. We investigated the software demodulation of the multiple frequency shift keying weak signals transmitted with JT65A communication protocol using deep convolutional neural network. We presented the demodulation performance in form of symbol and bit error rates. We focused on the interference immunity of the protocol over an additive white Gaussian noise with average signal-to-noise ratios in the range from -30 dB to 0 dB, which was obtained for the first time. We proved that the interference immunity is about 1.5 dB less than the theoretical limit of non-coherent demodulation of orthogonal MFSK signals.
引用
收藏
页码：339 / 342
页数：4
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