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
相关论文
共 50 条
  • [21] Impulse signals classification using one dimensional convolutional neural network
    Olkhovskiy, Mikhail
    Mullerova, Eva
    Martinek, Petr
    JOURNAL OF ELECTRICAL ENGINEERING-ELEKTROTECHNICKY CASOPIS, 2020, 71 (06): : 397 - 405
  • [22] Software effort estimation using convolutional neural network and fuzzy clustering
    Azzeh M.
    Alkhateeb A.
    Bou Nassif A.
    Neural Computing and Applications, 2024, 36 (23) : 14449 - 14464
  • [23] Classifying Radio Galaxies with the Convolutional Neural Network
    Aniyan, A. K.
    Thorat, K.
    ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES, 2017, 230 (02):
  • [24] Demodulation of Multi-Level Data Using Convolutional Neural Network in Holographic Data Storage
    Katano, Yutaro
    Muroi, Tetsuhiko
    Kinoshita, Nobuhiro
    Ishii, Norihiko
    2018 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA), 2018, : 728 - 732
  • [25] Convolutional Neural Network for Software Vulnerability Detection
    Yang, Kaixi
    Miller, Paul
    Martinez-del-Rincon, Jesus
    2022 CYBER RESEARCH CONFERENCE - IRELAND (CYBER-RCI), 2022, : 83 - 86
  • [26] A Novel Demodulation Scheme of MSK Signals Based on One-Dimensional Convolutional Neural Network under Impulsive Noise
    Jia, Guangyao
    Lu, Hangyu
    Wang, Shun
    Xu, Xin
    Liu, Xiaojun
    ELECTRONICS, 2023, 12 (21)
  • [27] Digital demodulation technology of software radio
    Huang, KL
    Wang, X
    ISTM/2005: 6th International Symposium on Test and Measurement, Vols 1-9, Conference Proceedings, 2005, : 7166 - 7169
  • [28] A novel approach for detection of dyslexia using convolutional neural network with EOG signals
    Ramis Ileri
    Fatma Latifoğlu
    Esra Demirci
    Medical & Biological Engineering & Computing, 2022, 60 : 3041 - 3055
  • [29] A novel approach for detection of dyslexia using convolutional neural network with EOG signals
    Ileri, Ramis
    Latifoglu, Fatma
    Demirci, Esra
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2022, 60 (11) : 3041 - 3055
  • [30] Femoral Fracture Assessment Using Acceleration Signals Combined with Convolutional Neural Network
    Zhang, Jiqiao
    Zhu, Silang
    Jin, Zihan
    Yang, Wenbin
    Chen, Gongfa
    Cui, Fangsen
    JOURNAL OF VIBRATION ENGINEERING & TECHNOLOGIES, 2024, 12 (03) : 4987 - 5005
12345