Software Demodulation of Weak Radio Signals using Convolutional Neural Network

Autor:	Mykola Kozlenko, Valerii Tkachuk, Vira Vialkova, I. Lazarowych
Rok vydání:	2020
Předmět:	symbols.namesake Additive white Gaussian noise Signal-to-noise ratio Interference (communication) Computer science Electronic engineering symbols Bit error rate Demodulation Keying Communications protocol Convolutional neural network Computer Science::Information Theory
Zdroj:	2020 IEEE 7th International Conference on Energy Smart Systems (ESS).
DOI:	10.1109/ess50319.2020.9160035
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::24500c8cebb92483d5333dea4e0614fa https://doi.org/10.1109/ess50319.2020.9160035 Zobrazit plný text záznamu