Software defined demodulation of multiple frequency shift keying with dense neural network for weak signal communications
M Kozlenko, V Vialkova - 2020 IEEE 15th International …, 2020 - ieeexplore.ieee.org
M Kozlenko, V Vialkova
2020 IEEE 15th International Conference on Advanced Trends in …, 2020•ieeexplore.ieee.orgIn 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.
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.
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.
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