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Conference object . 2023

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Weak sinusoidal signal extraction from white noise using convolutional neural network

Name: Weak sinusoidal signal extraction from white noise using convolutional neural network
Creator: Kozlenko, Mykola
Keywords: modulation, digital communications, machine learning, manipulation keying, JT65, detection, deep learning, convolutional neural network, demodulation, bit error rate

descriptionPublicationkeyboard_double_arrow_right Conference object 29 Nov 2023 English Publisher:Vasyl Stefanyk Precarpathian National University

Authors: Kozlenko, Mykola;

doi: 10.5281/zenodo.10467333

Weak sinusoidal signal extraction from white noise using convolutional neural network

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Abstract

A great number of analog and digital data communications schemes use the sinusoidal waveform as a basic elementary signal, including the spread spectrum data exchange techniques. Detection of the presence of the sinusoidal waveform in a mixture of signal and noise is a common task, regardless the specific modulation scheme. This paper presents the machine learning-based approach for detection of the sinusoidal wave. It presents the structure of the convolutional neural network, as well as the performance metrics for the sinusoidal signals detection. The paper provides an assessment of the overall accuracy for the binary signals. It reports the overall accuracy value of 0.93 for the sinusoidal signal detection in the presence of additive white Gaussian noise at the signal-to-noise ratio value of −20 dB for a balanced dataset.

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Keywords

modulation, digital communications, machine learning, manipulation keying, JT65, detection, deep learning, convolutional neural network, demodulation, bit error rate

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