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Automatic Classification of Digital Modulations
Kubánková, Anna ; Novotný, Vít (advisor)
This dissertation thesis deals with a new method for digital modulation recognition. The history and present state of the topic is summarized in the introduction. Present methods together with their characteristic properties are described. The recognition by means of artificial neural is presented in more detail. After setting the objective of the dissertation thesis, the digital modulations that were chosen for recognition are described theoretically. The modulations FSK, MSK, BPSK, QPSK, and QAM-16 are concerned. These modulations are mostly used in modern communication systems. The method designed is based on the analysis of module and phase spectrograms of the modulated signals. Their histograms are used for the examination of the spectrogram properties. They provide information on the count of carrier frequencies in the signal, which is used for the FSK and MSK recognition, and on the count of phase states on which the BPSK, QPSK, and QAM-16 are classified. The spectrograms in that the characteristic attributes of the modulations are visible are obtained with the segment length equal to the symbol length. It was found that it is possible to correctly recognize the modulation with the known symbol length at the signal-to-noise ratio at least 0 dB. That is why it is necessary to detect the symbol length prior to the spectrogram calculation. Four methods were designed for this purpose: autocorrelation function, cepstrum analysis, wavelet transform, and LPC coefficients. These methods were algorithmized and analyzed with signals disturbed by the white Gaussian noise, phase noise and with signals passed through a multipass fading channel. The method of detection by means of cepstrum analysis proved the most suitable and reliable. Finally the new method for digital modulation recognition was verified with signals passed through a channel with properties close to the real one.
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Automatic classification of digital modulations using neural networks
Sinyanskiy, Alexander ; Uher, Václav (referee) ; Kubánková, Anna (advisor)
This master’s thesis is about automatic digital modulation recognition using artificial neural networks. The paper briefly describes the issue and existing algorithms for solving the problem of modulation recognition. It was found that the best results are achieved when using the feature-recognition methods and artificial neural networks. The digital modulations that were chosen for recognition are described theoretically and they are ASK, FSK, BPSK, QPSK and 16QAM. These modulations are most commonly used today. Later was briefly described theory of neural networks. In another part was given to the characteristic features of modulation for modulation recognition using artificial neural networks. The penultimate part describes the parameters for signal simulation in Matlab, how to create the key features in Matlab and results after experimental simulation. The last part contains neural network optimization experiments.
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Automatic classification of digital modulations using neural networks
Sinyanskiy, Alexander ; Uher, Václav (referee) ; Kubánková, Anna (advisor)
This master’s thesis is about automatic digital modulation recognition using artificial neural networks. The paper briefly describes the issue and existing algorithms for solving the problem of modulation recognition. It was found that the best results are achieved when using the feature-recognition methods and artificial neural networks. The digital modulations that were chosen for recognition are described theoretically and they are ASK, FSK, BPSK, QPSK and 16QAM. These modulations are most commonly used today. Later was briefly described theory of neural networks. In another part was given to the characteristic features of modulation for modulation recognition using artificial neural networks. The penultimate part describes the parameters for signal simulation in Matlab, how to create the key features in Matlab and results after experimental simulation. The last part contains neural network optimization experiments.
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Automatic Classification of Digital Modulations
Kubánková, Anna ; Novotný, Vít (advisor)
This dissertation thesis deals with a new method for digital modulation recognition. The history and present state of the topic is summarized in the introduction. Present methods together with their characteristic properties are described. The recognition by means of artificial neural is presented in more detail. After setting the objective of the dissertation thesis, the digital modulations that were chosen for recognition are described theoretically. The modulations FSK, MSK, BPSK, QPSK, and QAM-16 are concerned. These modulations are mostly used in modern communication systems. The method designed is based on the analysis of module and phase spectrograms of the modulated signals. Their histograms are used for the examination of the spectrogram properties. They provide information on the count of carrier frequencies in the signal, which is used for the FSK and MSK recognition, and on the count of phase states on which the BPSK, QPSK, and QAM-16 are classified. The spectrograms in that the characteristic attributes of the modulations are visible are obtained with the segment length equal to the symbol length. It was found that it is possible to correctly recognize the modulation with the known symbol length at the signal-to-noise ratio at least 0 dB. That is why it is necessary to detect the symbol length prior to the spectrogram calculation. Four methods were designed for this purpose: autocorrelation function, cepstrum analysis, wavelet transform, and LPC coefficients. These methods were algorithmized and analyzed with signals disturbed by the white Gaussian noise, phase noise and with signals passed through a multipass fading channel. The method of detection by means of cepstrum analysis proved the most suitable and reliable. Finally the new method for digital modulation recognition was verified with signals passed through a channel with properties close to the real one.
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