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Classification of modulation

Classification of modulation. About me. My name: Marek SEDLACEK Study at: University of defence, CZ Faculty of : Military technology. Branch of study: Communication and information systems. Year of study: 3 rd

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Classification of modulation

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  1. Classification of modulation Sedlacek Marek

  2. About me • My name: Marek SEDLACEK • Study at: University of defence, CZ • Faculty of : Military technology. • Branch of study: Communication and information systems. • Year of study: 3rd • I’m engaged in classification of modulation within student’s scientific activity Sedlacek Marek

  3. Content • Why is analysisof modulated signal useful? • Standard methods for analysis of modulated signals • Modern approaches to signal analysis • Wigner’s spectrums. • Conclusion Sedlacek Marek

  4. Why is analysis of modulated signals useful? • If we know parameters of signal, we can use it in many cases to improve analyzed system (e.g.: analysis of radio channel in term of EMC or to locate source of jamming (both deliberate and unintended). • We can use signal analysis to demodulation of unknown signal. This is can be use for example in protecting against terrorism. Note:To classify signals we have to make analysis foremost. Sedlacek Marek

  5. Standardmethods of analysis of modulated signals • Utilize probabilistic models based on stationary statistics. • Spectral analysis – Identification of frequency spectrum or spectrum of powerspectral density. In most cases by using FFT or DFT. • Statistic analysis – Is engaged in identification basic statistical quantity (mean value, scatter, autocorrelation function, histogram etc.) Sedlacek Marek

  6. Experimental results: • All of these pictures I created in MATLAB 7. Sedlacek Marek

  7. AM has typical shape of spectrum. • But it is not unique character so we must apply other methods • AM signal should have linear running of unwrapped phase Sedlacek Marek

  8. Spectrum of FM modulation with index 1 is similar to AM spectrum. • From running of phase we can see it isn’t AM signal. Sedlacek Marek

  9. To classify SSB,VSB signals we can exploit that energy in one sideband is lower than upper band. Sedlacek Marek

  10. Modern approach • Utilize cyclostationarity to model random signals and substitute more approximate stationary models. • Cyclostationary models we can use when signals have undergone in periodic transformations (modulations, sampling etc.) • Cyclostationary signal is signal where his probabilistic parameters vary periodical in time. • Main property of wide-sense cyclostationarity is spectral correlation function (SCF). • Spectral correlation of noise is equal to null. Sedlacek Marek

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  12. Wigner’s spectrum • Wigner’s spectrum is showing how frequency is varying in time. • This picture is WS of ASK signal Sedlacek Marek

  13. Example of WS of 4-FSK Signal • There is 4-FSK signal. From this spectrum we can recognize when is signal transmitted on frequency f1, when on f2, f3 and f4 Sedlacek Marek

  14. Conclusion • To classify we can use a lot of methods but all of these only help to estimate type of modulation. (some with higher probability measure some with less) • To reliable result we have to utilize more than one of these methods. • All of these pictures and methods are easy to implement to GUI so can be „user friendly“. • Implementation in Matlab has advantage portability to other operating systems. Sedlacek Marek

  15. Thanks for your attention ! Sedlacek Marek

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