Classification of modulation 17/09/2018 Sedlacek Marek

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 17/09/2018 Sedlacek Marek

Content Why is analysis of modulated signal useful? Standard methods for analysis of modulated signals Modern approaches to signal analysis Wigner’s spectrums. Conclusion 17/09/2018 Sedlacek Marek

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. 17/09/2018 Sedlacek Marek

Standard methods of analysis of modulated signals Utilize probabilistic models based on stationary statistics. Spectral analysis – Identification of frequency spectrum or spectrum of power spectral 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.) 17/09/2018 Sedlacek Marek

Experimental results: All of these pictures I created in MATLAB 7. 17/09/2018 Sedlacek Marek

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 17/09/2018 Sedlacek Marek

Spectrum of FM modulation with index 1 is similar to AM spectrum. From running of phase we can see it isn’t AM signal. 17/09/2018 Sedlacek Marek

To classify SSB,VSB signals we can exploit that energy in one sideband is lower than upper band. 17/09/2018 Sedlacek Marek

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. 17/09/2018 Sedlacek Marek

17/09/2018 Sedlacek Marek

Wigner’s spectrum Wigner’s spectrum is showing how frequency is varying in time. This picture is WS of ASK signal 17/09/2018 Sedlacek Marek

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 17/09/2018 Sedlacek Marek

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. 17/09/2018 Sedlacek Marek

Thanks for your attention ! 17/09/2018 Sedlacek Marek