Classification of bandlimited fsk4 and fsk8 signals

This paper compares two types of classifiers applied to bandlimited FSK4 and FSK8 signals. The first classifier employs the decision-theoretic approach and the second classifier is a neural network structure. Key features are extracted using a zero crossing sampler. A novel decision tree is proposed and optimum threshold values are found for the decision theoretic approach. For the neural network, the optimum structure is found to be the smallest structure to give 100% overall success rate. The performance of the both classifiers has been evaluated by simulating bandlimited FSK4 and FSK8 signals corrupted by Gaussian noise. It is shown that the neural network outperforms the decision-theoretic approach particularly for SN

Automatic Classification of Digital Modulations

Tato disertační práce pojednává o nové metodě rozpoznávání digitálních modulací. V úvodní části je shrnut dosavadní vývoj a současný stav problematiky. Jsou popsány stávající metody spolu s jejich charakteristickými vlastnostmi. Větší pozornost je věnována využití umělých neuronových sítí. Po vytyčení cílů disertační práce jsou teoreticky popsány digitální modulace, které byly vybrány pro rozpoznávání. Jedná se o modulace FSK, MSK, BPSK, QPSK a QAM-16, které jsou nejčastěji používány v moderních komunikačních systémech. Navržená metoda je založena na analýze modulových a fázových spektrogramů modulovaných signálů. Pro posouzení vlastností spektrogramů jsou využívány jejich histogramy. Ty dávají informaci o počtu nosných frekvencí v signálu, což slouží pro rozpoznání FSK a MSK, a o počtu fázových stavů, podle čehož se určují modulace BPSK, QPSK a QAM-16. Spektrogramy, ve kterých jsou viditelné charakteristické příznaky modulací, jsou získávány při délce segmentu rovné délce symbolu. Bylo zjištěno, že při známé délce symbolu je možné správně rozpoznat modulaci při poměru signál-šum minimálně 0 dB. Proto je třeba před výpočtem spektrogramů detekovat délku symbolu. K tomuto účelu byly navrženy čtyři metody: autokorelační funkce, kepstrální analýza, waveletová transformace a LPC koeficienty. Tyto metody byly algoritmizovány a analyzovány se signály zarušenými bílým Gaussovským šumem, fázovým šumem a se signály po průchodu přenosovým kanálem s odrazy a úniky. Jako nejvhodnější a nejspolehlivější se ukázala metoda detekce pomocí kepstrální analýzy. Nakonec byla nová metoda rozpoznávání modulací ověřována se signály prošlé přenosovým kanálem, jehož vlastnosti se blíží reálnému kanálu.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.

Modulation classification of digital communication signals

Modulation classification of digital communications signals plays an important role in both military and civilian sectors. It has the potential of replacing several receivers with one universal receiver. An automatic modulation classifier can be defined as a system that automatically identifies the modulation type of the received signal given that the signal exists and its parameters lie in a known range. This thesis addresses the need for a universal modulation classifier capable of classifying a comprehensive list of digital modulation schemes. Two classification approaches are presented: a decision-theoretic (DT) approach and a neural network (NN) approach. First classifiers are introduced that can classify ASK, PSK, and FSK signals. A decision tree is designed for the DT approach and a NN structure is formulated und trained to classify these signals. Both classifiers use the same key features derived from the intercepted signal. These features are based on the instantaneous amplitude, instantaneous phase, and instantaneous frequency of the intercepted signal, and the cumulates of its complex envelope. Threshold values for the DT approach are found from the minimum total error probabilities of the extracted key features at SNR of 20 to -5dB. The NN parameters are found by training the networks on the same data. The DT and NN classifiers are expanded to include CPM signals. Signals within the CPM class are also added to the classifiers and a separate decision tree and new NN structure are found far these signals. New key features to classify these signals are also introduced. The classifiers are then expanded further to include multiple access signals, followed by QAM, PSK8 and FSK8 signals. New features arc found to classify these signals. The final decision tree is able to accommodate a total of fifteen different modulation types. The NN structure is designed in a hierarchical fashion to optimise the classification performance of these fifteen digital modulation schemes. Both DT and NN classifiers are able to classify signals with more than 90% accuracy in the presence of additive white Gaussian within SNR ranging from 20 to 5dB. However, the performance of the NN classifier appears to be more robust as it degrades gradually at the SNRs of 0 and -5dB. At -5dB, the NN has an overall accuracy of 73.58%, whereas the DT classifier achieves only 47.3% accuracy. The overall accuracy of the NN classifier, over the combined SNR range of 20 to -5dB, is 90.7% compared to 84.56% for the DT classifier. Finally, the performances of these classifiers are tested in the presence of Rayleigh fading. The DT and NN classifier structures are modified to accommodate fading and again, new key features are introduced to accomplish this. With the modifications, the overall accuracy of the NN classifier, over the combined SNR range of 20 to -5dB and 120Hz Doppler shift, is 87.34% compared to 80.52% for the DT classifier