Hussam Mustafa

Performance of the instantaneous frequency based classifier distinguishing BFSK from QAM and PSK modulations for asynchronous sampling and slow and fast fading

In this paper we propose a feature to distinguish frequency from amplitude-phase digital modulations. We compare the performance of the feature where every symbol is sampled more than once to that where every symbol is sampled only once. The feature is based on the product of two consecutive signal values and on time averaging of the imaginary part of the product if a symbol is sampled more than once. First, the conditional probability density functions of the feature given the present modulation are determined. The central limit theorem for strictly stationary m-dependent sequences is used to obtain Gaussian approximations. Then thresholds are determined based on the minimization of the total probability of misclassification. Following that, effects of fast and slow fading, and of the symbol period and delay being non-integer multiples of sampling period on the performance are studied. In the course of doing that, the proposed classifier is compared to the maximum likelihood classifier and the wavelet based classifier using support vector machine.

Automatic digital modulation classification using instantaneous features

In this paper, we propose a simple, effective and robust method based on the statistical moments of instantaneous features to classify digital modulation signals. This method adopts a tree structure scheme and uses different features in each branch to make full use of distinguishing modulation types characteristics. The proposed method is capable of differentiating ASK2, ASK4, FSK2, FSK4, PSK2 and PSK4 signals at the output of typical high frequency channel with white Gaussian noise, multi-path delay and Doppler shift. Unlike most other existing methods, our method assumes no prior information of the incoming signal (symbol rate, carrier frequency, amplitude etc.). Extensive simulation results demonstrate that this approach is robust in various practical situations.

Automatic radio station detection by clustering power spectrum components

In this paper, we propose a simple, effective and robust method based on the statistical moments of instantaneous features to classify digital modulation signals. This method adopts a tree structure scheme and uses different features in each branch to make full use of distinguishing modulation types characteristics. The proposed method is capable of differentiating ASK2, ASK4, FSK2, FSK4, PSK2 and PSK4 signals at the output of typical high frequency channel with white Gaussian noise, multi-path delay and Doppler shift. Unlike most other existing methods, our method assumes no prior information of the incoming signal (symbol rate, carrier frequency, amplitude etc.). Extensive simulation results demonstrate that this approach is robust in various practical situations.This paper describes three algorithms for automatic radio station detection based on the shape of power spectrum. The objective is to find the number of active stations and for each of them to estimate the bandwidth and carrier frequency. All three algorithms are grouping spectrum components into clusters that are assumed to correspond to active radio stations. The first algorithm finds local centers of mass and puts cluster boundaries in the middle between the neighboring centers of mass. The second algorithm determines the noise level and defines a threshold based on the level. Clusters are found based on the spectrum above the threshold. The third algorithm uses adaptive multiple thresholds for separating spectrum components in height and distance. Clustering is done at different height levels. The performances of algorithms are compared it the case of two active stations whose carrier frequencies and relative powers are arbitrarily chosen.

Effects of Carrier Offset on the Classification of Binary Frequency Shift Keying Based on the Product of Two Consecutive Signal Values

In this paper we propose a feature to distinguish frequency shift keying modulation from amplitude shift keying and phase shift keying modulations in the presence of carrier offset. The feature is based on the product of two consecutive signal values and on time averaging of the imaginary part of the product. First, the conditional probability density functions of the feature given the present modulation are determined. In order to overcome the complexity of calculating the probability density functions, the central limit theorem for strictly stationary m-dependent sequences is used to obtain Gaussian approximations. The classification threshold is based on the minimization of the total probability of misclassification. Finally the paper studies effects of carrier offset on the performance in the case of known and unknown carrier offset.

Effects of symbol rate on the classification of digital modulation signals

The paper considers features based on the multiplication of two consecutive signal values. Furthermore, three new classifiers using the features are proposed: fixed threshold tree classifier, dynamic threshold tree classifier and support vector machine (SVM) classifier. It is shown that the multiplication produces dependence of the features on the symbol rate. In order to quantify the effects of this dependence, the paper studies the performance of the newly proposed classifiers as well as the maximum likelihood (ML) classifier (Wei, W., 1998; Wei and Mendel, J.M., 2000), the qLLR classifier (Polydoros, A. and Kim, K., 1990), and the cumulants based classifier (Swami, A. and Sadler, B.M., 2000). Simulations show that the SVM classifier has promising results in the sense that it is closest to the theoretically optimal results obtained by the ML classifier.

Phonocardiography nonlinear multiple measurements in discovering the abnormalities in the bioprosthetic heart valves

This paper describes the benefit for potentially rectifying the multiple nonlinear Phonocardiography measurements so that we can apply the linear independent component analysis (ICA) to separate blindly the sources of heart valve murmuring in order to find a noninvasive way to discover abnormalities in the Bioprosthetic heart valves. At the beginning the paper will discuss a new design of measurement of the PCG signal based on the (ICA). The second part of the paper will show a comparison between the classification using the classical Fourier transform as a source of features and using the ICA sources of the same features. The last part of this paper will discuss various ways to overcome the nonlinear square detect law, and the lack of multiple independent readings, which is essential in the implementation of the ICA algorithm.

Expansion of cumulant‐based classifier to frequency shift keying modulations and to the use of support vector machines

This paper proposes an expansion of the cumulant-based classifier of digital modulations to frequency shift keying (FSK) modulations. Cumulant estimates are calculated when the FSK modulation is present. The features obtained from the cumulant estimators are used in a support vector machine (SVM) classifier. The performance of the SVM classifier is compared to other classifiers. Among these other classifiers is the cumulant-based tree classifier which uses thresholds defined by the asymptotic values of the cumulant estimators. The simulation results show that using SVM classifier improves the performance. Copyright

Effect of Carrier Offset on the Classification of Phase Shift Keying Modulation using the Subtraction of Two Consecutive Signal Values

In this paper we study the effect of carrier offset on a feature distinguishing binary phase shift keying and quadrature phase shift keying. The feature is based on the subtraction of two consecutive signal values and the kurtosis of the absolute value of this subtraction. The paper compares performance of the proposed feature to a cumulant based feature when both are used in a support vector machine classifier. Also the paper compares the proposed-feature based classification to the maximum likelihood classification and to the quasi-log-likelihood ratio classifier. The simulation results showed that the proposed feature classifier has a robust performance with respect to carrier offset compared to the other above mentioned classifiers.

Effect of Carrier offset on the Classification of Digital Modulation Signals Based on the Product of Two Consecutive Signal Values

In this paper we study the effect of carrier offset on a feature distinguishing different digital modulation signals. The feature is based on the multiplication of two consecutive signal values and the kurtosis of this multiplication. The feature distinguishes amplitude shift keying with two and four levels (ASK2 and ASK4), phase shift keying (PSK) and 16 signal constellation points quadrature amplitude modulation (QAM16). The paper compares the proposed feature to a cumulant based feature when both are used in a support vector machine classifier. Also the paper compares the proposed-feature based classification to the maximum likelihood classification.