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Engin Avci - One of the best experts on this subject based on the ideXlab platform.

  • selecting of the optimal feature subset and kernel parameters in Digital Modulation classification by using hybrid genetic algorithm support vector machines hgasvm
    Expert Systems With Applications, 2009
    Co-Authors: Engin Avci
    Abstract:

    The support vector machines is a new technique for many pattern recognition areas. The Digital Modulation classification is one of these pattern recognition areas. In SVM training, the kernels, kernel parameters, and feature selection have very important roles for SVM classification accuracy. Therefore, most appropriates of these kernel types, kernel parameters and features should be used for SVM training. In this study, a hybrid of genetic algorithm-support vector machines (HGASVM) approach is presented in Digital Modulation classification area for increasing the support vector machines (SVM) classification accuracy. This HGASVM approach proposed in this paper selects of the optimal kernel function type, kernel function parameter, most appropriate wavelet filter type for problem, wavelet entropy parameter, and soft margin constant C penalty parameter of support vector machines (SVM) classifier. The classification accuracy of this HGASVM approach is tried by using real Digital Modulation dataset and compared with the SVMs, which has kernel function type, kernel function parameter, wavelet filter type, wavelet entropy parameter, and C parameter are randomly selected. Here, discrete wavelet transform (DWT) and adaptive wavelet entropy are used in feature extraction stage of this HGASVM approach. The Digital Modulation types used in this study are ASK-2, ASK-4, ASK-8, FSK-2, FSK-4, FSK-8, PSK-2, PSK-4, and PSK-8. The experimental studies conducted in this study show that the classification accuracy of this HGASVM approach is more superior than SVM, which has constant parameters.

  • the performance comparison of discrete wavelet neural network and discrete wavelet adaptive network based fuzzy inference system for Digital Modulation recognition
    Expert Systems With Applications, 2008
    Co-Authors: Engin Avci, Derya Avci
    Abstract:

    In this paper, a new discrete wavelet neural network (DWNN) and discrete wavelet adaptive network based fuzzy inference system (DWANFIS) methods are offered for automatic Digital Modulation recognition (ADMR) and the performance comparison between these new DWNN and DWANFIS intelligent systems are performed by using bior1.3, bior2.2, bior2.8, bior3.5, bior6.8, coif1, coif2, coif3, coif4, coif5, db3, db5, db8, db10, sym2, sym3, sym5, sym7, and sym8 wavelet decomposition filters, respectively. Moreover in this study, discrete wavelet transform (DWT) and adaptive wavelet entropy are used in feature extraction stages of these intelligent systems. The Digital Modulation types used in this study are ASK2, ASK4, ASK8, FSK2, FSK4, FSK8, PSK2, PSK4, and PSK8. Here, mean correct recognition rates for Digital Modulation recognition were obtained 96.51% and 90.24% by using DWNN and DWANFIS intelligent systems, respectively.

Asoke K Nandi - One of the best experts on this subject based on the ideXlab platform.

  • automatic Digital Modulation recognition using artificial neural network and genetic algorithm
    Signal Processing, 2004
    Co-Authors: M L D Wong, Asoke K Nandi
    Abstract:

    Automatic recognition of Digital Modulation signals has seen increasing demand nowadays. The use of artificial neural networks for this purpose has been popular since the late 1990s. Here, we include a variety of Modulation types for recognition, e.g. QAM16, V29, V32, QAM64 through the addition of a newly proposed statistical feature set. Two training algorithms for multi-layer perceptron (MLP) recogniser, namely Backpropagation with Momentum and Adaptive Learning Rate is investigated, while resilient backpropagation (RPROP) is proposed for this problem, are employed in this work. In particular, the RPROP algorithm is applied for the first time in this area. In conjunction with these algorithms, we use a separate data set as validation set during training cycle to improve generalisation. Genetic algorithm (GA) based feature selection is used to select the best feature subset from the combined statistical and spectral feature set. RPROP MLP recogniser achieves about 99% recognition performance on most SNR values with only six features selected using GA.

  • Recognition of Digital Modulations
    Automatic Modulation Recognition of Communication Signals, 1996
    Co-Authors: Elsayed Elsayed Azzouz, Asoke K Nandi
    Abstract:

    In modern communication systems, Digital Modulation techniques rather than analogue ones are frequently used. So, the new trend is the Digital Modulation recognisers. Most of the Digital Modulation recognisers discussed in Chapter 1 utilise the pattern recognition approach such as [4], and [14]. So, they require long signal duration and the processing time may be very long; and this leads to the use of these algorithms in off-line analysis. Furthermore, some of these recognisers such as [4] require excessive computer storage to ensure correct Modulation recognition. Indeed, most of them are assigned to a subset of Modulation types of interest. Also, the practical implementation for some of these recognisers such as [14], [21], and [22] is excessively complex. However, the work on some of these recognisers attempts to identify Digital Modulations with number of levels > 4. On the other hand, the number of samples used in the algorithms presented in this thesis to decide about the Modulation type is 2048 (equivalent to 1.707 msec.) and this is likely to be suitable for on-line analysis.

  • automatic identification of Digital Modulation types
    Signal Processing, 1995
    Co-Authors: Elsayed Elsayed Azzouz, Asoke K Nandi
    Abstract:

    Abstract In both covert and overt operations, Modulation identification plays an important role. In communication intelligence (COMINT) applications the main objective is the perfect monitoring of the intercepted signals and one of the parameters that affect the perfect monitoring is the Modulation type of the intercepted signal. In this paper, a set of decision criteria for identifying different types of Digital Modulation is developed. Also, all the key features used in the identification algorithm are calculated using the conventional signal processing methods. Computer simulations for different types of band-limited Digitally modulated signals corrupted by band-limited Gaussian noise have been carried out. Expressions for the instantaneous amplitude, and phase of different types of Digitally modulated signals are derived. Also, two software solutions for estimating the instantaneous phase in the weak segments of a signal are introduced and analyzed. Finally, it is found that all Modulation types of interest have been classified with success rate ≥90% at SNR = 10 dB.

Derya Avci - One of the best experts on this subject based on the ideXlab platform.

  • the performance comparison of discrete wavelet neural network and discrete wavelet adaptive network based fuzzy inference system for Digital Modulation recognition
    Expert Systems With Applications, 2008
    Co-Authors: Engin Avci, Derya Avci
    Abstract:

    In this paper, a new discrete wavelet neural network (DWNN) and discrete wavelet adaptive network based fuzzy inference system (DWANFIS) methods are offered for automatic Digital Modulation recognition (ADMR) and the performance comparison between these new DWNN and DWANFIS intelligent systems are performed by using bior1.3, bior2.2, bior2.8, bior3.5, bior6.8, coif1, coif2, coif3, coif4, coif5, db3, db5, db8, db10, sym2, sym3, sym5, sym7, and sym8 wavelet decomposition filters, respectively. Moreover in this study, discrete wavelet transform (DWT) and adaptive wavelet entropy are used in feature extraction stages of these intelligent systems. The Digital Modulation types used in this study are ASK2, ASK4, ASK8, FSK2, FSK4, FSK8, PSK2, PSK4, and PSK8. Here, mean correct recognition rates for Digital Modulation recognition were obtained 96.51% and 90.24% by using DWNN and DWANFIS intelligent systems, respectively.

Fares S. Almehmadi - One of the best experts on this subject based on the ideXlab platform.

  • The effects of different noise types and mobility on error rate of Digital Modulation schemes over millimeter-wave Weibull fading channels
    Wireless Networks, 2018
    Co-Authors: Osamah S. Badarneh, Fares S. Almehmadi
    Abstract:

    Millimeter (mm-wave) communication is a prominent candidate to support the evolution towards fifth generation (5G) wireless systems. As such, in this paper, we study the impact of different noise type and mobility on the performance of coherent binary Digital Modulation schemes in mm-wave Weibull fading channels. To this end, exact and new closed-form expressions are derived for the bit error rate of coherent Digital Modulation schemes in millimeter wave Weibull fading channels in the presence of additive non-Gaussian noise. In addition, new and exact closed-form expressions are obtained for the symbol error rate of square M-ary quadrature amplitude Modulation scheme (M-AQM). The derived expressions take into consideration the mobility of the wireless receives. Besides, they are valid for integer and non-integer values of the fading and noise shaping parameters. Analytical results are supported by Monte-Carlo simulations to validate the accuracy of the obtained results.

Elsayed Elsayed Azzouz - One of the best experts on this subject based on the ideXlab platform.

  • Recognition of Digital Modulations
    Automatic Modulation Recognition of Communication Signals, 1996
    Co-Authors: Elsayed Elsayed Azzouz, Asoke K Nandi
    Abstract:

    In modern communication systems, Digital Modulation techniques rather than analogue ones are frequently used. So, the new trend is the Digital Modulation recognisers. Most of the Digital Modulation recognisers discussed in Chapter 1 utilise the pattern recognition approach such as [4], and [14]. So, they require long signal duration and the processing time may be very long; and this leads to the use of these algorithms in off-line analysis. Furthermore, some of these recognisers such as [4] require excessive computer storage to ensure correct Modulation recognition. Indeed, most of them are assigned to a subset of Modulation types of interest. Also, the practical implementation for some of these recognisers such as [14], [21], and [22] is excessively complex. However, the work on some of these recognisers attempts to identify Digital Modulations with number of levels > 4. On the other hand, the number of samples used in the algorithms presented in this thesis to decide about the Modulation type is 2048 (equivalent to 1.707 msec.) and this is likely to be suitable for on-line analysis.

  • automatic identification of Digital Modulation types
    Signal Processing, 1995
    Co-Authors: Elsayed Elsayed Azzouz, Asoke K Nandi
    Abstract:

    Abstract In both covert and overt operations, Modulation identification plays an important role. In communication intelligence (COMINT) applications the main objective is the perfect monitoring of the intercepted signals and one of the parameters that affect the perfect monitoring is the Modulation type of the intercepted signal. In this paper, a set of decision criteria for identifying different types of Digital Modulation is developed. Also, all the key features used in the identification algorithm are calculated using the conventional signal processing methods. Computer simulations for different types of band-limited Digitally modulated signals corrupted by band-limited Gaussian noise have been carried out. Expressions for the instantaneous amplitude, and phase of different types of Digitally modulated signals are derived. Also, two software solutions for estimating the instantaneous phase in the weak segments of a signal are introduced and analyzed. Finally, it is found that all Modulation types of interest have been classified with success rate ≥90% at SNR = 10 dB.