AUTOMATIC MODULATION CLASSIFICATION USING FEATURE BASED APPROACH

Abstract

Automatic Modulation Classification (AMC) is a scheme to classify the modulated signal by observing its received signal features. The received signal is usually corrupted by influence of various sources, such as, white guassian noise and fading, which degrades the signal quality. Automatic modulation classification plays an important role in cognitive radio communication. Due to amassed usage of digital signals in different technologies, such as, cognitive radios, scientists have focused on recognizing these signal types. AMC is expected to be incorporated in the upcoming cognitive communication. Generally, digital signal type classification can be categorized into two major categories: decision theoretic (DT) methods and pattern classification (PC) methods. In this research we focused on PC methods which are based upon features extraction. The feature extraction based modulation classification is accomplished in two modules. The first module is the feature extraction and second is classification process which gives decision based upon the features extracted. The features extracted from the received signal are higher order moments, higher order cummulants, spectral features, cyclo-stationary features and novel Gabor features. The classification of digital modulation formats such as pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM) and phase shift keying (PSK) and frequency shift keying (FSK) are considered throughout the research. The performance of proposed classifier are analyzed on additive white guassian noise channel (AWGN), Rayleigh flat fading channel, Rician flat fading channel and log normal fading channel. The proposed classifier algorithm for classification of different unknown modulated signals is based on normalized higher even order cummulants features and spectral features. The proposed classifiers are based on likelihood function, vi multilayer perceptron and linear discriminant analysis. The simulation results show that the proposed algorithms have high classification accuracy even at low signal to noise ratio (SNR). The proposed classifier algorithms perform efficiently as compared to the existing classifiers. A novel joint feature extraction and classification technique is proposed to classify the digital modulated signals by adaptively tuning the parameters of Gabor filter network. The Gabor atom parameters are tuned using delta rule and weights of the Gabor filter using least mean square (LMS) algorithm. The proposed algorithm classifies efficiently the PSK, FSK and QAM signals with 100% classification. The Modified gabor filter network is proposed for classification of M-PAM signals. The proposed HMM and Gabor filter network formulates an optimal classifier structure. The proposed classifier use Baum-Welch algorithm and Genetic algorithm (GA) to update the Gabor filter network and hidden markov model (HMM) parameters. The fitness function for the genetic algorithm is probability of observation sequence given the model. The objective is to maximize the probability of observation sequence. To improve the classification accuracy, three parameters of Gabor filters (GFs) network and one HMM parameter are adjusted simultaneously such that the probability of observation sequence is maximized. The proposed classifiers are compared with well-known techniques in the literature and simulation results show the supremacy of the proposed schemes over the contemporary techniques.