000			02548nam a22001817a 4500
082			_a621.38 _bS568A
100			_aSiddha, Priyanka _926073
245			_aAutomatic Modulation Classification using Wavelet Transform _cby Priyanka Siddha
260			_aIIT Jodhpur _bDepartment of Electrical Engineering _c2017
300			_aix,46p. _bHB
520			_a"Automatic Modulation Classification (AMC) is a process which classifies the modulation scheme of a received signal. It is followed by demodulation process for accurately retrieving the information present in the received signal. A prominent application of AMC is in military intelligence to decode a signal of interest. Also, an energy efficient jamming signal can be designed after knowing the specific modulation scheme. In civil applications, AMC is used in software defined radio and in cognitive radio to design single receiver for different type of transmitters to select appropriate demodulation approach. Generally, there are two groups of algorithms for classification, one is Likelihood Based (LB) Algorithms and second is Feature Based (FB) Algorithms. LB algorithms give optimal solutions but have high computational complexity, whereas FB algorithms are of reduced complexity but give a less optimal solution. With new techniques, FB algorithms can give near optimal solution for properly chosen features. Features used in FB algorithms are Instantaneous features like amplitude, frequency, phase; Transform based features such as Fourier and Wavelet transform; Higher order moment, cumulants based and Cyclostationarity based. In the proposed work, analytic Morlet wavelet is used as mother wavelet to extract features presented in [Sethi and Ray, 2013] which were originally extracted from the baseband signal. These features are further used as the input to different machine learning classifiers viz. Support Vector Machine, KNN and Bagged Tree, and results are compared to the baseline model in which Haar wavelet was used to extract four features from four type of signals to get a set of sixteen features. The same set of sixteen features is extracted from Daubechies mother wavelet. Average accuracy obtained for Morlet wavelet is 96.48% Keywords: AMC, Wavelet Transform, Morlet, SVM, KNN, Bagged Tree i"
650			_aAutomatic Modulation Classification _926074
650			_a Wavelet Transform _926075
650			_aMTech Theses _926076
650			_aDepartment of Electrical Engineering _926077
700			_aYadav, Sandeep Kumar _926078
942			_cTH
999			_c14683 _d14683