Automatic Modulation Classification using Wavelet Transform (Record no. 14683)

MARC details
000 -LEADER
fixed length control field 02548nam a22001817a 4500
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621.38
Item number S568A
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Siddha, Priyanka
245 ## - TITLE STATEMENT
Title Automatic Modulation Classification using Wavelet Transform
Statement of responsibility, etc by Priyanka Siddha
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication IIT Jodhpur
Name of publisher Department of Electrical Engineering
Year of publication 2017
300 ## - PHYSICAL DESCRIPTION
Number of Pages ix,46p.
Other physical details HB
520 ## - SUMMARY, ETC.
Summary, etc "Automatic Modulation Classification (AMC) is a process which classifies the modulation scheme of a<br/>received signal. It is followed by demodulation process for accurately retrieving the information present<br/>in the received signal. A prominent application of AMC is in military intelligence to decode a signal of<br/>interest. Also, an energy efficient jamming signal can be designed after knowing the specific modulation<br/>scheme. In civil applications, AMC is used in software defined radio and in cognitive radio to design<br/>single receiver for different type of transmitters to select appropriate demodulation approach.<br/>Generally, there are two groups of algorithms for classification, one is Likelihood Based (LB) Algorithms<br/>and second is Feature Based (FB) Algorithms. LB algorithms give optimal solutions but have<br/>high computational complexity, whereas FB algorithms are of reduced complexity but give a less optimal<br/>solution. With new techniques, FB algorithms can give near optimal solution for properly chosen<br/>features. Features used in FB algorithms are Instantaneous features like amplitude, frequency, phase;<br/>Transform based features such as Fourier and Wavelet transform; Higher order moment, cumulants<br/>based and Cyclostationarity based.<br/>In the proposed work, analytic Morlet wavelet is used as mother wavelet to extract features<br/>presented in [Sethi and Ray, 2013] which were originally extracted from the baseband signal. These features<br/>are further used as the input to different machine learning classifiers viz. Support Vector Machine,<br/>KNN and Bagged Tree, and results are compared to the baseline model in which Haar wavelet was used<br/>to extract four features from four type of signals to get a set of sixteen features. The same set of sixteen<br/>features is extracted from Daubechies mother wavelet. Average accuracy obtained for Morlet wavelet<br/>is 96.48%<br/>Keywords: AMC, Wavelet Transform, Morlet, SVM, KNN, Bagged Tree<br/>i"<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical Term Automatic Modulation Classification
Topical Term Wavelet Transform
Topical Term MTech Theses
Topical Term Department of Electrical Engineering
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Yadav, Sandeep Kumar
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis
Holdings
Withdrawn status Lost status Damaged status Not for loan Collection code Permanent Location Current Location Shelving location Date acquired Full call number Accession Number Price effective from Koha item type
      Not For Loan Reference S. R. Ranganathan Learning Hub S. R. Ranganathan Learning Hub Course Reserve 2024-01-18 621.38 S568A TM00106 2024-01-18 Thesis