Automatic Modulation Classification using Wavelet Transform (Record no. 14683)
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000 -LEADER | |
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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 |
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 |
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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 |