IDR Logo

Please use this identifier to cite or link to this item: http://idr.iitbbs.ac.in/jspui/handle/2008/1315
Title: Cyclostationary Features Based Modulation Classification in Presence of Non Gaussian Noise Using Sparse Signal Decomposition
Authors: Satija U.
Mohanty M.
Ramkumar B.
Keywords: Automatic modulation classification
Cognitive radio
Cyclostationary
Sparse signal decomposition
Issue Date: 2017
Citation: 3
Abstract: Automatic modulation classification�(AMC) is a salient component in the area of cognitive radio, signal detection, interference identification, electronic warfare, spectrum management and surveillance. The majority of the existing signals detection and classification methods presume that the received signal is corrupted by additive white Gaussian noise. The performance of the modulation classification algorithms degrades severely under the non-Gaussian impulsive noise. Hence, in this paper, we introduce a robust algorithm to identify the modulation type of digital signal�contaminated with non-Gaussian impulse noise and additive white Gaussian noise (AWGN) using a sparse signal decomposition on hybrid dictionary. The algorithm first detects and removes the impulse noise using sparse signal decomposition thereafter it classifies the modulation schemes using cyclostationary feature extraction algorithm. Simulation results demonstrate the superiority of the proposed method under different non-Gaussian impulse noise and AWGN conditions. The performance of the proposed classifier is evaluated using well known classifiers available in the literature. � 2017, Springer Science+Business Media New York.
URI: http://dx.doi.org/10.1007/s11277-017-4444-4
http://10.10.32.48:8080/jspui/handle/2008/1315
Appears in Collections:Research Publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.