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     Research Journal of Applied Sciences, Engineering and Technology

    Abstract
2014(Vol.7, Issue:24)
Article Information:

Modulation Classification using Cyclostationary Features on Fading Channels

Sajjad Ahmed Ghauri, Ijaz Mansoor Qureshi, Ihtesham Shah and Nasir Khan
Corresponding Author:  Sajjad Ahmed Ghauri 
Submitted: April ‎15, ‎2014
Accepted: ‎May ‎09, ‎2014
Published: June 25, 2014
Abstract:
In this study Automatic Modulation Classification (AMC) which is based on cyclostationary property of the modulated signal are discussed and implemented for the purpose of classification. Modulation Classification (MC) is a technique used to make better the overall performance of cognitive radios. Recently Cognitive Radio (CR) plays a key role in the field of communication. CR also used in the development of different wireless application and the exploitation of civilian and military applications. In modulated signals there is cyclostationary property that can be used for the detection of modulation formats. The extraction of cyclostationary features, is used for classification of digital modulation schemes at different values of SNR’s, the considered modulation formats are FSK [2-64], PSK [2-64], PAM [2-64] and QAM [2-64] and the channel models considered are AWGN and Rayleigh flat fading. When the receiver, receives the signal it extract the cyclostationary features i-e Spectral Coherence Function (SCF) and Cyclic Domain Profile (CDP) and then uses a multilayer perception which is also known as Feed Forward Back Propagation Neural Network (FFBPNN) for classification of the modulation formats. The performance of proposed algorithm in the form of confusion matrix shows the correct classification accuracy of the considered modulation format. The simulation result shows the performance of proposed algorithm and feature extraction at lower SNR’s.

Key words:  Automatic Modulation Classification (AMCs), Cyclic Domain Profile (CDP), cyclostationary features, Cognitive Radio (CR), Feed Forward Back Propagation Neural Network (FFBPNN), Spectral Coherence Function (SCF),
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Cite this Reference:
Sajjad Ahmed Ghauri, Ijaz Mansoor Qureshi, Ihtesham Shah and Nasir Khan, . Modulation Classification using Cyclostationary Features on Fading Channels. Research Journal of Applied Sciences, Engineering and Technology, (24): 5331-5339.
ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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