Automatic Modulation Classification for Cognitive Radios Using Cyclic Feature Detection

Ramkumar, Barathram

doi:10.1109/MCAS.2008.931739

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Automatic Modulation Classification for Cognitive Radios Using Cyclic Feature Detection

2009-01-01

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Ramkumar, Barathram *

DOI：10.1109/MCAS.2008.931739

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Abstract

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Cognitive radios have become a key research area in communications over the past few years. Automatic modulation classification (AMC) is an important component that improves the overall performance of the cognitive radio. Most modulated signals exhibit the property of cyclostationarity that can be exploited for the purpose of classification. In this paper, AMCs that are based on exploiting the cyclostationarity property of the modulated signals are discussed. Inherent advantages of using cyclostationarity based AMC are also addressed. When the cognitive radio is in a network, distributed sensing methods have the potential to increase the spectral sensing reliability, and decrease the probability of interference to existing radio systems. The use of cyclostationarity based methods for distributed signal detection and classification are presented. Examples are given to illustrate the concepts. The Matlab codes for some of the algorithms described in the paper are available for free download at http://filebox.vt.edu/user/bramkum.

Keywords:

SPECTRAL CORRELATION

SIGNAL INTERCEPTION

MAXIMUM-LIKELIHOOD

IDENTIFICATION

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