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Automatic Modulation Identification Based on the Probability Density Function of Signal Phase.
- Source :
-
IEEE Transactions on Communications . Apr2012, Vol. 60 Issue 4, p1033-1044. 0p. - Publication Year :
- 2012
-
Abstract
- Automatic modulation recognition is advantageous for wireless communication systems employing adaptive modulation, software-defined radio, and cognitive radio. In this paper, we consider a phase based maximum likelihood (ML) approach for identifying the modulation format of a linearly modulated signal. Since the optimal ML scheme is computationally intensive, we propose two approximate ML alternatives, which can offer close-to-optimal performance with reduced complexity. We then present a general performance analysis for classification of K types of modulation constellations. For K<=5, probability of correct classification (Pcc) can be evaluated via simplified integration. In the case of K>5, we obtain a set of upper bounds on Pcc, which provide a tradeoff between accuracy and complexity in calculating the Pcc. In addition, asymptotic behavior of phase based ML classification schemes is investigated. [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 00906778
- Volume :
- 60
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Communications
- Publication Type :
- Academic Journal
- Accession number :
- 74305469
- Full Text :
- https://doi.org/10.1109/TCOMM.2012.021712.100638