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Title Automatic Modulation classification using genetic programming

Author(s) Asad Hussain

Abstract With the popularity of software defined radio and cognitive radio-based technologies in wireless communication, RF devices have to adapt to changing conditions and adjust its transmitting parameters such as transmitting power, operating frequency and modulation scheme. Thus, Automatic Modulation Classification (AMC) becomes an essential feature for such scenarios, where receiver has a little or no knowledge about the transmitter. This research explores the use of iterative techniques such as Genetic Programming (GP) for classification of digital modulated signals. K-nearest neighbor (KNN) has been used to evaluate ﬁtness of GP individuals during the training phase. Additionally, in the testing phase, KNN has been used for deducing the classiﬁcation performance of the best individual produced by GP. Several modulation schemes are used in this research for classification purpose. Higher order statistics have been used as input features for the GP. Simulation results demonstrate that the proposed method provides better classiﬁcation performance as compared to other well-known state of art techniques

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Engineering

Language English

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