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- W4765155 abstract "Modulation classification is a crucial step between data recognition and demodulation in Cognitive Radio systems. In this thesis, automatic blind modulation classification approaches designed for multiple modulation classification in distributed sensor network are discussed. Optimal Bayesian Approach and Likelihood Based Detection are the main mathematical foundations. First, we build a channel and signal model under the assumption of Additive White Gaussian Noise (AWGN) and Rayleigh Fading Channel. For the distributed detection scheme, we compare the performance of Chair-Varshney Fusion Rule with Majority Fusion Rule in a coherent communication environment. For a more general scenario with embedded unknown channel parameters, Hybrid Likelihood Ratio Test method with different types of estimations are studied. Treating transmitted symbols as hidden variables, we propose an Expectation Maximization Algorithm based approach to determine maximum likelihood estimates (MLE) so that we can have closed form expressions of MLEs. Centralized data fusion based classifier equipped with EM algorithm based maximum likelihood estimates is then evaluated via comparison to the classifier equipped with Method of Moments (MoM) estimates. From computer simulation experiments, we conclude that EM algorithm based MLE can efficiently obtain better classification performance than Moments-Based estimation especially in low Signal-to-Noise Ratio environments. MODULATION CLASSIFICATION AND PARAMETER ESTIMATION IN WIRELESS NETWORKS" @default.
- W4765155 created "2016-06-24" @default.
- W4765155 creator A5055795314 @default.
- W4765155 date "2012-01-01" @default.
- W4765155 modified "2023-10-03" @default.
- W4765155 title "Modulation Classification and Parameter Estimation in Wireless Networks" @default.
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