Modulation Classification and Parameter Estimation in Wireless Networks
作者： Ruoyu Li
DOI:
关键词: Rayleigh fading 、 Demodulation 、 Computer science 、 Algorithm 、 Expectation–maximization algorithm 、 Additive white Gaussian noise 、 Likelihood-ratio test 、 Artificial intelligence 、 Machine learning 、 Cognitive radio 、 Sensor fusion 、 Estimation theory
摘要: Modulation classification is a crucial step between data recognition and demodulation in Cognitive Radio systems. In this thesis, automatic blind modulation approaches designed for multiple distributed sensor network are discussed. Optimal Bayesian Approach Likelihood Based Detection the main mathematical foundations. First, we build channel signal model under assumption of Additive White Gaussian Noise (AWGN) Rayleigh Fading Channel. For detection scheme, compare performance Chair-Varshney Fusion Rule with Majority coherent communication environment. more general scenario embedded unknown parameters, Hybrid Ratio Test method different types estimations studied. Treating transmitted symbols as hidden variables, propose an Expectation Maximization Algorithm based approach to determine maximum likelihood estimates (MLE) so that can have closed form expressions MLEs. Centralized fusion classifier equipped EM algorithm then evaluated via comparison Method Moments (MoM) estimates. From computer simulation experiments, conclude MLE efficiently obtain better than Moments-Based estimation especially low Signal-to-Noise environments. MODULATION CLASSIFICATION AND PARAMETER ESTIMATION IN WIRELESS NETWORKS
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