IEEE Communications Letters · 2019 · 64 citations · 8 references
EngineeringCommunicationElectrical EngineeringDigital CommunicationMachine LearningWireless CommunicationsChannel EstimationChannel Performance AnalysisDigital Signal ProcessingMaximum Likelihood EstimationModulationWireless Channel ModelingChannel Estimation For Fading ChannelsExpectation-maximizationExpectation-maximization AlgorithmExpectation-maximization Channel EstimationFlat Fading ChannelsMaximum Likelihood Channel EstimationMaximum Likelihood EstimatorModulation Classification
In this letter, we use squared iterative method with parameter checking to accelerate the convergence rate of expectation/conditional maximization (ECM) algorithm when estimating the channel parameters blindly in flat fading non-Gaussian channels, and further, we proposed automatic modulation classification (AMC) in flat fading non-Gaussian channels based on the proposed maximum likelihood estimator. The numerical results show that the proposed method can accelerate the convergence rate of ECM algorithm, and AMC based on the proposed method is faster than that based on ECM, while the accuracy of the former shows nearly no loss compared with that of the latter.
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