A Faster Maximum-Likelihood Modulation Classification in Flat Fading Non-Gaussian Channels

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 classi...

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Bibliographic Details
Published in:IEEE communications letters Vol. 23; no. 3; pp. 454 - 457
Main Authors: Chen, Wenhao, Xie, Zhuochen, Ma, Lu, Liu, Jie, Liang, Xuwen
Format: Journal Article
Language:English
Published: New York IEEE 01.03.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Acceleration
Algorithms
automatic modulation classification
Channel estimation
Channels
Classification
Convergence
Economic models
Electronic countermeasures
expectation/conditional maximization algorithm
Fading
Fading channels
Gaussian mixture model
Iterative methods
Mathematical model
Maximum likelihood estimation
Maximum likelihood estimators
Modulation
Parameter estimation
squared iterative method
ISSN:1089-7798, 1558-2558
Online Access:Get full text
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Summary: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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ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2019.2894400