Journal
IEEE COMMUNICATIONS LETTERS
Volume 23, Issue 3, Pages 454-457Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LCOMM.2019.2894400
Keywords
Maximum likelihood estimation; expectation/conditional maximization algorithm; squared iterative method; Gaussian mixture model; automatic modulation classification
Categories
Funding
- Natural Science Foundation of China [91738201]
- Shanghai sailing program [17YF1418200]
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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 nonGaussian 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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