A Faster Maximum-Likelihood Modulation Classification in Flat Fading Non-Gaussian Channels
| Autor: | Wenhao Chen, Zhuochen Xie, Jie Liu, Lu Ma, Xuwen Liang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Iterative method
020206 networking & telecommunications 02 engineering and technology Maximization Computer Science Applications Computer Science::Performance Rate of convergence Electronic countermeasure Modeling and Simulation Modulation (music) Convergence (routing) 0202 electrical engineering electronic engineering information engineering Fading Electrical and Electronic Engineering Algorithm Computer Science::Information Theory Mathematics Communication channel |
| Zdroj: | IEEE Communications Letters. 23:454-457 |
| ISSN: | 2373-7891 1089-7798 |
| DOI: | 10.1109/lcomm.2019.2894400 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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