Automatic Modulation Classification of Overlapped Sources Using Multiple Cumulants

Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel est...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2017-07, Vol.66 (7), p.6089-6101
Main Authors: Huang, Sai, Yao, Yuanyuan, Wei, Zhiqing, Feng, Zhiyong, Zhang, Ping
Format: Article
Language:English
Subjects:
Automatic modulation classification (AMC)
Blind equalizers
Channel estimation
Classification
Computer simulation
Decision making
Independent component analysis
Interception
Likelihood ratio
maximum likelihood (ML) classification
Maximum likelihood estimation
Modulation
Monitoring
multiple cumulants
overlapped signal classification
Receiving antennas
Signal monitoring
Signal processing
spectrum monitoring
Wireless communication
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Summary:Automatic modulation classification (AMC) for overlapped sources plays an important role in spectrum monitoring and signal interception. In this paper, we propose a feature-based AMC framework for multiple overlapped sources. The framework first separates the overlapped sources via blind channel estimation and then conducts novel maximum-likelihood-based multicumulant classification (MLMC) for each of the sources. MLMC employs multiple cumulants of arbitrary orders and arbitrary lags as discriminating features and a maximum likelihood ratio test for decision making. Hence, MLMC maximizes the probability of correct classification under the condition that the selected cumulants are utilized. Moreover, both the case with perfect channel estimation and the practically more relevant case with blind channel estimations, called fast independent component analysis and natural gradient independent component analysis, are presented to facilitate the signal separation process. Extensive simulations are also conducted to verify the validity and the superiority of the proposed framework and the MLMC algorithm.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2016.2636324