Journal
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS
Volume 16, Issue 1, Pages 400-411Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TWC.2016.2623716
Keywords
Automatic modulation classification; order-statistics machine learning; backpropagation neural networks; linear support vector machine
Funding
- National Basic Research Program of China (973 Program) [2013CB336600]
- National Natural Science Foundation of China [61422109, 61531011, 61631015]
- Natural Sciences and Engineering Research Council of Canada [327285-2013]
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In this paper, we propose three automatic modulation classification classifiers based on order-statistics and reduced order-statistics, where the order-statistics are the random variables sorted by ascending order and the reduced order-statistics represent a subset of the original order-statistics. Specifically, the linear support vector machine classifier applies the linear combination of the order-statistics of the received signals, while the approximate maximum likelihood and the backpropagation neural networks (BPNNs) classifier resort to the reduced order-statistics to decrease the computational complexity. Moreover, BPNN is applicable for modulation classification both in known and unknown channel scenarios. It is shown that in the known channel scenario, the proposed classifiers provide a good tradeoff between performance and computational complexity, while in the unknown channel scenario, the proposed BPNN classifier outperforms the expectation maximization classifier in terms of both classification performance and computational complexity. Simulations results are provided to evaluate the proposed classifiers.
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