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Automatic Modulation Recognition Using Compressive Cyclic Features

Department of Electronic Engineering, University of Electronic Science and Technology of China, Qingshuihe Campus, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, Sichuan, China
Author to whom correspondence should be addressed.
Algorithms 2017, 10(3), 92;
Received: 30 June 2017 / Revised: 10 August 2017 / Accepted: 10 August 2017 / Published: 18 August 2017
PDF [326 KB, uploaded 21 August 2017]


Higher-order cyclic cumulants (CCs) have been widely adopted for automatic modulation recognition (AMR) in cognitive radio. However, the CC-based AMR suffers greatly from the requirement of high-rate sampling. To overcome this limit, we resort to the theory of compressive sensing (CS). By exploiting the sparsity of CCs, recognition features can be extracted from a small amount of compressive measurements via a rough CS reconstruction algorithm. Accordingly, a CS-based AMR scheme is formulated. Simulation results demonstrate the availability and robustness of the proposed approach. View Full-Text
Keywords: higher-order cyclic cumulant (CC); compressive sensing (CS); automatic modulation recognition (AMR) higher-order cyclic cumulant (CC); compressive sensing (CS); automatic modulation recognition (AMR)

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Xie, L.; Wan, Q. Automatic Modulation Recognition Using Compressive Cyclic Features. Algorithms 2017, 10, 92.

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