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Algorithms 2017, 10(3), 92; doi:10.3390/a10030092

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
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Received: 30 June 2017 / Revised: 10 August 2017 / Accepted: 10 August 2017 / Published: 18 August 2017
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Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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

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