Automatic Modulation Recognition Using Compressive Cyclic Features
AbstractHigher-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
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Xie, L.; Wan, Q. Automatic Modulation Recognition Using Compressive Cyclic Features. Algorithms 2017, 10, 92.
Xie L, Wan Q. Automatic Modulation Recognition Using Compressive Cyclic Features. Algorithms. 2017; 10(3):92.Chicago/Turabian Style
Xie, Lijin; Wan, Qun. 2017. "Automatic Modulation Recognition Using Compressive Cyclic Features." Algorithms 10, no. 3: 92.
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