Next Article in Journal
Post-Processing Partitions to Identify Domains of Modularity Optimization
Previous Article in Journal
Transformation-Based Fuzzy Rule Interpolation Using Interval Type-2 Fuzzy Sets
Open AccessLetter

Automatic Modulation Recognition Using Compressive Cyclic Features

by * and
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; https://doi.org/10.3390/a10030092
Received: 30 June 2017 / Revised: 10 August 2017 / Accepted: 10 August 2017 / Published: 18 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)
Show Figures

Figure 1

MDPI and ACS Style

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

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop