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.
Highlights
Automatic modulation recognition (AMR) always plays a crucial role in spectral monitoring, surveillance, and spectrum sensing in cognitive radios
We investigate the feature extraction in the framework of compressive sensing (CS), which enables the idea that cyclic cumulants (CCs) can be estimated from a relatively small number of compressive measurements
Since the cyclic frequency (CF) belong to a finite set depending on the signal bandwidth and order n [9], the CCs can be treated as sparse in the Fourier domain under the assumption of no cyclic leakage
Summary
Automatic modulation recognition (AMR) always plays a crucial role in spectral monitoring, surveillance, and spectrum sensing in cognitive radios. To perform a CC-based AMR, a large amount of signal symbols and an extremely higher sampling rate are both required to achieve an acceptable performance, which definitely results in computational complexity and a heavy sampling burden. To address this issue, we investigate the feature extraction in the framework of compressive sensing (CS), which enables the idea that CCs can be estimated from a relatively small number of compressive measurements. Simulation results validate the effectiveness of this novel AMR algorithm
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