Abstract
Automatic modulation recognition(MR) is important in non-cooperative communications. Aiming to improve the performance of the modulation recognition at low signal to noise ratio(SNR) region, this paper proposes a novel intelligent modulation recognition(IMR) approach based on cyclic spectrum(CS). Since different modulated signals have unique CSs, they can be identified from the features of the CS images using machine learning algorithms, such as Softmax. Note that the training data used in machine learning can be greatly reduced exploiting the CS properties, such as symmetry and signature waveforms occurring only at integer multiples of the symbol rate. Simulation results show that the proposed IMR significantly improves the correct recognition ratio at low SNR, compared to existing method based on CS.
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