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The reaction of detection and classification of signals in low Signal-to-Noise Ratio (SNR) regimes poses significant challenges in the physical layer design of cognitive radio networks. This paper considers a cognitive radio consisting of one Primary User (PU) and K Secondary Users (SUs), where the main objective for an arbitrary SU is to recognize the PU's signal from other secondary users' signals,...
The performance of cognitive radio is sensitive to the accuracy of signal classification. The proposed method can increase the accuracy of existing methods on the certain degree at SNR=0 dB and below. In simulation, we classify five types of signals which are AM, BPSK, FSK, MSK and QPSK. The experiments show that above 99.9% received signals are correctly classified at SNR=-12 dB and above.
This paper deals with automatic modulation classification of communication signals. A new scheme of automatic modulation classification using wavelet analysis and wavelet support vector machine (WSVM) is proposed. Further, a new way of training for wavelet features is carried out to adapt to signals which are non-stable and varied in a wide range of signal-to-noise rates (SNR). Through such training,...
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