An SNR Unaware Large Margin Automatic Modulations Classifier in Variable SNR Environments

Automatic classification of modulation type in detected signals is an intermediate step between signal detection and demodulation, and is also an essential task for an intelligent receiver in various civil and military applications. In this paper, a new two-stage partially supervised classification method is proposed for Additive White Gaussian Noise (AWGN) channels with unknown signal to noise ratios, in which a system adaptation to the environment Signal-to-Noise Ratios (SNR) and signals classification are combined. System adaptation to the environment SNR enables us to construct a blind classifier to the SNR. In the classification phase of this algorithm, a passive-aggressive online learning algorithm is applied to identify the modulation type of input signals. Simulation results show that the accuracy of the proposed algorithm approaches to a well-trained system in the target SNR, even in low SNRs.