Automatic modulation classification (AMC) plays an important role in various applications such as cognitive radio and dynamic spectrum access. Many research works have been exploring deep learning (DL) based AMC, but they primarily focus on single-carrier signals. With the advent of various multicarrier waveforms, the authors propose to revisit DL-based AMC to consider the diversity and complexity of these novel transmission waveforms in this letter. Specifically, the authors develop a novel representation of multicarrier signals and use suitable networks for classification. In addition, to cope with non-target signals, support vector data description (SVDD) is applied with the activations of the networks’ hidden layer. Experimental results demonstrate the effectiveness of the proposed scheme.

In this letter, a joint weighted power detector (JWPD) based on maximum a posterior probability (MAP) criteria is proposed for Willie aiming at two-hop covert communication scenario, which is a near optimal detector. Instead of only supervising one single phase, Willie combines the observations of two phases to make joint decision in the proposed scheme. The proposed scheme achieves lower probability of detection error (PDE) than the existing single-phase-detector (SPD) scheme and adding-power-directly-detector (APDD) scheme due to sufficient utilization of the two-phases observations. Numerical results demonstrate the benefit of our proposed scheme.